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2024-03-29T09:07:06Z
User contributions
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http://help.emd.dk/mediawiki/index.php?title=EMD-API:_EMD-WRF_On-Demand&diff=15325
EMD-API: EMD-WRF On-Demand
2023-05-24T09:33:23Z
<p>Ronnie: /* Data Model - EMD-API: EMD-WRF On-Demand */</p>
<hr />
<div>[[Category:EMD-API]]<br />
== Introduction ==<br />
[[File:REST-api-diagram.png|right|thumb|300px]][[File:EMDAPI_451x303.jpg|thumb|300px|right]]The EMD-API: EMD-WRF On-Demand is a part of EMD-API. It provides a unified interface to the EMD-WRF mesoscale on-demand services (see [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_and_Custom-Area here] and [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_ICING here]). The service is aimed at detailed time-series analysis. EMD-API helps consultants, analysts and scientists working with high-resolution climate data in achieving their goals in an efficient way. EMD-API: EMD-WRF On-Demand has the following key-features: <br />
<br />
* '''Global data delivery''': All models available has a global footprint - so calculations are available at any location.<br />
* '''On-demand calculation''': Modelling is done at the high-performance computing (HPC) facilities at EMD.<br />
* '''Multiple model configurations''': We provide access to multiple model configurations, aimed at single points, larger areas and ice-loss calculations. <br />
* '''Unified interface''': EMD-API is a unified interface which allows for easy integration to internal processes and tools. <br />
* '''Trusted datasets''': EMD-API builds upon the trusted models, data-bases and data-sources that have been used through the [http://help.emd.dk/mediawiki/index.php?title=Main_Page online-data services] in windPRO for more than a decade. <br />
* '''Built on open standards''': EMD-API is a [https://en.wikipedia.org/wiki/Representational_state_transfer REST] based service that implements the [https://swagger.io/specification/ OpenAPI] standard. Results are in the open [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf .mesores] data-format. <br />
* '''Available from any development tool''': Service access is available from your preferred development platform - like C#, R, python, html, java, php, scala and swift (just use the OpenAPI tools to generate the client software for your preferred platform).<br />
<br />
== Access and Requirements ==<br />
To see more documentation and to access the calculation-services, please visit the API-documentation through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Climate Data UI (API) - [https://api.emd.dk/emd-wrf-od/ui/ here].<br />
<br />
Any technical questions on our calculation service can be addressed to our Senior Technical Specialist - Morten Lybech Thøgersen: [mailto:mlt@emd.dk mlt@emd.dk].<br />
<br />
'''Requirements:<br>'''<br />
In order to use the EMD-API: EMD-WRF On-Demand, you will need to:<br />
# Have an API access token for your user and company account. Note: Your access token is personal and will allow to operate the EMD-API: EMD-WRF On-Demand. You can also browse and view credit-status and pending calculations for your company in read-only model.<br />
# Have some calculation credits on your account (a calculation credit corresponds to one month of mesoscale time-series data in the standard 1-point model configuration)<br />
<br />
Calculation results are delivered in the .mesores open-data format for easy integration in windPRO and other tools such as python (pandas) and R. Read more [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf here].<br />
<br />
== Available Models and Cost==<br />
The following mesoscale model configurations are currently available through the EMD-API. The cost per month is 1 calculation credit (CPU=Cost-Per-Unit) for the 1-point calculations, 2 CPU for the 9-point and 3 CPU for the 25 point calculation. The icing configuration is 2-CPU per month of time-series. Since the calculations are run on-demand by the aid of 1000's of computer-cores, the calculation will last a while (several hours to days) to complete. <br />
<br />
<pre><br />
EMD-API AVAILABLE MODELS<br />
------------------------<br />
description model_id start_date end_date supported_areas<br />
------------------------ -------------------------------- -------------------- -------------------- ------------------------------------<br />
ERA5 3km GlobCover emd_36_3km_era5hourly_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
ERA5 3km Icing emd_36_3km_era5hourly_icing_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point']<br />
CFSR/CFSv2 3km GlobCover emd_36_3km_cfsr_glob 1994-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
</pre><br />
<br />
== Data Model - EMD-API: EMD-WRF On-Demand ==<br />
The EMD-API: EMD-WRF On-Demand service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as json or yaml, [https://api.emd.dk/emd-wrf-od/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Models'': Full list of available EMD-WRF On-Demand model configurations including their ID’s and other meta-data.<br />
* ''Information on used and purchased credits'': Request information of completed, cancelled and pending calculations for your company account.<br />
* ''Place Order'': Order a mesoscale time-series data for any location with any model (dataset) - given any latitude-longitude location. You can decide which period to generate.<br />
* ''Order Status'': Request progress for a specific order - and eventually recieve the download URL for the order. Options: [PENDING, CANCELLED OR SUCCESS].<br />
* ''Cancel Order'': Cancel an order that has not started yet (status must be PENDING).<br />
<br />
== Python - Installation and Example Code == <br />
These examples privides a demonstration on how to use the EMD-API: EMD-WRF On-Demand with python installed in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of python. When the virtual environment is created, then activate the environment and install the required packages.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines, one-by-one:''<br />
<pre><br />
conda create -n emdapiclient_emdwrfod python=3.8<br />
conda activate emdapiclient_emdwrfod<br />
conda install -c conda-forge pandas requests tabulate<br />
</pre><br />
<br />
'''Python - Example Code'''<br />
<br />
In order to test your setup and learn to use the EMD-API: EMD-WRF On-Demand, we suggest that you download and run the three examples that we have created - [https://help.emd.dk/mediawiki/images/e/e0/20230524_EMDAPI_EMDWRF-OnDemand.zip here].<br><br />
Unpack the zip files, activate your python environment and run the command below in your integrated-environment, terminal or command-shell.<br><br />
<br />
Then work your way through through each example provided. Each of the three python samples holds a specific focus:<br />
<br />
# ''emdwrfod_example_status.py'': Status and overview of user-request and past-calculations.<br />
# ''emdwrfod_example_order_submit.py'': Submit one or more calculations.<br />
# ''emdwrfod_example_order_check.py'': Check calculation status and retrieve data from one or more calculations.<br />
<br />
== Client Software Generation: Many Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, python, java, php, scala and swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libries yourself - one possible process is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/emd-wrf-od/openapi.yaml here-yaml] or [https://api.emd.dk/emd-wrf-od/openapi.json here-json]<br />
# Load it into the swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API:_EMD-WRF_On-Demand&diff=15324
EMD-API: EMD-WRF On-Demand
2023-05-24T09:33:11Z
<p>Ronnie: /* Data Model - EMD-API: EMD-WRF On-Demand */</p>
<hr />
<div>[[Category:EMD-API]]<br />
== Introduction ==<br />
[[File:REST-api-diagram.png|right|thumb|300px]][[File:EMDAPI_451x303.jpg|thumb|300px|right]]The EMD-API: EMD-WRF On-Demand is a part of EMD-API. It provides a unified interface to the EMD-WRF mesoscale on-demand services (see [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_and_Custom-Area here] and [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_ICING here]). The service is aimed at detailed time-series analysis. EMD-API helps consultants, analysts and scientists working with high-resolution climate data in achieving their goals in an efficient way. EMD-API: EMD-WRF On-Demand has the following key-features: <br />
<br />
* '''Global data delivery''': All models available has a global footprint - so calculations are available at any location.<br />
* '''On-demand calculation''': Modelling is done at the high-performance computing (HPC) facilities at EMD.<br />
* '''Multiple model configurations''': We provide access to multiple model configurations, aimed at single points, larger areas and ice-loss calculations. <br />
* '''Unified interface''': EMD-API is a unified interface which allows for easy integration to internal processes and tools. <br />
* '''Trusted datasets''': EMD-API builds upon the trusted models, data-bases and data-sources that have been used through the [http://help.emd.dk/mediawiki/index.php?title=Main_Page online-data services] in windPRO for more than a decade. <br />
* '''Built on open standards''': EMD-API is a [https://en.wikipedia.org/wiki/Representational_state_transfer REST] based service that implements the [https://swagger.io/specification/ OpenAPI] standard. Results are in the open [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf .mesores] data-format. <br />
* '''Available from any development tool''': Service access is available from your preferred development platform - like C#, R, python, html, java, php, scala and swift (just use the OpenAPI tools to generate the client software for your preferred platform).<br />
<br />
== Access and Requirements ==<br />
To see more documentation and to access the calculation-services, please visit the API-documentation through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Climate Data UI (API) - [https://api.emd.dk/emd-wrf-od/ui/ here].<br />
<br />
Any technical questions on our calculation service can be addressed to our Senior Technical Specialist - Morten Lybech Thøgersen: [mailto:mlt@emd.dk mlt@emd.dk].<br />
<br />
'''Requirements:<br>'''<br />
In order to use the EMD-API: EMD-WRF On-Demand, you will need to:<br />
# Have an API access token for your user and company account. Note: Your access token is personal and will allow to operate the EMD-API: EMD-WRF On-Demand. You can also browse and view credit-status and pending calculations for your company in read-only model.<br />
# Have some calculation credits on your account (a calculation credit corresponds to one month of mesoscale time-series data in the standard 1-point model configuration)<br />
<br />
Calculation results are delivered in the .mesores open-data format for easy integration in windPRO and other tools such as python (pandas) and R. Read more [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf here].<br />
<br />
== Available Models and Cost==<br />
The following mesoscale model configurations are currently available through the EMD-API. The cost per month is 1 calculation credit (CPU=Cost-Per-Unit) for the 1-point calculations, 2 CPU for the 9-point and 3 CPU for the 25 point calculation. The icing configuration is 2-CPU per month of time-series. Since the calculations are run on-demand by the aid of 1000's of computer-cores, the calculation will last a while (several hours to days) to complete. <br />
<br />
<pre><br />
EMD-API AVAILABLE MODELS<br />
------------------------<br />
description model_id start_date end_date supported_areas<br />
------------------------ -------------------------------- -------------------- -------------------- ------------------------------------<br />
ERA5 3km GlobCover emd_36_3km_era5hourly_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
ERA5 3km Icing emd_36_3km_era5hourly_icing_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point']<br />
CFSR/CFSv2 3km GlobCover emd_36_3km_cfsr_glob 1994-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
</pre><br />
<br />
== Data Model - EMD-API: EMD-WRF On-Demand ==<br />
The EMD-API: EMD-WRF On-Demand service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as json or yaml, [https://api.emd.dk/emd-wrf-od/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Models'': Full list of available EMD-WRF On-Demand model configurations including their ID’s and other meta-data<br />
* ''Information on used and purchased credits'': Request information of completed, cancelled and pending calculations for your company account.<br />
* ''Place Order'': Order a mesoscale time-series data for any location with any model (dataset) - given any latitude-longitude location. You can decide which period to generate.<br />
* ''Order Status'': Request progress for a specific order - and eventually recieve the download URL for the order. Options: [PENDING, CANCELLED OR SUCCESS].<br />
* ''Cancel Order'': Cancel an order that has not started yet (status must be PENDING)<br />
<br />
== Python - Installation and Example Code == <br />
These examples privides a demonstration on how to use the EMD-API: EMD-WRF On-Demand with python installed in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of python. When the virtual environment is created, then activate the environment and install the required packages.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines, one-by-one:''<br />
<pre><br />
conda create -n emdapiclient_emdwrfod python=3.8<br />
conda activate emdapiclient_emdwrfod<br />
conda install -c conda-forge pandas requests tabulate<br />
</pre><br />
<br />
'''Python - Example Code'''<br />
<br />
In order to test your setup and learn to use the EMD-API: EMD-WRF On-Demand, we suggest that you download and run the three examples that we have created - [https://help.emd.dk/mediawiki/images/e/e0/20230524_EMDAPI_EMDWRF-OnDemand.zip here].<br><br />
Unpack the zip files, activate your python environment and run the command below in your integrated-environment, terminal or command-shell.<br><br />
<br />
Then work your way through through each example provided. Each of the three python samples holds a specific focus:<br />
<br />
# ''emdwrfod_example_status.py'': Status and overview of user-request and past-calculations.<br />
# ''emdwrfod_example_order_submit.py'': Submit one or more calculations.<br />
# ''emdwrfod_example_order_check.py'': Check calculation status and retrieve data from one or more calculations.<br />
<br />
== Client Software Generation: Many Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, python, java, php, scala and swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libries yourself - one possible process is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/emd-wrf-od/openapi.yaml here-yaml] or [https://api.emd.dk/emd-wrf-od/openapi.json here-json]<br />
# Load it into the swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API:_EMD-WRF_On-Demand&diff=15323
EMD-API: EMD-WRF On-Demand
2023-05-24T09:26:58Z
<p>Ronnie: /* Access and Requirements */</p>
<hr />
<div>[[Category:EMD-API]]<br />
== Introduction ==<br />
[[File:REST-api-diagram.png|right|thumb|300px]][[File:EMDAPI_451x303.jpg|thumb|300px|right]]The EMD-API: EMD-WRF On-Demand is a part of EMD-API. It provides a unified interface to the EMD-WRF mesoscale on-demand services (see [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_and_Custom-Area here] and [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_ICING here]). The service is aimed at detailed time-series analysis. EMD-API helps consultants, analysts and scientists working with high-resolution climate data in achieving their goals in an efficient way. EMD-API: EMD-WRF On-Demand has the following key-features: <br />
<br />
* '''Global data delivery''': All models available has a global footprint - so calculations are available at any location.<br />
* '''On-demand calculation''': Modelling is done at the high-performance computing (HPC) facilities at EMD.<br />
* '''Multiple model configurations''': We provide access to multiple model configurations, aimed at single points, larger areas and ice-loss calculations. <br />
* '''Unified interface''': EMD-API is a unified interface which allows for easy integration to internal processes and tools. <br />
* '''Trusted datasets''': EMD-API builds upon the trusted models, data-bases and data-sources that have been used through the [http://help.emd.dk/mediawiki/index.php?title=Main_Page online-data services] in windPRO for more than a decade. <br />
* '''Built on open standards''': EMD-API is a [https://en.wikipedia.org/wiki/Representational_state_transfer REST] based service that implements the [https://swagger.io/specification/ OpenAPI] standard. Results are in the open [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf .mesores] data-format. <br />
* '''Available from any development tool''': Service access is available from your preferred development platform - like C#, R, python, html, java, php, scala and swift (just use the OpenAPI tools to generate the client software for your preferred platform).<br />
<br />
== Access and Requirements ==<br />
To see more documentation and to access the calculation-services, please visit the API-documentation through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Climate Data UI (API) - [https://api.emd.dk/emd-wrf-od/ui/ here].<br />
<br />
Any technical questions on our calculation service can be addressed to our Senior Technical Specialist - Morten Lybech Thøgersen: [mailto:mlt@emd.dk mlt@emd.dk].<br />
<br />
'''Requirements:<br>'''<br />
In order to use the EMD-API: EMD-WRF On-Demand, you will need to:<br />
# Have an API access token for your user and company account. Note: Your access token is personal and will allow to operate the EMD-API: EMD-WRF On-Demand. You can also browse and view credit-status and pending calculations for your company in read-only model.<br />
# Have some calculation credits on your account (a calculation credit corresponds to one month of mesoscale time-series data in the standard 1-point model configuration)<br />
<br />
Calculation results are delivered in the .mesores open-data format for easy integration in windPRO and other tools such as python (pandas) and R. Read more [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf here].<br />
<br />
== Available Models and Cost==<br />
The following mesoscale model configurations are currently available through the EMD-API. The cost per month is 1 calculation credit (CPU=Cost-Per-Unit) for the 1-point calculations, 2 CPU for the 9-point and 3 CPU for the 25 point calculation. The icing configuration is 2-CPU per month of time-series. Since the calculations are run on-demand by the aid of 1000's of computer-cores, the calculation will last a while (several hours to days) to complete. <br />
<br />
<pre><br />
EMD-API AVAILABLE MODELS<br />
------------------------<br />
description model_id start_date end_date supported_areas<br />
------------------------ -------------------------------- -------------------- -------------------- ------------------------------------<br />
ERA5 3km GlobCover emd_36_3km_era5hourly_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
ERA5 3km Icing emd_36_3km_era5hourly_icing_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point']<br />
CFSR/CFSv2 3km GlobCover emd_36_3km_cfsr_glob 1994-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
</pre><br />
<br />
== Data Model - EMD-API: EMD-WRF On-Demand ==<br />
The EMD-API: EMD-WRF On-Demand service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as json or yaml, [https://api.emd.dk/emd-wrf-od/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Models'': Full list of available EMD-WRF On-Demand model configurations including their ID’s and other meta-data<br />
* ''Information on used and purchased credits'': Request information of completed, cancelled or pending calculations for your company account.<br />
* ''Place Order'': Order a mesoscale time-series data for any location whith any model (dataset) - given any latitude-longitude location). You can decide which period to download.<br />
* ''Order Status'': Request progress for a specific order - and eventually recieve the download URL for the order. Options: [PENDING, CANCELLED OR SUCCESS]., <br />
* ''Cancel Order'': Cancel an order that has not started yet (status must be PENDING)<br />
<br />
== Python - Installation and Example Code == <br />
These examples privides a demonstration on how to use the EMD-API: EMD-WRF On-Demand with python installed in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of python. When the virtual environment is created, then activate the environment and install the required packages.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines, one-by-one:''<br />
<pre><br />
conda create -n emdapiclient_emdwrfod python=3.8<br />
conda activate emdapiclient_emdwrfod<br />
conda install -c conda-forge pandas requests tabulate<br />
</pre><br />
<br />
'''Python - Example Code'''<br />
<br />
In order to test your setup and learn to use the EMD-API: EMD-WRF On-Demand, we suggest that you download and run the three examples that we have created - [https://help.emd.dk/mediawiki/images/e/e0/20230524_EMDAPI_EMDWRF-OnDemand.zip here].<br><br />
Unpack the zip files, activate your python environment and run the command below in your integrated-environment, terminal or command-shell.<br><br />
<br />
Then work your way through through each example provided. Each of the three python samples holds a specific focus:<br />
<br />
# ''emdwrfod_example_status.py'': Status and overview of user-request and past-calculations.<br />
# ''emdwrfod_example_order_submit.py'': Submit one or more calculations.<br />
# ''emdwrfod_example_order_check.py'': Check calculation status and retrieve data from one or more calculations.<br />
<br />
== Client Software Generation: Many Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, python, java, php, scala and swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libries yourself - one possible process is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/emd-wrf-od/openapi.yaml here-yaml] or [https://api.emd.dk/emd-wrf-od/openapi.json here-json]<br />
# Load it into the swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API:_EMD-WRF_On-Demand&diff=15322
EMD-API: EMD-WRF On-Demand
2023-05-24T09:25:12Z
<p>Ronnie: /* Introduction */</p>
<hr />
<div>[[Category:EMD-API]]<br />
== Introduction ==<br />
[[File:REST-api-diagram.png|right|thumb|300px]][[File:EMDAPI_451x303.jpg|thumb|300px|right]]The EMD-API: EMD-WRF On-Demand is a part of EMD-API. It provides a unified interface to the EMD-WRF mesoscale on-demand services (see [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_and_Custom-Area here] and [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_ICING here]). The service is aimed at detailed time-series analysis. EMD-API helps consultants, analysts and scientists working with high-resolution climate data in achieving their goals in an efficient way. EMD-API: EMD-WRF On-Demand has the following key-features: <br />
<br />
* '''Global data delivery''': All models available has a global footprint - so calculations are available at any location.<br />
* '''On-demand calculation''': Modelling is done at the high-performance computing (HPC) facilities at EMD.<br />
* '''Multiple model configurations''': We provide access to multiple model configurations, aimed at single points, larger areas and ice-loss calculations. <br />
* '''Unified interface''': EMD-API is a unified interface which allows for easy integration to internal processes and tools. <br />
* '''Trusted datasets''': EMD-API builds upon the trusted models, data-bases and data-sources that have been used through the [http://help.emd.dk/mediawiki/index.php?title=Main_Page online-data services] in windPRO for more than a decade. <br />
* '''Built on open standards''': EMD-API is a [https://en.wikipedia.org/wiki/Representational_state_transfer REST] based service that implements the [https://swagger.io/specification/ OpenAPI] standard. Results are in the open [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf .mesores] data-format. <br />
* '''Available from any development tool''': Service access is available from your preferred development platform - like C#, R, python, html, java, php, scala and swift (just use the OpenAPI tools to generate the client software for your preferred platform).<br />
<br />
== Access and Requirements ==<br />
To see more documentation and to access the calculation-services, please visit the API-documentation through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Climate Data UI (API) - [https://api.emd.dk/emd-wrf-od/ui/ here].<br />
<br />
Any technical questions on our calculation service can be addressed to our Senior Technical Specialist - Morten Lybech Thøgersen: [mailto:mlt@emd.dk mlt@emd.dk].<br />
<br />
'''Requirements:<br>'''<br />
In order to use the EMD-API: EMD-WRF On-Demand, you will need to:<br />
# Have an API access token for your user and company account. Note: Your access token is personal and will allow to operate the EMD-API: EMD-WRF On-Demand. You can also browse and view credit-status and pending calculations for your company in read-only model.<br />
# Have some calculation credits on your account (a calculation credit corresponds to one month of mesoscale time-series data in the standard 1-point model configuration)<br />
<br />
Calculation results are devlivered in the .mesores open-data format for easy integration in windPRO and other tools such as python (pandas) and R. Read more [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf here].<br />
<br />
== Available Models and Cost==<br />
The following mesoscale model configurations are currently available through the EMD-API. The cost per month is 1 calculation credit (CPU=Cost-Per-Unit) for the 1-point calculations, 2 CPU for the 9-point and 3 CPU for the 25 point calculation. The icing configuration is 2-CPU per month of time-series. Since the calculations are run on-demand by the aid of 1000's of computer-cores, the calculation will last a while (several hours to days) to complete. <br />
<br />
<pre><br />
EMD-API AVAILABLE MODELS<br />
------------------------<br />
description model_id start_date end_date supported_areas<br />
------------------------ -------------------------------- -------------------- -------------------- ------------------------------------<br />
ERA5 3km GlobCover emd_36_3km_era5hourly_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
ERA5 3km Icing emd_36_3km_era5hourly_icing_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point']<br />
CFSR/CFSv2 3km GlobCover emd_36_3km_cfsr_glob 1994-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
</pre><br />
<br />
== Data Model - EMD-API: EMD-WRF On-Demand ==<br />
The EMD-API: EMD-WRF On-Demand service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as json or yaml, [https://api.emd.dk/emd-wrf-od/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Models'': Full list of available EMD-WRF On-Demand model configurations including their ID’s and other meta-data<br />
* ''Information on used and purchased credits'': Request information of completed, cancelled or pending calculations for your company account.<br />
* ''Place Order'': Order a mesoscale time-series data for any location whith any model (dataset) - given any latitude-longitude location). You can decide which period to download.<br />
* ''Order Status'': Request progress for a specific order - and eventually recieve the download URL for the order. Options: [PENDING, CANCELLED OR SUCCESS]., <br />
* ''Cancel Order'': Cancel an order that has not started yet (status must be PENDING)<br />
<br />
== Python - Installation and Example Code == <br />
These examples privides a demonstration on how to use the EMD-API: EMD-WRF On-Demand with python installed in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of python. When the virtual environment is created, then activate the environment and install the required packages.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines, one-by-one:''<br />
<pre><br />
conda create -n emdapiclient_emdwrfod python=3.8<br />
conda activate emdapiclient_emdwrfod<br />
conda install -c conda-forge pandas requests tabulate<br />
</pre><br />
<br />
'''Python - Example Code'''<br />
<br />
In order to test your setup and learn to use the EMD-API: EMD-WRF On-Demand, we suggest that you download and run the three examples that we have created - [https://help.emd.dk/mediawiki/images/e/e0/20230524_EMDAPI_EMDWRF-OnDemand.zip here].<br><br />
Unpack the zip files, activate your python environment and run the command below in your integrated-environment, terminal or command-shell.<br><br />
<br />
Then work your way through through each example provided. Each of the three python samples holds a specific focus:<br />
<br />
# ''emdwrfod_example_status.py'': Status and overview of user-request and past-calculations.<br />
# ''emdwrfod_example_order_submit.py'': Submit one or more calculations.<br />
# ''emdwrfod_example_order_check.py'': Check calculation status and retrieve data from one or more calculations.<br />
<br />
== Client Software Generation: Many Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, python, java, php, scala and swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libries yourself - one possible process is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/emd-wrf-od/openapi.yaml here-yaml] or [https://api.emd.dk/emd-wrf-od/openapi.json here-json]<br />
# Load it into the swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API:_EMD-WRF_On-Demand&diff=15321
EMD-API: EMD-WRF On-Demand
2023-05-24T09:23:35Z
<p>Ronnie: /* Introduction */</p>
<hr />
<div>[[Category:EMD-API]]<br />
== Introduction ==<br />
[[File:REST-api-diagram.png|right|thumb|300px]][[File:EMDAPI_451x303.jpg|thumb|300px|right]]The EMD-API: EMD-WRF On-Demand is a part of EMD-API. It provides a unified interface to the EMD-WRF mesoscale on-demand services (see [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_and_Custom-Area here] and [https://help.emd.dk/mediawiki/index.php/EMD-WRF_On-Demand_ICING here]). The service is aimed at detailed time-series analysis. EMD-API helps consultants, analysts and scientists working with high-resolution climate data in achieving their goals in an efficient way. EMD-API: EMD-WRF On-Demand has the following key-features: <br />
<br />
* '''Global data delivery''': All models available has a global footprint - so calculations are available at any location.<br />
* '''On-demand calculation''': Modelling is done at the high-performance computing (HPC) facilities at EMD.<br />
* '''Multiple model configurations''': We provide access to multiple model configurations, aimed at single points, larger areas and ice-loss calculations. <br />
* '''Unified interface''': EMD-API is a unified interface which allows for easy integration to internal processes and tools. <br />
* '''Trusted datasets''': EMDAPI builds upon the trusted models, data-bases and data-sources that have been used through the [http://help.emd.dk/mediawiki/index.php?title=Main_Page online-data services] in windPRO for more than a decade. <br />
* '''Built on open standards''': EMDAPI is a [https://en.wikipedia.org/wiki/Representational_state_transfer REST] based service that implements the [https://swagger.io/specification/ OpenAPI] standard]. Results are in the open [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf .mesores] data-format. <br />
* '''Available from any development tool''': Service access is available from your preferred development platform - like C#, R, python, html, java, php, scala and swift (just use the OpenAPI tools to generate the client software for your preferred platform).<br />
<br />
== Access and Requirements ==<br />
To see more documentation and to access the calculation-services, please visit the API-documentation through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Climate Data UI (API) - [https://api.emd.dk/emd-wrf-od/ui/ here].<br />
<br />
Any technical questions on our calculation service can be addressed to our Senior Technical Specialist - Morten Lybech Thøgersen: [mailto:mlt@emd.dk mlt@emd.dk].<br />
<br />
'''Requirements:<br>'''<br />
In order to use the EMD-API: EMD-WRF On-Demand, you will need to:<br />
# Have an API access token for your user and company account. Note: Your access token is personal and will allow to operate the EMD-API: EMD-WRF On-Demand. You can also browse and view credit-status and pending calculations for your company in read-only model.<br />
# Have some calculation credits on your account (a calculation credit corresponds to one month of mesoscale time-series data in the standard 1-point model configuration)<br />
<br />
Calculation results are devlivered in the .mesores open-data format for easy integration in windPRO and other tools such as python (pandas) and R. Read more [https://www.emd-international.com/files/flow/EMD_technote_MESORES_20230426.pdf here].<br />
<br />
== Available Models and Cost==<br />
The following mesoscale model configurations are currently available through the EMD-API. The cost per month is 1 calculation credit (CPU=Cost-Per-Unit) for the 1-point calculations, 2 CPU for the 9-point and 3 CPU for the 25 point calculation. The icing configuration is 2-CPU per month of time-series. Since the calculations are run on-demand by the aid of 1000's of computer-cores, the calculation will last a while (several hours to days) to complete. <br />
<br />
<pre><br />
EMD-API AVAILABLE MODELS<br />
------------------------<br />
description model_id start_date end_date supported_areas<br />
------------------------ -------------------------------- -------------------- -------------------- ------------------------------------<br />
ERA5 3km GlobCover emd_36_3km_era5hourly_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
ERA5 3km Icing emd_36_3km_era5hourly_icing_glob 1999-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point']<br />
CFSR/CFSv2 3km GlobCover emd_36_3km_cfsr_glob 1994-01-01T00:00:00Z 2023-05-01T00:00:00Z ['1 point', '9 points', '25 points']<br />
</pre><br />
<br />
== Data Model - EMD-API: EMD-WRF On-Demand ==<br />
The EMD-API: EMD-WRF On-Demand service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as json or yaml, [https://api.emd.dk/emd-wrf-od/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Models'': Full list of available EMD-WRF On-Demand model configurations including their ID’s and other meta-data<br />
* ''Information on used and purchased credits'': Request information of completed, cancelled or pending calculations for your company account.<br />
* ''Place Order'': Order a mesoscale time-series data for any location whith any model (dataset) - given any latitude-longitude location). You can decide which period to download.<br />
* ''Order Status'': Request progress for a specific order - and eventually recieve the download URL for the order. Options: [PENDING, CANCELLED OR SUCCESS]., <br />
* ''Cancel Order'': Cancel an order that has not started yet (status must be PENDING)<br />
<br />
== Python - Installation and Example Code == <br />
These examples privides a demonstration on how to use the EMD-API: EMD-WRF On-Demand with python installed in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of python. When the virtual environment is created, then activate the environment and install the required packages.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines, one-by-one:''<br />
<pre><br />
conda create -n emdapiclient_emdwrfod python=3.8<br />
conda activate emdapiclient_emdwrfod<br />
conda install -c conda-forge pandas requests tabulate<br />
</pre><br />
<br />
'''Python - Example Code'''<br />
<br />
In order to test your setup and learn to use the EMD-API: EMD-WRF On-Demand, we suggest that you download and run the three examples that we have created - [https://help.emd.dk/mediawiki/images/e/e0/20230524_EMDAPI_EMDWRF-OnDemand.zip here].<br><br />
Unpack the zip files, activate your python environment and run the command below in your integrated-environment, terminal or command-shell.<br><br />
<br />
Then work your way through through each example provided. Each of the three python samples holds a specific focus:<br />
<br />
# ''emdwrfod_example_status.py'': Status and overview of user-request and past-calculations.<br />
# ''emdwrfod_example_order_submit.py'': Submit one or more calculations.<br />
# ''emdwrfod_example_order_check.py'': Check calculation status and retrieve data from one or more calculations.<br />
<br />
== Client Software Generation: Many Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, python, java, php, scala and swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libries yourself - one possible process is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/emd-wrf-od/openapi.yaml here-yaml] or [https://api.emd.dk/emd-wrf-od/openapi.json here-json]<br />
# Load it into the swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Main_Page&diff=14745
Main Page
2022-12-13T06:33:19Z
<p>Ronnie: </p>
<hr />
<div>[[File:Windpro_v2_rgb.png|right|150px]] <br />
This windPRO-Wiki currently only contains a description of all the online-datasets that are available directly from within windPRO. This wiki seeks to describe the remote sensing data and other data available for download or which can be accessed from windPRO. Since more online-datasets become available in-between windPRO releases, then [http://www.emd.dk EMD] has decided to release the online dataset documentation in a dynamic wiki-format. This enables a more dynamic (and frequent) update of the associated datasets and their documentation. This page describes the datasets available within windPRO 3.5. <br />
We welcome suggestions for new datasets to integrate with windPRO: Please submit any proposals at [https://tinyurl.com/new-windpro-dataset this feedback form]. <br />
<br />
== [[:Category:Online Data|WindPRO Documentation on Online Data]] ==<br />
In the table below, you can find the complete list of online services and datasets available from within WindPRO. Please consult the individual dataset-descriptions for information on update frequencies, coverage and resolutions. <br />
{| cellpadding="5"<br />
! width=375px |<br />
! width=375px |<br />
! width=375px |<br />
|- valign="top"<br />
|'''[[:Category:Digital Elevation Models|Global & Regional Digital Elevation Models (DEM)]]'''<br><hr><br />
* [[Global_AW3D30|ALOS World 3D 30m mesh (AW3D30)]]<br />
* [[Copernicus_DEM|Copernicus DEM]]<br />
* [[EU-DEM|European Elevation Model (EU-DEM)]]<br />
* [[NASA-DEM|NASADEM (successor of SRTM)]]<br />
* [[Shuttle_Radar_Topography_Mission|Shuttle Radar Topography Mission (SRTM)]]<br />
* [[Viewfinder_Panoramas|Viewfinder Panoramas DEM]]<br><br><br />
'''[[:Category:Digital Elevation Models|National Digital Elevation Models (DEM)]]'''<br><hr><br />
* [[Austrian_Elevation_Model|Austrian Elevation Model (DGM)]]<br />
* [[Australian_Elevation_Models|Australian Elevation Models]]<br />
* [[Belgium-Flemish_Elevation_Models|Belgium Flemish Elevation Model (DTM)]]<br />
* [[Belgium-Walloon_Elevation_Models|Belgium Walloon Elevation Models (MNT)]]<br />
* [[Danish_Elevation_Model|Danish Elevation Model (Danmarks Højdemodel)]]<br />
* [[Estonian_Elevation_Models|Estonian Elevation Models]]<br />
* [[Finnish Elevation Model|Finnish Elevation Model]]<br />
* [[French_Elevation_Models|French Elevation Models]]<br />
* [[German_DGM_datasets|German Elevation Models (DGM)]]<br />
* [[Iceland_Elevation_Model|Iceland LMI Elevation Model 2016]]<br />
* [[Italian_Elevation_Model_-_TINITALY|Italian Nationwide Model (TINITALY)]]<br />
* [[Italy-Sardinia_Elevation_Model|Italian-Sardinia Elevation Model]]<br />
* [[Italy-Tuscany_Elevation_Model|Italian-Tuscany Elevation Model]]<br />
* [[Latvian_Elevation_Model|Latvian Elevation Model]]<br />
* [[Luxembourg_Elevation_Model|Luxembourg Elevation Model (BD-L-MNT5)]]<br />
* [[Netherlands_Elevation_Models|Netherlands Elevation Models (AHN2/AHN3)]]<br />
* [[Norwegian_Elevation_Models|Norwegian Digital Elevation Models (DTM/DOM)]]<br />
* [[Slovenia_Elevation_Model|Slovenia Elevation Model]]<br />
* [[Spanish_Elevation_Models|Spanish Elevation Models (MTD)]]<br />
* [[Swedish_Elevation_Model|Swedish Elevation Model (GSD)]]<br />
* [[Switzerland_Elevation_Model|Switzerland Elevation Model (DGM)]]<br />
* [[Taiwan_Elevation_Model|Taiwan Elevation Model]]<br />
* [[United_Kingdom_Elevation_Datasets|United Kingdom Elevation Datasets]]<br />
* [[National_Elevation_Dataset|US National Elevation Dataset (NED)]]<br><br><br />
'''[[:Category:Digital_Roughness_Data|Digital Roughness Models (DRM)]]'''<br><hr><br />
* [[Copernicus_Global_Land_Service_-_Land_Cover_100m|Copernicus Global Land Service, Land Cover 100]]<br />
* [[Corine_Land_Cover|Corine Land Cover (2006, 2012 and 2018)]]<br />
* [[Data_For_Wind|European Data For Wind]]<br />
* [[Global_Land_Cover_Characteristics|Global Land Cover Characteristics (GLCC)]]<br />
* [[Glob_Cover|GlobCover]]<br />
* [[MODIS_VCF|MODIS VCF]]<br />
* [[National_Land_Cover_Database_2011|US National Land Cover Database 2011]]<br><br><br />
'''[[:Category:Solar Data|Solar Irradiance Data]]'''<br><hr><br />
* [[Heliosat_(SARAH)|Heliosat (SARAH)]] <br />
* [[Heliosat_(SARAH)_East|Heliosat (SARAH) East]]<br><br><br />
|'''[[:Category:Atlas_Data|Atlas Datasets]]'''<br><hr><br />
* [[GASP_Global|Global Atlas of Siting Parameters (GASP)]]<br />
* [[RASP_Sweden|Regional Atlas of Siting Parameters - Sweden]]<br><br><br />
'''[[:Category:Wind Data|Wind Data]]'''<br><hr><br />
* [[Blended_Coastal_Winds|Blended Coastal Winds]]<br />
* [[CFS-_and_CFSR_Data|CFS- and CFSR Data]]<br />
* [[Danish Windindex Data]]<br />
* [[ERA-Interim|EMD-Global Wind Data (based on ERA-Interim)]]<br />
* [[ERA5_Gaussian_Grid|ERA5 Gaussian Grid]]<br />
* [[ERA5(T)_Rectangular_Grid|ERA5(T) Rectangular Grid]]<br />
* [[MERRA_Data|MERRA Data]]<br />
* [[MERRA2_Data|MERRA-2 Data]]<br />
* [[METAR_Data|METAR Data]]<br />
* [[NCEP_/_NCAR_Global_Reanalysis_Data|NCEP/NCAR Global Reanalysis Data]]<br />
* [[NCEP_North_American_Regional_Reanalysis_Data|North American Regional Reanalysis Data]]<br />
* [[QuikScat|QuikScat Offshore Wind Dataset]]<br />
* [[SYNOP_Data|SYNOP Data]]<br><br><br />
'''[[:Category:Wind Data|EMD-WRF Mesoscale Wind Data]]'''<br><hr><br />
* [[EMD-WRF_Europe%2B|Europe]]: EMD-WRF Europe+ (ERA5)<br />
* [[EMD-ConWx_Meso_Data_Europe|Europe]]: EMD-ConWx Europe (ERA-Interim)<br />
* [[EMD-WRF_On-Demand_and_Custom-Area|Custom Area]]: EMD-WRF Custom-Area<br />
* [[EMD-WRF Middle East|Middle East]]: EMD-WRF Middle East Meso Data<br />
* [[EMD-WRF_South_Korea_(ERA5)|South Korea]]: EMD-WRF South Korea (ERA5)<br />
* [[EMD-WRF South Korea|South Korea]]: EMD-WRF South Korea, ERA-Interim<br />
* [[EMD-WRF South Africa|South Africa]]: EMD-WRF South Africa Meso Data<br />
* [[EMD-WRF India|India]]: EMD-WRF India Meso Data<br />
* [[EMD-WRF Indonesia|Indonesia]]: EMD-WRF Indonesia Meso Data<br />
* [[EMD-WRF_On-Demand_and_Custom-Area|On Demand]]: EMD-WRF Global Meso On-Demand<br />
* [[EMD-WRF_On-Demand_ICING|On Demand ICING]]: EMD-WRF OD ICING<br><br><br />
'''[[:Category:Wind Data|3rd Party Mesoscale Wind Data]]'''<br><hr><br />
* [[KNMI_North_Sea_Wind_(KNMI-KNW)|KNMI-KNW North Sea Wind]]<br />
* [[New_European_Wind_Atlas_(NEWA)|NEWA: New European Wind Atlas]]<br />
* [[NORA3|NORA3: Norwegian Reanalysis]]<br><br><br />
'''[[:Category:Existing_Turbines|Databases on Turbines]]'''<br><hr><br />
* [[Danish_Wind_Turbines|Danish Turbines (makes, positions, productions)]]<br />
* [[Finnish_Wind_Turbines|Finnish Turbines (positions)]]<br />
* [[DE_Wind_Turbines_MaStR|German MaStR Turbines (types, positions)]]<br />
* [[OSM_Turbines|Open Streep Map Turbines (positions only)]]<br />
* [[US_Wind_Turbines|Turbines in the United States]]<br />
* [[WTG_Catalogue|WindPRO Wind Turbine Catalogue]]<br />
|<br />
'''[[:Category:Maps|Digital Map Data - Dynamic Maps]]'''<br><hr><br />
* [[Dynamic Maps - Server status|Dynamic Maps - Server status]]<br />
<br><br />
'''[[:Category:Maps|Digital Map Data - Orthophotos]]'''<br><hr><br />
* [[Danish_Orthophoto_Mosaic|Danish Orthophoto Mosaic]]<br />
* [[French_Orthophoto_Mosaic|French Orthophoto Mosaic]]<br />
* [[Finnish_Orthophoto_Mosaic|Finnish Orthophoto Mosaic]]<br />
* [[Latvian_Orthophoto_Mosaic|Latvian Orthophoto Mosaic]]<br />
* [[Spanish_Orthophoto_Mosaic|Spanish Orthophoto Mosaic]]<br />
* [[GeoCover_Images|GeoCover Images]]<br />
* [[WindPRO_Global_Satellite_Imagery|windPRO Global Satellite Imagery - 10m,<br>2018 & 2022]]<br />
* [[WindPRO_European_Satellite_Imagery|windPRO European Satellite Imagery - 2.5m]]<br><br><br />
'''[[:Category:Maps|Digital Map Data - Topographic Maps]]'''<br><hr><br />
* [[UK-Great_Britain%3A_OS_Open_Maps|British Ordnance Survey OpenData]]<br />
* [[Estonian_Topographic_Map|Estonian Topographic Map]]<br />
* [[German Topographic Maps]]<br />
* [[French_Raster_Map|French Raster Map]]<br />
* [[Finnish_Topographic_Map|Finnish Topographic Map]]<br />
* [[Latvian_Topographic_Map|Latvian Topographic Map]]<br />
* [[Norwegian_Topographic_Map|Norwegian Topographic Map]]<br />
* [[Spanish_Topographic_Map|Spanish Topographic Map]]<br />
* [[Swedish_Topographic_Map|Swedish Topographic Map]]<br />
* [[Open_Street_Map|Open Street Map]]<br />
* [[OnMaps|OnMaps]]<br><br><br />
'''[[:Category:Forest_Maps|Forest Maps (Canopy/Tree Heights)]]'''<br><hr><br />
* [[Danish_KU_Forest_Heights|Danish KU Forest Map]]<br />
* [[Estonian_Canopy_Heights|Estonian Forest Map]]<br />
* [[Finnish_LUKE_Forest_Map|Finnish LUKE Forest Map]]<br />
* [[Global_Sentinel-2_10m_Canopy_Height_ETH|Global Sentinel-2 10m Canopy Height (ETH Zurich)]]<br />
* [[Latvian_Canopy_Heights|Latvian Canopy Heights]]<br />
* [[Near-Global_Forest_Canopy_Heights_GLAD|Near-Global Forest Canopy Heights GLAD]]<br />
* [[Norwegian_SR16_Forest_Heights|Norwegian SR16 Forest Map]]<br />
* [[Swedish_SLU_Forest_Map|Swedish SLU Forest Map]]<br />
<br />
<br><br />
'''[[:Category:Bathymetry_Models|Digital Bathymetry Models (DBM), Water Depths]]'''<br><hr><br />
* [[EMODnet_Bathymetry|European Bathymetry - EMODnet,<br>2020, 2018 & 2016]]<br />
* [[Global_Bathymetry_GEBCO|Global Bathymetry - GEBCO, 2021, 2019 & 2014]]<br />
<br><br />
<span style="color:#FF4D00">'''Other Maps, Tools and Data Sources'''</span><br><hr><br />
* [[Commercial_DEM_Providers|3rd Party Commercial DEM Providers]]<br />
* [[Aster_Global_Digital_Elevation_Model|Aster Global Elevation Model (Aster GDEM)]]<br />
* [[CGIAR_SRTM_90m_Digital_Elevation_Data|CGIAR 90m Digital Elevation Data.]]<br />
* [[WindSight_-_Premium_Data_Layers_by_DHI_GRAS|WindSight - Premium Data Layers by DHI GRAS]]<br />
* [[Dynamic_maps|Dynamic maps]]<br />
* [[Google_Earth_Export|Export of WindPRO data into Google Earth]]<br />
* [[ÜSTÜN_windPROSPER_maps|ÜSTÜN windPROSPER maps]]<br />
* [[Web_Map_Service|Web Map Service (WMS)]]<br />
* [[MERIT_DEM|MERIT-DEM - Digital Elevation Model - 90m]]<br />
|}</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Dynamic_Maps_-_Server_status&diff=14540
Dynamic Maps - Server status
2022-08-10T05:52:21Z
<p>Ronnie: </p>
<hr />
<div>== Introduction ==<br />
This page holds a list of any current issues concerning the operational status of the Dynamic Maps provided by EMD<br><br />
If you discover an issue with the Dynamic Maps, please contact the EMD hotline at [mailto:support@emd.dk support@emd.dk].<br />
<br />
== Operational Status ==<br />
2022-07-27 - 2022-07-28: Our servers were very slow<br></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Dynamic_Maps_-_Server_status&diff=14539
Dynamic Maps - Server status
2022-08-10T05:52:05Z
<p>Ronnie: Created page with "== Introduction == This page holds a list of any current issues concerning the operational status of the Dynamic Maps provided by EMD<br> If you discover an issue with the Dyn..."</p>
<hr />
<div>== Introduction ==<br />
This page holds a list of any current issues concerning the operational status of the Dynamic Maps provided by EMD<br><br />
If you discover an issue with the Dynamic Maps, please contact the EMD hotline at [mailto:support@emd.dk support@emd.dk].<br />
<br />
== Operational Status ==<br />
2022-07-27 - 2022-07-27: Our servers were very slow<br></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Main_Page&diff=14538
Main Page
2022-08-10T05:46:51Z
<p>Ronnie: </p>
<hr />
<div>[[File:Windpro_v2_rgb.png|right|150px]] <br />
This windPRO-Wiki currently contains a description of all the online-datasets that are available directly from within windPRO. This wiki seeks to describe the remote sensing data and other data available for download or which can be accessed from windPRO. Since more online-datasets become available in-between windPRO releases, then [http://www.emd.dk EMD] has decided to release the online dataset documentation in a dynamic wiki-format. This enables a more dynamic (and frequent) update of the associated datasets and their documentation. This page describes the datasets available within windPRO 3.5. <br />
We welcome suggestions for new datasets to integrate with windPRO: Please submit any proposals at [https://tinyurl.com/new-windpro-dataset this feedback form]. <br />
<br />
== [[:Category:Online Data|WindPRO Documentation on Online Data]] ==<br />
In the table below, you can find the complete list of online services and datasets available from within WindPRO. Please consult the individual dataset-descriptions for information on update frequencies, coverage and resolutions. <br />
{| cellpadding="5"<br />
! width=375px |<br />
! width=375px |<br />
! width=375px |<br />
|- valign="top"<br />
|'''[[:Category:Digital Elevation Models|Global & Regional Digital Elevation Models (DEM)]]'''<br><hr><br />
* [[Global_AW3D30|ALOS World 3D 30m mesh (AW3D30)]]<br />
* [[Copernicus_DEM|Copernicus DEM]]<br />
* [[EU-DEM|European Elevation Model (EU-DEM)]]<br />
* [[NASA-DEM|NASADEM (successor of SRTM)]]<br />
* [[Shuttle_Radar_Topography_Mission|Shuttle Radar Topography Mission (SRTM)]]<br />
* [[Viewfinder_Panoramas|Viewfinder Panoramas DEM]]<br><br><br />
'''[[:Category:Digital Elevation Models|National Digital Elevation Models (DEM)]]'''<br><hr><br />
* [[Austrian_Elevation_Model|Austrian Elevation Model (DGM)]]<br />
* [[Australian_Elevation_Models|Australian Elevation Models]]<br />
* [[Belgium-Flemish_Elevation_Models|Belgium Flemish Elevation Model (DTM)]]<br />
* [[Belgium-Walloon_Elevation_Models|Belgium Walloon Elevation Models (MNT)]]<br />
* [[Danish_Elevation_Model|Danish Elevation Model (Danmarks Højdemodel)]]<br />
* [[Estonian_Elevation_Models|Estonian Elevation Models]]<br />
* [[Finnish Elevation Model|Finnish Elevation Model]]<br />
* [[French_Elevation_Models|French Elevation Models]]<br />
* [[German_DGM_datasets|German Elevation Models (DGM)]]<br />
* [[Iceland_Elevation_Model|Iceland LMI Elevation Model 2016]]<br />
* [[Italian_Elevation_Model_-_TINITALY|Italian Nationwide Model (TINITALY)]]<br />
* [[Italy-Sardinia_Elevation_Model|Italian-Sardinia Elevation Model]]<br />
* [[Italy-Tuscany_Elevation_Model|Italian-Tuscany Elevation Model]]<br />
* [[Latvian_Elevation_Model|Latvian Elevation Model]]<br />
* [[Luxembourg_Elevation_Model|Luxembourg Elevation Model (BD-L-MNT5)]]<br />
* [[Netherlands_Elevation_Models|Netherlands Elevation Models (AHN2/AHN3)]]<br />
* [[Norwegian_Elevation_Models|Norwegian Digital Elevation Models (DTM/DOM)]]<br />
* [[Slovenia_Elevation_Model|Slovenia Elevation Model]]<br />
* [[Spanish_Elevation_Models|Spanish Elevation Models (MTD)]]<br />
* [[Swedish_Elevation_Model|Swedish Elevation Model (GSD)]]<br />
* [[Switzerland_Elevation_Model|Switzerland Elevation Model (DGM)]]<br />
* [[Taiwan_Elevation_Model|Taiwan Elevation Model]]<br />
* [[United_Kingdom_Elevation_Datasets|United Kingdom Elevation Datasets]]<br />
* [[National_Elevation_Dataset|US National Elevation Dataset (NED)]]<br><br><br />
'''[[:Category:Digital_Roughness_Data|Digital Roughness Models (DRM)]]'''<br><hr><br />
* [[Copernicus_Global_Land_Service_-_Land_Cover_100m|Copernicus Global Land Service, Land Cover 100]]<br />
* [[Corine_Land_Cover|Corine Land Cover (2006, 2012 and 2018)]]<br />
* [[Data_For_Wind|European Data For Wind]]<br />
* [[Global_Land_Cover_Characteristics|Global Land Cover Characteristics (GLCC)]]<br />
* [[Glob_Cover|GlobCover]]<br />
* [[MODIS_VCF|MODIS VCF]]<br />
* [[National_Land_Cover_Database_2011|US National Land Cover Database 2011]]<br><br><br />
'''[[:Category:Solar Data|Solar Irradiance Data]]'''<br><hr><br />
* [[Heliosat_(SARAH)|Heliosat (SARAH)]] <br />
* [[Heliosat_(SARAH)_East|Heliosat (SARAH) East]]<br><br><br />
|'''[[:Category:Atlas_Data|Atlas Datasets]]'''<br><hr><br />
* [[GASP_Global|Global Atlas of Siting Parameters (GASP)]]<br />
* [[RASP_Sweden|Regional Atlas of Siting Parameters - Sweden]]<br><br><br />
'''[[:Category:Wind Data|Wind Data]]'''<br><hr><br />
* [[Blended_Coastal_Winds|Blended Coastal Winds]]<br />
* [[CFS-_and_CFSR_Data|CFS- and CFSR Data]]<br />
* [[Danish Windindex Data]]<br />
* [[ERA-Interim|EMD-Global Wind Data (based on ERA-Interim)]]<br />
* [[ERA5_Gaussian_Grid|ERA5 Gaussian Grid]]<br />
* [[ERA5(T)_Rectangular_Grid|ERA5(T) Rectangular Grid]]<br />
* [[MERRA_Data|MERRA Data]]<br />
* [[MERRA2_Data|MERRA-2 Data]]<br />
* [[METAR_Data|METAR Data]]<br />
* [[NCEP_/_NCAR_Global_Reanalysis_Data|NCEP/NCAR Global Reanalysis Data]]<br />
* [[NCEP_North_American_Regional_Reanalysis_Data|North American Regional Reanalysis Data]]<br />
* [[QuikScat|QuikScat Offshore Wind Dataset]]<br />
* [[SYNOP_Data|SYNOP Data]]<br><br><br />
'''[[:Category:Wind Data|EMD-WRF Mesoscale Wind Data]]'''<br><hr><br />
* [[EMD-WRF_Europe%2B|Europe]]: EMD-WRF Europe+ (ERA5)<br />
* [[EMD-ConWx_Meso_Data_Europe|Europe]]: EMD-ConWx Europe (ERA-Interim)<br />
* [[EMD-WRF_On-Demand_and_Custom-Area|Custom Area]]: EMD-WRF Custom-Area<br />
* [[EMD-WRF Middle East|Middle East]]: EMD-WRF Middle East Meso Data<br />
* [[EMD-WRF_South_Korea_(ERA5)|South Korea]]: EMD-WRF South Korea (ERA5)<br />
* [[EMD-WRF South Korea|South Korea]]: EMD-WRF South Korea, ERA-Interim<br />
* [[EMD-WRF South Africa|South Africa]]: EMD-WRF South Africa Meso Data<br />
* [[EMD-WRF India|India]]: EMD-WRF India Meso Data<br />
* [[EMD-WRF Indonesia|Indonesia]]: EMD-WRF Indonesia Meso Data<br />
* [[EMD-WRF_On-Demand_and_Custom-Area|On Demand]]: EMD-WRF Global Meso On-Demand<br />
* [[EMD-WRF_On-Demand_ICING|On Demand ICING]]: EMD-WRF OD ICING<br><br><br />
'''[[:Category:Wind Data|3rd Party Mesoscale Wind Data]]'''<br><hr><br />
* [[KNMI_North_Sea_Wind_(KNMI-KNW)|KNMI-KNW North Sea Wind]]<br />
* [[New_European_Wind_Atlas_(NEWA)|NEWA: New European Wind Atlas]]<br />
* [[NORA3|NORA3: Norwegian Reanalysis]]<br><br><br />
'''[[:Category:Existing_Turbines|Databases on Turbines]]'''<br><hr><br />
* [[Danish_Wind_Turbines|Danish Turbines (makes, positions, productions)]]<br />
* [[Finnish_Wind_Turbines|Finnish Turbines (positions)]]<br />
* [[DE_Wind_Turbines_MaStR|German MaStR Turbines (types, positions)]]<br />
* [[OSM_Turbines|Open Streep Map Turbines (positions only)]]<br />
* [[US_Wind_Turbines|Turbines in the United States]]<br />
* [[WTG_Catalogue|WindPRO Wind Turbine Catalogue]]<br />
|<br />
'''[[:Category:Maps|Digital Map Data - Dynamic Maps]]'''<br><hr><br />
* [[Dynamic Maps - Server status|Dynamic Maps - Server status]]<br />
<br><br />
'''[[:Category:Maps|Digital Map Data - Orthophotos]]'''<br><hr><br />
* [[Danish_Orthophoto_Mosaic|Danish Orthophoto Mosaic]]<br />
* [[French_Orthophoto_Mosaic|French Orthophoto Mosaic]]<br />
* [[Finnish_Orthophoto_Mosaic|Finnish Orthophoto Mosaic]]<br />
* [[Latvian_Orthophoto_Mosaic|Latvian Orthophoto Mosaic]]<br />
* [[Spanish_Orthophoto_Mosaic|Spanish Orthophoto Mosaic]]<br />
* [[GeoCover_Images|GeoCover Images]]<br />
* [[WindPRO_Global_Satellite_Imagery|windPRO Global Satellite Imagery - 10m,<br>2018 & 2022]]<br />
* [[WindPRO_European_Satellite_Imagery|windPRO European Satellite Imagery - 2.5m]]<br><br><br />
'''[[:Category:Maps|Digital Map Data - Topographic Maps]]'''<br><hr><br />
* [[UK-Great_Britain%3A_OS_Open_Maps|British Ordnance Survey OpenData]]<br />
* [[Estonian_Topographic_Map|Estonian Topographic Map]]<br />
* [[German Topographic Maps]]<br />
* [[French_Raster_Map|French Raster Map]]<br />
* [[Finnish_Topographic_Map|Finnish Topographic Map]]<br />
* [[Latvian_Topographic_Map|Latvian Topographic Map]]<br />
* [[Norwegian_Topographic_Map|Norwegian Topographic Map]]<br />
* [[Spanish_Topographic_Map|Spanish Topographic Map]]<br />
* [[Swedish_Topographic_Map|Swedish Topographic Map]]<br />
* [[Open_Street_Map|Open Street Map]]<br />
* [[OnMaps|OnMaps]]<br><br><br />
'''[[:Category:Forest_Maps|Forest Maps (Canopy/Tree Heights)]]'''<br><hr><br />
* [[Danish_KU_Forest_Heights|Danish KU Forest Map]]<br />
* [[Estonian_Canopy_Heights|Estonian Forest Map]]<br />
* [[Finnish_LUKE_Forest_Map|Finnish LUKE Forest Map]]<br />
* [[Global_Sentinel-2_10m_Canopy_Height_ETH|Global Sentinel-2 10m Canopy Height (ETH Zurich)]]<br />
* [[Latvian_Canopy_Heights|Latvian Canopy Heights]]<br />
* [[Near-Global_Forest_Canopy_Heights_GLAD|Near-Global Forest Canopy Heights GLAD]]<br />
* [[Norwegian_SR16_Forest_Heights|Norwegian SR16 Forest Map]]<br />
* [[Swedish_SLU_Forest_Map|Swedish SLU Forest Map]]<br />
<br />
<br><br />
'''[[:Category:Bathymetry_Models|Digital Bathymetry Models (DBM), Water Depths]]'''<br><hr><br />
* [[EMODnet_Bathymetry|European Bathymetry - EMODnet,<br>2020, 2018 & 2016]]<br />
* [[Global_Bathymetry_GEBCO|Global Bathymetry - GEBCO, 2021, 2019 & 2014]]<br />
<br><br />
<span style="color:#FF4D00">'''Other Maps, Tools and Data Sources'''</span><br><hr><br />
* [[Commercial_DEM_Providers|3rd Party Commercial DEM Providers]]<br />
* [[Aster_Global_Digital_Elevation_Model|Aster Global Elevation Model (Aster GDEM)]]<br />
* [[CGIAR_SRTM_90m_Digital_Elevation_Data|CGIAR 90m Digital Elevation Data.]]<br />
* [[WindSight_-_Premium_Data_Layers_by_DHI_GRAS|WindSight - Premium Data Layers by DHI GRAS]]<br />
* [[Dynamic_maps|Dynamic maps]]<br />
* [[Google_Earth_Export|Export of WindPRO data into Google Earth]]<br />
* [[ÜSTÜN_windPROSPER_maps|ÜSTÜN windPROSPER maps]]<br />
* [[Web_Map_Service|Web Map Service (WMS)]]<br />
* [[MERIT_DEM|MERIT-DEM - Digital Elevation Model - 90m]]<br />
|}</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Main_Page&diff=14378
Main Page
2022-05-24T11:02:46Z
<p>Ronnie: </p>
<hr />
<div>[[File:Windpro_v2_rgb.png|right|150px]] <br />
This windPRO-Wiki currently contains a description of all the online-datasets that are available directly from within windPRO. This wiki seeks to describe the remote sensing data and other data available for download or which can be accessed from windPRO. Since more online-datasets become available in-between windPRO releases, then [http://www.emd.dk EMD] has decided to release the online dataset documentation in a dynamic wiki-format. This enables a more dynamic (and frequent) update of the associated datasets and their documentation. This page describes the datasets available within windPRO 3.5. <br />
We welcome suggestions for new datasets to integrate with windPRO: Please submit any proposals at [https://tinyurl.com/new-windpro-dataset this feedback form]. <br />
<br />
== [[:Category:Online Data|WindPRO Documentation on Online Data]] ==<br />
In the table below, you can find the complete list of online services and datasets available from within WindPRO. Please consult the individual dataset-descriptions for information on update frequencies, coverage and resolutions. <br />
{| cellpadding="5"<br />
! width=350px |<br />
! width=350px |<br />
! width=350px |<br />
|- valign="top"<br />
|'''[[:Category:Digital Elevation Models|Global & Regional Digital Elevation Models (DEM)]]'''<br><hr><br />
* [[Global_AW3D30|ALOS World 3D 30m mesh (AW3D30)]]<br />
* [[Copernicus_DEM|Copernicus DEM]]<br />
* [[EU-DEM|European Elevation Model (EU-DEM)]]<br />
* [[NASA-DEM|NASADEM (successor of SRTM)]]<br />
* [[Shuttle_Radar_Topography_Mission|Shuttle Radar Topography Mission (SRTM)]]<br />
* [[Viewfinder_Panoramas|Viewfinder Panoramas DEM]]<br><br><br />
'''[[:Category:Digital Elevation Models|National Digital Elevation Models (DEM)]]'''<br><hr><br />
* [[Austrian_Elevation_Model|Austrian Elevation Model (DGM)]]<br />
* [[Australian_Elevation_Models|Australian Elevation Models]]<br />
* [[Belgium-Flemish_Elevation_Models|Belgium Flemish Elevation Model (DTM)]]<br />
* [[Belgium-Walloon_Elevation_Models|Belgium Walloon Elevation Models (MNT)]]<br />
* [[Danish_Elevation_Model|Danish Elevation Model (Danmarks Højdemodel)]]<br />
* [[Estonian_Elevation_Models|Estonian Elevation Models]]<br />
* [[Finnish Elevation Model|Finnish Elevation Model]]<br />
* [[French_Elevation_Models|French Elevation Models]]<br />
* [[German_DGM_datasets|German Elevation Models (DGM)]]<br />
* [[Iceland_Elevation_Model|Iceland LMI Elevation Model 2016]]<br />
* [[Italian_Elevation_Model_-_TINITALY|Italian Nationwide Model (TINITALY)]]<br />
* [[Italy-Sardinia_Elevation_Model|Italian-Sardinia Elevation Model]]<br />
* [[Italy-Tuscany_Elevation_Model|Italian-Tuscany Elevation Model]]<br />
* [[Latvian_Elevation_Model|Latvian Elevation Model]]<br />
* [[Luxembourg_Elevation_Model|Luxembourg Elevation Model (BD-L-MNT5)]]<br />
* [[Netherlands_Elevation_Models|Netherlands Elevation Models (AHN2/AHN3)]]<br />
* [[Norwegian_Elevation_Models|Norwegian Digital Elevation Models (DTM/DOM)]]<br />
* [[Slovenia_Elevation_Model|Slovenia Elevation Model]]<br />
* [[Spanish_Elevation_Models|Spanish Elevation Models (MTD)]]<br />
* [[Swedish_Elevation_Model|Swedish Elevation Model (GSD)]]<br />
* [[Switzerland_Elevation_Model|Switzerland Elevation Model (DGM)]]<br />
* [[United_Kingdom_Elevation_Datasets|United Kingdom Elevation Datasets]]<br />
* [[National_Elevation_Dataset|US National Elevation Dataset (NED)]]<br><br><br />
'''[[:Category:Digital_Roughness_Data|Digital Roughness Models (DRM)]]'''<br><hr><br />
* [[Copernicus_Global_Land_Service_-_Land_Cover_100m|Copernicus Global Land Service, Land Cover 100]]<br />
* [[Corine_Land_Cover|Corine Land Cover (2006, 2012 and 2018)]]<br />
* [[Data_For_Wind|European Data For Wind]]<br />
* [[Global_Land_Cover_Characteristics|Global Land Cover Characteristics (GLCC)]]<br />
* [[Glob_Cover|GlobCover]]<br />
* [[MODIS_VCF|MODIS VCF]]<br />
* [[National_Land_Cover_Database_2011|US National Land Cover Database 2011]]<br><br><br />
'''[[:Category:Solar Data|Solar Irradiance Data]]'''<br><hr><br />
* [[Heliosat_(SARAH)|Heliosat (SARAH)]] <br />
* [[Heliosat_(SARAH)_East|Heliosat (SARAH) East]]<br><br><br />
|'''[[:Category:Atlas_Data|Atlas Datasets]]'''<br><hr><br />
* [[GASP_Global|Global Atlas of Siting Parameters (GASP)]]<br />
* [[RASP_Sweden|Regional Atlas of Siting Parameters - Sweden]]<br><br><br />
'''[[:Category:Wind Data|Wind Data]]'''<br><hr><br />
* [[Blended_Coastal_Winds|Blended Coastal Winds]]<br />
* [[CFS-_and_CFSR_Data|CFS- and CFSR Data]]<br />
* [[Danish Windindex Data]]<br />
* [[ERA-Interim|EMD-Global Wind Data (based on ERA-Interim)]]<br />
* [[ERA5_Gaussian_Grid|ERA5 Gaussian Grid]]<br />
* [[ERA5(T)_Rectangular_Grid|ERA5(T) Rectangular Grid]]<br />
* [[MERRA_Data|MERRA Data]]<br />
* [[MERRA2_Data|MERRA-2 Data]]<br />
* [[METAR_Data|METAR Data]]<br />
* [[NCEP_/_NCAR_Global_Reanalysis_Data|NCEP/NCAR Global Reanalysis Data]]<br />
* [[NCEP_North_American_Regional_Reanalysis_Data|North American Regional Reanalysis Data]]<br />
* [[QuikScat|QuikScat Offshore Wind Dataset]]<br />
* [[SYNOP_Data|SYNOP Data]]<br><br><br />
'''[[:Category:Wind Data|EMD-WRF Mesoscale Wind Data]]'''<br><hr><br />
* [[EMD-WRF_Europe%2B|Europe]]: EMD-WRF Europe+ (ERA5)<br />
* [[EMD-ConWx_Meso_Data_Europe|Europe]]: EMD-ConWx Europe (ERA-Interim)<br />
* [[EMD-WRF_On-Demand_and_Custom-Area|Custom Area]]: EMD-WRF Custom-Area<br />
* [[EMD-WRF Middle East|Middle East]]: EMD-WRF Middle East Meso Data<br />
* [[EMD-WRF_South_Korea_(ERA5)|South Korea]]: EMD-WRF South Korea (ERA5)<br />
* [[EMD-WRF South Korea|South Korea]]: EMD-WRF South Korea, ERA-Interim<br />
* [[EMD-WRF South Africa|South Africa]]: EMD-WRF South Africa Meso Data<br />
* [[EMD-WRF India|India]]: EMD-WRF India Meso Data<br />
* [[EMD-WRF Indonesia|Indonesia]]: EMD-WRF Indonesia Meso Data<br />
* [[EMD-WRF_On-Demand_and_Custom-Area|On Demand]]: EMD-WRF Global Meso On-Demand<br />
* [[EMD-WRF_On-Demand_ICING|On Demand ICING]]: EMD-WRF OD ICING<br><br><br />
'''[[:Category:Wind Data|3rd Party Mesoscale Wind Data]]'''<br><hr><br />
* [[KNMI_North_Sea_Wind_(KNMI-KNW)|KNMI-KNW North Sea Wind]]<br />
* [[New_European_Wind_Atlas_(NEWA)|NEWA: New European Wind Atlas]]<br />
* [[NORA3|NORA3: Norwegian Reanalysis]]<br><br><br />
'''[[:Category:Existing_Turbines|Databases on Turbines]]'''<br><hr><br />
* [[Danish_Wind_Turbines|Danish Turbines (makes, positions, productions)]]<br />
* [[Finnish_Wind_Turbines|Finnish Turbines (positions)]]<br />
* [[DE_Wind_Turbines_MaStR|German MaStR Turbines (types, positions)]]<br />
* [[OSM_Turbines|Open Streep Map Turbines (positions only)]]<br />
* [[US_Wind_Turbines|Turbines in the United States]]<br />
* [[WTG_Catalogue|WindPRO Wind Turbine Catalogue]]<br />
|'''[[:Category:Maps|Digital Map Data - Orthophotos]]'''<br><hr><br />
* [[Danish_Orthophoto_Mosaic|Danish Orthophoto Mosaic]]<br />
* [[French_Orthophoto_Mosaic|French Orthophoto Mosaic]]<br />
* [[Finnish_Orthophoto_Mosaic|Finnish Orthophoto Mosaic]]<br />
* [[Latvian_Orthophoto_Mosaic|Latvian Orthophoto Mosaic]]<br />
* [[Spanish_Orthophoto_Mosaic|Spanish Orthophoto Mosaic]]<br />
* [[GeoCover_Images|GeoCover Images]]<br />
* [[WindPRO_Global_Satellite_Imagery|windPRO Global Satellite Imagery - 10m]]<br />
* [[WindPRO_European_Satellite_Imagery|windPRO European Satellite Imagery - 2.5m]]<br><br><br />
'''[[:Category:Maps|Digital Map Data - Topographic Maps]]'''<br><hr><br />
* [[UK-Great_Britain%3A_OS_Open_Maps|British Ordnance Survey OpenData]]<br />
* [[Estonian_Topographic_Map|Estonian Topographic Map]]<br />
* [[German Topographic Maps]]<br />
* [[French_Raster_Map|French Raster Map]]<br />
* [[Finnish_Topographic_Map|Finnish Topographic Map]]<br />
* [[Latvian_Topographic_Map|Latvian Topographic Map]]<br />
* [[Norwegian_Topographic_Map|Norwegian Topographic Map]]<br />
* [[Spanish_Topographic_Map|Spanish Topographic Map]]<br />
* [[Swedish_Topographic_Map|Swedish Topographic Map]]<br />
* [[Open_Street_Map|Open Street Map]]<br />
* [[OnMaps|OnMaps]]<br><br><br />
'''[[:Category:Forest_Maps|Forest Maps (Canopy/Tree Heights)]]'''<br><hr><br />
* [[Danish_KU_Forest_Heights|Danish KU Forest Map]]<br />
* [[Estonian_Canopy_Heights|Estonian Forest Map]]<br />
* [[Finnish_LUKE_Forest_Map|Finnish LUKE Forest Map]]<br />
* [[Near-Global_Forest_Canopy_Heights_GLAD|Near-Global Forest Canopy Heights GLAD]]<br />
* [[Latvian_Canopy_Heights|Latvian Canopy Heights]]<br />
* [[Norwegian_SR16_Forest_Heights|Norwegian SR16 Forest Map]]<br />
* [[Swedish_SLU_Forest_Map|Swedish SLU Forest Map]]<br />
<br />
<br><br />
'''[[:Category:Bathymetry_Models|Digital Bathymetry Models (DBM), Water Depths]]'''<br><hr><br />
* [[EMODnet_Bathymetry|European Bathymetry - EMODnet - 2020, 2018 & 2016]]<br />
* [[Global_Bathymetry_GEBCO|Global Bathymetry - GEBCO - 2021, 2019 & 2014]]<br />
<br><br />
<span style="color:#FF4D00">'''Other Maps, Tools and Data Sources'''</span><br><hr><br />
* [[Commercial_DEM_Providers|3rd Party Commercial DEM Providers]]<br />
* [[Aster_Global_Digital_Elevation_Model|Aster Global Elevation Model (Aster GDEM)]]<br />
* [[CGIAR_SRTM_90m_Digital_Elevation_Data|CGIAR 90m Digital Elevation Data.]]<br />
* [[WindSight_-_Premium_Data_Layers_by_DHI_GRAS|WindSight - Premium Data Layers by DHI GRAS]]<br />
* [[Dynamic_maps|Dynamic maps]]<br />
* [[Google_Earth_Export|Export of WindPRO data into Google Earth]]<br />
* [[ÜSTÜN_windPROSPER_maps|ÜSTÜN windPROSPER maps]]<br />
* [[Web_Map_Service|Web Map Service (WMS)]]<br />
* [[MERIT_DEM|MERIT-DEM - Digital Elevation Model - 90m]]<br />
|}</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Iceland_Elevation_Model&diff=14377
Iceland Elevation Model
2022-05-24T10:31:15Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:ISL10.png|right|thumb|350px|Part of the 10m DTM for Iseland. Credit: Contains elevation data from the National Land Survey of Iceland.]]<br />
== Introduction ==<br />
Iceland LMI Elevation Model 2016 - 10m resolution (LMÍ Hæðarlíkan 2016): The National Land Survey of Iceland has released a DTM of Iceland: This new DTM is an upgrade of an earlier DTM. The recent data added, which vary in origin, cover 39.100 km2 or some 38% of the country. <br />
<br />
Dataset Overview<br />
* Spatial Coverage: Iceland<br />
* Reesolution: 10m<br />
* Data type: Digital Terrain Model (DTM)<br />
* Coordinate system: ISN93 / Lambert (EPSG:3057) <br />
* Version: 2016<br />
* Vertical accuracy: Varies, see image to the right and usage notes below<br />
<br />
== Usage Notes ==<br />
* An alternativetive Icelandic elevation model (IcelandDEM version 1.0) is available from the icelandic<br />
* The vertical accuracy of the new (2016) data added are (see location on figure to the right): 1) Lidar data for the glaciers of Iceland (surveyed in the years 2007-2012), 15144 km2, LE90: 2.65 m. 2) Data from 5-m-contour lines, 10736 km2, LE90: 3.9 m. 3) Emisar radar data, 4536 km2, LE90: 3.2 m. 4) Data from 10-m-contour lines, 2938 km2, LE90: 8.48 m, 5) SwedeSurvey photogrammetic data, 1433 km2, LE90: 2.60 m, 6) 1:25.000 contour data, 1152 km2, LE90: 3.8 m, 7) British lidar data (courtesy of Dr. Susan Conway, Open University), 532 km2, LE90: 0.96-4.63 m.<br />
<br />
== Availability from within WindPRO ==<br />
The data are available directly from within windPRO in 10-meter resolution. The data can be accessed from the online-services in the following objects:<br />
* Line Object (with purpose to height contour lines)<br />
* Elevation Grid Object<br />
<br />
== Acknowledgement == <br />
* The LMI are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy - and wind energy in particular.<br />
<br />
== License ==<br />
The product belongs to the open geo-data of Iceland - and it was released from the National Land Survey of Iceland according to the Creative Commons Attribution 4.0 International License. An attribution must be made when using data, such as:<br />
* Contains data from the Iceland LMI Elevation Model 2016 from the National Land Survey of Iceland (LMI)<br />
* Distribution through EMD and windPRO. CC BY 4.0.<br />
<br />
== External Links ==<br />
* [https://www.lmi.is The National Land Survey of Iceland]</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:ISL10.png&diff=14376
File:ISL10.png
2022-05-24T10:29:17Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Iceland_Elevation_Model&diff=14375
Iceland Elevation Model
2022-05-24T10:26:03Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:BEL.jpg|right|thumb|350px|Part of the 1m DTM for Flanders, Belgium. Credit: Contains elevation data from the Agency of Geographical Information Flanders (Agentschap voor Geografische Informatie Vlaanderen) - Digital Height Model Flanders 2013-2015]]<br />
== Introduction ==<br />
Iceland LMI Elevation Model 2016 - 10m resolution (LMÍ Hæðarlíkan 2016): The National Land Survey of Iceland has released a DTM of Iceland: This new DTM is an upgrade of an earlier DTM. The recent data added, which vary in origin, cover 39.100 km2 or some 38% of the country. <br />
<br />
Dataset Overview<br />
* Spatial Coverage: Iceland<br />
* Reesolution: 10m<br />
* Data type: Digital Terrain Model (DTM)<br />
* Coordinate system: ISN93 / Lambert (EPSG:3057) <br />
* Version: 2016<br />
* Vertical accuracy: Varies, see image to the right and usage notes below<br />
<br />
== Usage Notes ==<br />
* An alternativetive Icelandic elevation model (IcelandDEM version 1.0) is available from the icelandic<br />
* The vertical accuracy of the new (2016) data added are (see location on figure to the right): 1) Lidar data for the glaciers of Iceland (surveyed in the years 2007-2012), 15144 km2, LE90: 2.65 m. 2) Data from 5-m-contour lines, 10736 km2, LE90: 3.9 m. 3) Emisar radar data, 4536 km2, LE90: 3.2 m. 4) Data from 10-m-contour lines, 2938 km2, LE90: 8.48 m, 5) SwedeSurvey photogrammetic data, 1433 km2, LE90: 2.60 m, 6) 1:25.000 contour data, 1152 km2, LE90: 3.8 m, 7) British lidar data (courtesy of Dr. Susan Conway, Open University), 532 km2, LE90: 0.96-4.63 m.<br />
<br />
== Availability from within WindPRO ==<br />
The data are available directly from within windPRO in 10-meter resolution. The data can be accessed from the online-services in the following objects:<br />
* Line Object (with purpose to height contour lines)<br />
* Elevation Grid Object<br />
<br />
== Acknowledgement == <br />
* The LMI are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy - and wind energy in particular.<br />
<br />
== License ==<br />
The product belongs to the open geo-data of Iceland - and it was released from the National Land Survey of Iceland according to the Creative Commons Attribution 4.0 International License. An attribution must be made when using data, such as:<br />
* Contains data from the Iceland LMI Elevation Model 2016 from the National Land Survey of Iceland (LMI)<br />
* Distribution through EMD and windPRO. CC BY 4.0.<br />
<br />
== External Links ==<br />
* [https://www.lmi.is The National Land Survey of Iceland]</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Iceland_Elevation_Model&diff=14374
Iceland Elevation Model
2022-05-24T10:24:05Z
<p>Ronnie: Created page with "Category: Online DataCategory: Digital Elevation ModelsCategory: InnoWind Image:BEL.jpg|right|thumb|350px|Part of the 1m DTM for Flanders, Belgium. Credit: Conta..."</p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:BEL.jpg|right|thumb|350px|Part of the 1m DTM for Flanders, Belgium. Credit: Contains elevation data from the Agency of Geographical Information Flanders (Agentschap voor Geografische Informatie Vlaanderen) - Digital Height Model Flanders 2013-2015]]<br />
== Introduction ==<br />
Iceland LMI Elevation Model 2016 - 10m resolution (LMÍ Hæðarlíkan 2016): The National Land Survey of Iceland has released a DTM of Iceland: This new DTM is an upgrade of an earlier DTM. The recent data added, which vary in origin, cover 39.100 km2 or some 38% of the country. <br />
<br />
Dataset Overview<br />
* Spatial Coverage: Iceland<br />
* Reesolution: 10m<br />
* Data type: Digital Terrain Model (DTM)<br />
* Coordinate system: ISN93 / Lambert (EPSG:3057) <br />
* Version: 2016<br />
* Vertical accuracy: Varies, see image to the right and usage notes below<br />
<br />
== Usage Notes ==<br />
* An alternativetive Icelandic elevation model (IcelandDEM version 1.0) is available from the icelandic<br />
* The vertical accuracy of the new (2016) data added are (see location on figure to the right): 1) Lidar data for the glaciers of Iceland (surveyed in the years 2007-2012), 15144 km2, LE90: 2.65 m. 2) Data from 5-m-contour lines, 10736 km2, LE90: 3.9 m. 3) Emisar radar data, 4536 km2, LE90: 3.2 m. 4) Data from 10-m-contour lines, 2938 km2, LE90: 8.48 m, 5) SwedeSurvey photogrammetic data, 1433 km2, LE90: 2.60 m, 6) 1:25.000 contour data, 1152 km2, LE90: 3.8 m, 7) British lidar data (courtesy of Dr. Susan Conway, Open University), 532 km2, LE90: 0.96-4.63 m.<br />
<br />
== Availability from within WindPRO ==<br />
The data are available directly from within windPRO in 10-meter resolution. The data can be accessed from the online-services in the following objects:<br />
* Line Object (with purpose to height contour lines)<br />
* Elevation Grid Object<br />
<br />
== Acknowledgement == <br />
* The LMI are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy - and wind energy in particular.<br />
<br />
== License ==<br />
The product belongs to the open geo-data of Iceland - and it was released from the National Land Survey of Iceland according to the Creative Commons Attribution 4.0 International License. An attribution must be made when using data, such as:<br />
* Contains data from the Iceland LMI Elevation Model 2016 from the National Land Survey of Iceland (LMI)<br />
* Distribution through EMD and windPRO. CC BY 4.0.<br />
<br />
== External Links ==<br />
* [https://www.lmi.is] The National Land Survey of Iceland]</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=13878
EMD-API - Wind Energy Index Service
2021-12-13T11:24:38Z
<p>Ronnie: </p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The Wind Energy Index API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (Location Index) (API) - [https://api.emd.dk/wind-index/location/ui/ here].<br />
* EMD-API Wind Energy Index UI (Turbine Index) (API) - [https://api.emd.dk/wind-index/turbine/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/advanced/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the zip-file holding the OpenAPI Python client for the [https://help.emd.dk/mediawiki/images/3/33/20211206_python_client_windindex_turbine.zip turbine] or the [https://help.emd.dk/mediawiki/images/5/5a/20211206_python_client_windindex_location.zip location] emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [https://help.emd.dk/mediawiki/images/8/8e/Emdapi_windindex_notebook.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/advanced/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/advanced/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=13877
EMD-API - Wind Energy Index Service
2021-12-13T11:23:29Z
<p>Ronnie: </p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The Wind Energy Index API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (Location Index) (API) - [https://api.emd.dk/wind-index/location/ui/ here].<br />
* EMD-API Wind Energy Index UI (Turbine Index) (API) - [https://api.emd.dk/wind-index/turbine/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/advanced/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the zip-file holding the OpenAPI Python client for the [https://help.emd.dk/mediawiki/images/3/33/20211206_python_client_windindex_turbine.zip turbine] or the [https://help.emd.dk/mediawiki/images/5/5a/20211206_python_client_windindex_location.zip location] emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [:File:emdapi_windindex_notebook.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/advanced/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/advanced/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:Emdapi_windindex_notebook.zip&diff=13876
File:Emdapi windindex notebook.zip
2021-12-13T11:22:53Z
<p>Ronnie: MsUpload</p>
<hr />
<div>MsUpload</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=13875
EMD-API - Wind Energy Index Service
2021-12-13T11:21:05Z
<p>Ronnie: </p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The Wind Energy Index API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (Location Index) (API) - [https://api.emd.dk/wind-index/location/ui/ here].<br />
* EMD-API Wind Energy Index UI (Turbine Index) (API) - [https://api.emd.dk/wind-index/turbine/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/advanced/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the zip-file holding the OpenAPI Python client for the [https://help.emd.dk/mediawiki/images/3/33/20211206_python_client_windindex_turbine.zip turbine] or the [https://help.emd.dk/mediawiki/images/5/5a/20211206_python_client_windindex_location.zip location] emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [https://help.emd.dk/mediawiki/images/1/15/20210106_EMDAPI_WindIndexAdvanced.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/advanced/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/advanced/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:20211206_python_client_windindex_location.zip&diff=13874
File:20211206 python client windindex location.zip
2021-12-13T11:20:16Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=13873
EMD-API - Wind Energy Index Service
2021-12-13T11:19:58Z
<p>Ronnie: </p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The Wind Energy Index API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (Location Index) (API) - [https://api.emd.dk/wind-index/location/ui/ here].<br />
* EMD-API Wind Energy Index UI (Turbine Index) (API) - [https://api.emd.dk/wind-index/turbine/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/advanced/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the zip-file holding the OpenAPI Python client for the [https://help.emd.dk/mediawiki/images/3/33/20211206_python_client_windindex_turbine.zip turbine] or the [https://help.emd.dk/mediawiki/images/3/33/20211206_python_client_windindex_turbine.zip location] emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [https://help.emd.dk/mediawiki/images/1/15/20210106_EMDAPI_WindIndexAdvanced.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/advanced/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/advanced/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:20211206_python_client_windindex_turbine.zip&diff=13872
File:20211206 python client windindex turbine.zip
2021-12-13T11:18:36Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=13772
EMD-API - Wind Energy Index Service
2021-11-26T10:26:46Z
<p>Ronnie: /* Access */</p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The Wind Energy Index API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (Location Index) (API) - [https://api.emd.dk/wind-index/location/ui/ here].<br />
* EMD-API Wind Energy Index UI (Turbine Index) (API) - [https://api.emd.dk/wind-index/turbine/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/advanced/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the [https://help.emd.dk/mediawiki/images/9/9a/20210106-python-generated-wind-index-advanced.zip zip-file] holding the OpenAPI Python client for the emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [https://help.emd.dk/mediawiki/images/1/15/20210106_EMDAPI_WindIndexAdvanced.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/advanced/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/advanced/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=13764
EMD-API - Wind Energy Index Service
2021-11-25T06:47:01Z
<p>Ronnie: /* Access */</p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The Wind Energy Index API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (Location Index) (API) - [https://api.emd.dk/wind-index/premium/ui/ here].<br />
* EMD-API Wind Energy Index UI (Turbine Index) (API) - [https://api.emd.dk/wind-index/advanced/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/advanced/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the [https://help.emd.dk/mediawiki/images/9/9a/20210106-python-generated-wind-index-advanced.zip zip-file] holding the OpenAPI Python client for the emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [https://help.emd.dk/mediawiki/images/1/15/20210106_EMDAPI_WindIndexAdvanced.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/advanced/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/advanced/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=12714
EMD-API - Wind Energy Index Service
2021-01-06T12:45:25Z
<p>Ronnie: /* Client Software Other Languages and Tools */</p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (API) - [https://api.emd.dk/wind-index/advanced/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/advanced/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the [https://help.emd.dk/mediawiki/images/9/9a/20210106-python-generated-wind-index-advanced.zip zip-file] holding the OpenAPI Python client for the emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [https://help.emd.dk/mediawiki/images/1/15/20210106_EMDAPI_WindIndexAdvanced.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/advanced/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/advanced/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=12713
EMD-API - Wind Energy Index Service
2021-01-06T12:43:51Z
<p>Ronnie: </p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (API) - [https://api.emd.dk/wind-index/advanced/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/advanced/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the [https://help.emd.dk/mediawiki/images/9/9a/20210106-python-generated-wind-index-advanced.zip zip-file] holding the OpenAPI Python client for the emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [https://help.emd.dk/mediawiki/images/1/15/20210106_EMDAPI_WindIndexAdvanced.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:20210106_EMDAPI_WindIndexAdvanced.zip&diff=12712
File:20210106 EMDAPI WindIndexAdvanced.zip
2021-01-06T12:42:12Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Wind_Energy_Index_Service&diff=12711
EMD-API - Wind Energy Index Service
2021-01-06T12:40:09Z
<p>Ronnie: </p>
<hr />
<div>[[Category:EMD-API]][[Image:wind-energy-in-dk.png|thumb|350px|right|Comissioning of On-Shore Wind Turbines in Denmark.]][[File:EMDAPI_451x303.jpg|thumb|350px|right]]<br />
== Introduction ==<br />
The Wind Energy Index Service is available as a global service - providing reliable wind-index information for turbine locations in any part of the world.<br />
The service is available from a REST / OPENAPI interface. This page describes how to install the service - and how to consume it from a Python client. Resources for the OpenAPI standard and the data model are here: <br />
<br />
* [https://swagger.io/specification/ OpenAPI-standard] - at Swagger / Smartbear<br />
* [https://github.com/OAI/OpenAPI-Specification OpenAPI Specification and Data Model] - at GitHub.<br />
<br />
Please note: <br />
* This EMD-API introduction is aimed at programmers, modellers or analysts who are working with machine-driven interfaces and workflows, typically using programming languages like [https://www.python.org/ Python] or [https://www.r-project.org/about.html R]. <br />
* Also note, that we provide a Python (Jupyter Notebook) example to get you kick-started in using our API-services and to integrate towards your own services and tools.<br />
<br />
== Access ==<br />
The API is currently (December 2020) in beta-release. To see more documentation and to access the data-services, please visit the API through the following URL's:<br />
<br />
* EMD-API Overview (Wiki) - [https://help.emd.dk/mediawiki/index.php?title=Category%3AEMD-API here].<br />
* EMD-API Main Page (API) - [https://api.emd.dk here].<br />
* EMD-API Wind Energy Index UI (API) - [https://api.emd.dk/wind-index/advanced/ui/ here].<br />
<br />
Any technical questions on our Wind Energy Index Services can be addressed to our Senior Wind Energy Consultant Henrik S. Pedersen: [mailto:hsp@emd.dk hsp@emd.dk].<br />
<br />
== Data Model - Wind Energy Index Service ==<br />
The EMD Wind Energy Index Service is documented in a REST based API using the OpenAPI Specification. You can view the interfaces and download the interfaces as JSON or YAML, [https://api.emd.dk/wind-index/ui/ here]. The service provides the following functionality:<br />
<br />
* ''List Turbines'': Available turbines with their identification ID. The list is private and will return all turbines connected to your account.<br />
* ''Turbine Details'': Name, data-availability, hub-height, position-latitude, position-longitude, power-curve, control-strategy, rated-power, training-start, training-end, time-zone.<br />
* ''Wind Energy Index Data'': Month-wind-index, month-anomaly-index, month-predicted-production, last-quarter-index, last-12-months-average-index<br />
** Request data for all months between 1990 and present<br />
** Request data for specific month between 1990 and present<br />
<br />
Reference index period is the 15-year period from 2004-2018 (both years inclusive). Currently, the following parameters are returned from the wind-energy-index tables:<br />
<br />
* ''month_index'': Wind energy index for current month (seen in comparison to reference period of 2004-2018)<br />
* ''month_anormality_index'': Deviation of current month seen in comparison to average-index of the same month in full reference period (e.g. index for January-2020 divided by average-January-2004-2018).<br />
* ''last12_month_index'': Average index of the previous 12 month (including the month of consideration) compared to reference period (2004-2018).<br />
* ''predicted_production'': Estimated production for the month (kWh).<br />
* ''last_full_quarter_index'': Average index for the previous calendar quarter (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018-Q4)<br />
* ''last_full_year_index'': Average index for the previous calendar year (e.g. when requesting data from 2019-02 (February), this index returned will be from 2018)<br />
<br />
== Python - Installation == <br />
[[File:turbinelocation_sample.png|thumb|350px|right|Turbine Location from EMD-API.]]The simplest way to use the EMDAPI with Python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of Python. When the virtual environment is created, then activate the environment.<br />
<br />
''Open your Anaconda Prompt. Copy-paste the following lines:''<br />
<pre><br />
conda create -n emdapiwindindex python=3.8.5<br />
conda activate emdapiwindindex<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the Jupyter Notebooks. We have validated this setup using specific package versions (used in the commands below). <br />
<br />
''In the Anaconda Prompt, copy-paste the following lines, one by one:''<br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1 matplotlib=3.3.1 pyproj=3.0.0<br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
pip install tilemapbase<br />
</pre><br />
<br />
Download the [https://help.emd.dk/mediawiki/images/9/9a/20210106-python-generated-wind-index-advanced.zip zip-file] holding the OpenAPI Python client for the emdapi wind-index-service. <br><br />
Unpack the file and install it within your virtual environment:<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have unpacked the zipped file. Copy-paste the following line:''<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
== Python and Jupyter Notebook Examples for Demonstration and Test ==<br />
[[File:windindex_krogstrupenge.png|thumb|450px|right|Wind Index From Years 2018-2019 with Reference Period (2004-2018)]]<br />
In order to test your setup and learn how-to use the EMDAPI Wind Energy Index Service, we suggest that you download our Jupyter Notebook and Python examples - [https://help.emd.dk/mediawiki/images/e/e7/20201217_EMDAPI_WindIndexService.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If Jupyter prompts for you to select another Python-kernel, then select the emdapiwindindex kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<br />
''In the Anaconda Prompt: Move to the folder, where you have saved the Jupyter Notebook examples. Copy paste the following line to open Jupyter Notebook from where you can open the examples.'' <br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and Jupyter user-interface), run select the Jupyter Notebook file (*.ipynb). <br><br />
Then work your way through the example provided:<br />
<br />
# ''emdapi_windindexservice.ipynb'' Demonstration of login to the system, plotting turbine data and requesting wind-energy-index data.<br />
# ''emdapi_windindex_python.py'': Python code to demonstrate API login and requests for data (to execute - simply run ''python emdapi_windindex_python.py'' in your conda environment)<br />
<br />
Make sure that the new emdapi virtual environment (python-kernel) is available to be used with Jupyter Notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapiwindindex<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
REST and OpenAPI is easily consumed from a lot of software tools. It is perfectly possible that your preferred language is supported. OpenAPI works well with languages such as - but not limited to - C#, R, Python, Java, PHP, Scala and Swift. Just download the YAML or JSON service description and use the [https://editor.swagger.io/ Swagger Editor] or [https://github.com/OpenAPITools/openapi-generator OpenAPI Generator] to generate the client libraries for your preferred software. Then you are ready to integrate towards your preferred systems and workflows.<br />
<br />
To generate the client libraries yourself - one possible approach is to:<br />
<br />
# Download the OpenAPI (openapi.yaml or openapi.json) description files - [https://api.emd.dk/wind-index/openapi.yaml here-yaml] or [https://api.emd.dk/wind-index/openapi.json here-json]<br />
# Load it into the Swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the Swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:20210106-python-generated-wind-index-advanced.zip&diff=12710
File:20210106-python-generated-wind-index-advanced.zip
2021-01-06T12:39:03Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Climate_Data_Access&diff=12163
EMD-API - Climate Data Access
2020-09-08T06:15:55Z
<p>Ronnie: </p>
<hr />
<div>== Origin and Purpose ==<br />
[[File:EMDAPI_451x303.jpg|thumb|400px|right]]EMDAPI is a software library from EMD International. It delivers a unified interface to a wide range of climate data. EMDAPI helps consultants, analysts and scientists working with high-resolution climate data in achieving their goals in an efficient way. It has the following key-features: <br />
<br />
* '''Instant data delivery''': All datasets within the EMDAPI are ready processed and requests are served within seconds or minutes<br />
* '''40+ climate datasets''': EMDAPI provides access more than 40 of the best local, regional and global climate datasets and allows access to more than 1Pb of data. <br />
* '''Unified interface''': The unified interface which allows for integration to internal processes and tools - and also very efficient uncertainty analysis with gigabytes of data easily accessed. <br />
* '''Trusted datasets''': EMDAPI builds upon the trusted data-bases and data-sources that have been used through the [http://help.emd.dk/mediawiki/index.php?title=Main_Page online-data services] in windPRO for more than a decade. <br />
* '''Built on open standards''': EMDAPI is a [https://en.wikipedia.org/wiki/Representational_state_transfer REST] based service that implements the [https://swagger.io/specification/ OpenAPI] standard]. <br />
* '''Available from any development tool''': Access to the climate databases is available from your preferred development platform - C#, R, python, html, java, php, scala and swift. Just use the OpenAPI tools to generate the client software for your preferred platform.<br />
<br />
=== Access ===<br />
The API is currently (August 2020) in beta-release. <br />
To see more documentation and to access the data-services, please visit the API through the following URL:<br />
<br />
* https://api.emd.dk<br />
<br />
== Python - Installation and Test == <br />
[[Image:EgmondAanZee.jpg|thumb|400px|right|Data nodes near the Egmond Aan Zee Offhore ]]The simplest way to use the EMDAPI with python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of python. When the virtual environment is created, then activate the environment.<br />
<pre><br />
conda create -n emdapi python=3.8.5<br />
conda activate emdapi<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the jupyter notebooks. We have have validated this setup using specific package versions (used in the commands below). <br />
<pre><br />
conda install -c conda-forge pandas=1.1.0 numpy=1.19.1<br />
conda install -c conda-forge matplotlib=3.3.1 basemap=1.2.2 basemap-data-hires=1.2.2 <br />
conda install -c conda-forge jupyter=1.0.0 ipykernel=5.3.4 <br />
</pre><br />
<br />
Download the [http://api.emd.dk/static/python-client-generated.zip zipped-file] holding the OpenAPI python client. <br><br />
Unpack the file and install it within your virtual environment:<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
Make sure that the new emdapi virtual enviroment (python-kernel) is available to be used with jupyter-notebook environment:<br />
<pre><br />
python -m ipykernel install --user --name=emdapi<br />
</pre><br />
<br />
In order to test your setup and learn to use the EMDAPI, we suggest that you download the jupyter-notebook examples that we have created - [https://help.emd.dk/mediawiki/images/4/4b/EMDAPI_JupyterNotebooks.zip here].<br><br />
Unpack the zip files and run the command below in your terminal or command-shell.<br><br />
If jupyter prompts for you to select another python-kernel, then select the emdapi kernel (may also be selected directly from the 'Kernel' drop-down menu).<br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
Within the internet-browser (and jupyter user-interface), run select the notebook file (*.ipynb). <br><br />
Then work your way through through each example provided.<br />
<br />
== Client Software Other Languages and Tools ==<br />
A list of client software generated from the [https://editor.swagger.io swagger editor] is found below. <br />
<br />
# [https://api.emd.dk/static/python-client-generated.zip Python]<br />
# [https://api.emd.dk/static/csharp-client-generated.zip CSharp]<br />
# [https://api.emd.dk/static/html2-client-generated.zip HTML2]<br />
# [https://api.emd.dk/static/dynamic-html-client-generated.zip Dynamic HTML]<br />
# [https://api.emd.dk/static/r-client-generated.zip R]<br />
# [https://api.emd.dk/static/java-client-generated.zip Java]<br />
# [https://api.emd.dk/static/scala-client-generated.zip Scala]<br />
# [https://api.emd.dk/static/php-client-generated.zip PHP]<br />
# [https://api.emd.dk/static/swift5-client-generated.zip Swift5]<br />
<br />
If you want to generate the client libries yourself - or use other tool than mentioned above - one possible process is to:<br />
<br />
# Download the OpenAPI (openapi.yaml) description file - [https://api.emd.dk/openapi.yaml here]<br />
# Load it into the swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-API_-_Climate_Data_Access&diff=12145
EMD-API - Climate Data Access
2020-09-07T11:54:59Z
<p>Ronnie: </p>
<hr />
<div>== Origin and Purpose ==<br />
[[File:EMDAPI_451x303.jpg|thumb|400px|right]]EMDAPI is a software library by EMD delivering a unified interface to a wide range of climate data. EMDAPI helps consultants, analysts and scientists working with high-resolution climate data in achieving their goals in an efficient way, it has the following key-features: <br />
<br />
* '''Instant data delivery''': All datasets within the EMDAPI are ready processed and requests are served within seconds or minutes<br />
* '''40+ climate datasets''': EMDAPI provides access more than 40 of the best climate datasets and allows access to more than 1Pb of data. <br />
* '''Unified interface''': The unified interface which allows for integration to internal processes and tools - and also very efficient uncertainty analysis with gigabytes of data easily accessed. <br />
* '''Trusted datasets''': EMDAPI builds upon the trusted data-bases and data-sources that have been used through the [http://help.emd.dk/mediawiki/index.php?title=Main_Page online-data services] in windPRO for more than a decade. <br />
* '''Built on open standards''': EMDAPI is a [https://en.wikipedia.org/wiki/Representational_state_transfer REST] based service that implements the [https://swagger.io/specification/ OpenAPI] standard]. <br />
* '''Available from any development tool''': Access to the climate databases is available from your preferred development platform - C#, R, python, html, java, php, scala and swift. Just use the OpenAPI tools to generate the client software for your preferred platform.<br />
<br />
=== Access ===<br />
The API is currently (August 2020) in beta-release. <br />
To see more documentation and to access the data-services, please visit the API through the following URL:<br />
<br />
* https://api.emd.dk<br />
<br />
== Python - Installation and Test == <br />
[[Image:EgmondAanZee.jpg|thumb|400px|right|Data nodes near the Egmond Aan Zee Offhore ]]The simplest way to use the EMDAPI with python is to install the client software in a virtual environment. If you are using CONDA or [https://docs.conda.io/en/latest/miniconda.html MINICONDA], we recommend that you create a new virtual environment and use a recent 3.x version of python. When the virtual environment is created, then activate the environment.<br />
<pre><br />
conda create -n emdapi python=3.8<br />
conda activate emdapi<br />
</pre><br />
<br />
Install the required packages needed in order to do data-science and use the examples provided within the jupyter notebooks:<br />
<pre><br />
conda install pandas numpy matplotlib basemap basemap-data-hires jupyter ipykernel <br />
</pre><br />
<br />
Download the [http://api.emd.dk/static/python-client-generated.zip zipped-file] holding the OpenAPI python client. Unpack the file and install it within your virtual environment:<br />
<pre><br />
python setup.py install<br />
</pre><br />
<br />
Ensure that the python-kernel is available from the jupyter-notebook:<br />
<pre><br />
python -m ipykernel install --user --name=emdapi<br />
</pre><br />
<br />
Download and unpack a few jupyter notebook examples [https://help.emd.dk/mediawiki/images/4/4b/EMDAPI_JupyterNotebooks.zip here] and then give the installation a test-drive using the jupyter notebooks provided<br />
<pre><br />
jupyter notebook<br />
</pre><br />
<br />
== Client Software Other Languages and Tools ==<br />
A list of client software generated from the [https://editor.swagger.io swagger editor] is found below. <br />
<br />
# [https://api.emd.dk/static/python-client-generated.zip Python]<br />
# [https://api.emd.dk/static/csharp-client-generated.zip CSharp]<br />
# [https://api.emd.dk/static/html2-client-generated.zip HTML2]<br />
# [https://api.emd.dk/static/dynamic-html-client-generated.zip Dynamic HTML]<br />
# [https://api.emd.dk/static/r-client-generated.zip R]<br />
# [https://api.emd.dk/static/java-client-generated.zip Java]<br />
# [https://api.emd.dk/static/scala-client-generated.zip Scala]<br />
# [https://api.emd.dk/static/php-client-generated.zip PHP]<br />
# [https://api.emd.dk/static/swift5-client-generated.zip Swift5]<br />
<br />
If you want to generate the client libries yourself - or use other tool than mentioned above - one possible process is to:<br />
<br />
# Download the OpenAPI (openapi.yaml) description file - [https://api.emd.dk/openapi.yaml here]<br />
# Load it into the swagger editor - [https://editor.swagger.io here]<br />
# Choose to "Generate Client" from the drop-down menu within the swagger editor.</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-WRF_South_Korea_(ERA5)&diff=11413
EMD-WRF South Korea (ERA5)
2020-03-02T13:23:47Z
<p>Ronnie: Ronnie moved page EMD-WRF South Korea (ERA5) to EMD-WRF South Korea - ERA5</p>
<hr />
<div>#REDIRECT [[EMD-WRF South Korea - ERA5]]</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=EMD-WRF_South_Korea_-_ERA5&diff=11412
EMD-WRF South Korea - ERA5
2020-03-02T13:23:46Z
<p>Ronnie: Ronnie moved page EMD-WRF South Korea (ERA5) to EMD-WRF South Korea - ERA5</p>
<hr />
<div>[[Category:Online Data]][[Category:Wind Data]][[Image:south_korea_domain.png|right|thumb|450px|EMD-WRF South Korea (ERA5) Domain]]<br />
== Introduction ==<br />
The EMD-WRF South Korea (ERA5) dataset is our most accurate, high-resolution mesoscale dataset covering South Korea and minor parts of Japan and North Korea. This mesoscale dataset is derived from [[ERA5_Data|ERA-5]] reanalysis data from ECMWF (http://www.ecmwf.int) as its boundary conditions. As a premium dataset, a ‘EMD-WRF South Korea (ERA5)’ subscription gives you instant access to 45000+ continuously updated onshore and offshore mesoscale time series. The domain area is shown in the figure to the right. <br />
<br />
Direct access to time series via windPRO’s user friendly Online Data Service - i.e. no delivery time!<br />
<br />
The WRF mesoscale model is run in an optimized configuration at a high spatial resolution of 3km to produce hourly time series. Spatial visualization of the full domain is included via [http://www.windprospecting.com windprospecting.com] - and typically accessed from within windPRO.<br />
<br />
At release time, the timespan of the data is at least 20 years back from today. Data access is via windPRO’s user friendly interface to on-line data and requires payment of an annual subscription fee. The EMD-WRF South Korea (ERA5) dataset is the successor of the [[EMD-WRF_South_Korea_(ERA-Interim)|EMD-WRF South Korea (ERA-Interim)]].<br />
<br />
== Release Schedule and Availability ==<br />
Data is updated monthly with approximately 3 months delay defined by ERA-5's availability (from ECMWF), download time from the Copernicus Climate Datastore (from EU-Storage Servers) and computational efforts regarding EMD's high-performance computer clusters (from EMD). Before contacting support because of delays in the release schedule, please check ERA-5's availability on the ERA5 availability page (see link below). 'EMD-WRF South Korea (ERA5)' data cannot be computed at EMD HPC before ERA-5 data is available and have been downloaded to our storage system.<br />
<br />
== Validation ==<br />
The below table shows the correlation statistcs (R2) for the 'New' EMD-WRF South Korea based on ERA5 using 14 masts in the South Korea domain. Notice the significant improvement in R2 compared to the 'Old' ERA-Interim based EMD-WRF and the raw ERA5 data. <br />
<br />
[[Image:ValidTable.png|center|thumb|750px|Validation against 14 masts.]]<br />
<br />
== Dataset Parameters == <br />
A total of 127 sensors (parameters) are ready available for in-depth analysis directly in windPRO. The different parameters in the EMD-WRF South Korea (ERA5) dataset that are available from within windPRO are shown in the table below.<br />
<br />
{| class="wikitable"<br />
|+ align="bottom"|Table: Overview of EMD-WRF South Korea (ERA5) Dataset Parameters (3 km grid).<br />
!Parameter<br />
!Unit<br />
!Description<br />
!Type<br />
|-<br />
|time <br />
|<br />
|UTC time stamp<br />
|<br />
|-<br />
|wSpeed.x<br />
|m/s<br />
|Wind speeds at different heights above ground (x).<br>Heights (x): 10,25,50,75,100,150,200,300,400,500,750,1000,4000m<br />
|Instantaneous<br />
|-<br />
|wDir.x<br />
|deg<br />
|Wind directions at different heights above ground (x).<br>Heights (x): 10,25,50,75,100,150,200,300,400,500,750,1000,4000m<br />
|Instantaneous<br />
|-<br />
|sqrtTKE.x<br />
|m/s<br />
|Wind speed given as standard deviation in m/s. Derived from the turbulent kinetic energy (TKE) at different heights above ground (x).<br>Heights (x): 10,25,50,75,100,150,200,300,400,500,750,1000,4000m<br />
|Instantaneous<br />
|-<br />
|press.x<br />
|Pa<br />
|Pressure at different heights above ground (x).<br>Heights (x): 10,25,50,75,100,150,200,300,400,500,750,1000,4000m<br />
|Instantaneous<br />
|-<br />
|temp.x<br />
|celcius<br />
|Temperature at different heights above ground (x).<br>Heights (x): 10,25,50,75,100,150,200,300,400,500,750,1000,4000m<br />
|Instantaneous<br />
|-<br />
|rh.x<br />
|%<br />
|Relative humidity at different heights above ground (x).<br>Heights (x): 10,25,50,75,100,150,200,300,400,500,750,1000,4000m<br />
|Instantaneous<br />
|-<br />
|cloudWater.x<br />
|kg/kg<br />
|Cloud water content at different heights above ground (x).<br>Heights (x): 10,25,50,75,100,150,200,300,400,500,750,1000,4000m<br />
|Instantaneous<br />
|-<br />
|cloudIce.x<br />
|kg/m^2<br />
|Cloud icing content at different heights above ground (x).<br>Heights (x): 10,25,50,75,100,150,200,300,400,500,750,1000,4000m<br />
|Instantaneous<br />
|-<br />
|psfc<br />
|Pa<br />
|Pressure at site<br />
|Instantaneous<br />
|-<br />
|msl<br />
|Pa<br />
|Pressure at mean sea level<br />
|Instantaneous<br />
|-<br />
|wSpeed.850hpa<br />
|m/s<br />
|Wind speeds at pressure level 850hPa.<br />
|Instantaneous<br />
|-<br />
|wDir.850hpa<br />
|deg<br />
|Wind speeds at pressure levels 850hPa.<br />
|Instantaneous<br />
|-<br />
|temperature.2<br />
|celcius<br />
|Temperatures at 2m<br />
|Instantaneous<br />
|-<br />
|waterTemp<br />
|celcius<br />
|Water temperature<br />
|Instantaneous<br />
|-<br />
|soilTemp.0-10cm<br />
|celcius<br />
|The temperature in the upper 10 cm of the soil<br />
|Instantaneous<br />
|-<br />
|relHumidity.2<br />
|%<br />
|Relative humidity in height 2m above ground level<br />
|Instantaneous<br />
|-<br />
|snowDepth<br />
|m<br />
|Snow depth (if present)<br />
|Instantaneous<br />
|-<br />
|vis.s<br />
|m<br />
|Visibility at surface<br />
|Instantaneous<br />
|-<br />
|sensHeatFlux.s<br />
|w/m2<br />
|Sensible Heat Flux at surface<br />
|Instantaneous<br />
|-<br />
|totPrecip.s<br />
|kg/m^2<br />
|Total Precipitation at surface<br />
|1h Accumulated<br />
|-<br />
|downShortWaveFlux.s<br />
|w/m2<br />
|Downward shortwave irradiance at surface<br />
|1h Average<br />
|-<br />
|swdDir.s<br />
|w/m2<br />
|Direct shortwave irradiance at surface<br />
|1h Average<br />
|-<br />
|swdDif.s<br />
|w/m2<br />
|Diffuse shortwave irradiance at surface<br />
|1h Average<br />
|-<br />
|cloudBottom<br />
|m<br />
|Height of cloud bottom<br />
|Instantaneous<br />
|-<br />
|cloudTop<br />
|m<br />
|Height of cloud top<br />
|Instantaneous<br />
|-<br />
|totalCloudCover.a<br />
|%<br />
|Total cloud cover in atmosphere<br />
|1h Average<br />
|-<br />
|convCloudCover.a<br />
|%<br />
|Convective cloud cover in atmosphere<br />
|1h Average<br />
|-<br />
|rmol<br />
|1/m<br />
|Inverse Monin-Obukhov-Length<br />
|<br />
|-<br />
|znt<br />
|m<br />
|Rougnhess length<br />
|Instantaneous<br />
|-<br />
|u*<br />
|m/s<br />
|U-start (friction velocity)<br />
|Instantaneous<br />
|-<br />
|pblh<br />
|m<br />
|Height of the PBL boundary layer<br />
|Instantaneous<br />
|}<br />
<br />
== Required Modules/Licenses ==<br />
To access the EMD-WRF South Korea (ERA5) mesoscale data the following licenses/modules are required in your windPRO setup:<br />
* Basis<br />
* METEO<br />
* EMD-WRF South Korea (ERA5)<br />
When the license fee is paid, you then have access to the full dataset without further cost. The price is available from the [http://www.emd.dk/windpro/price-list/data-sets/ price-list on the EMD-homepage]. Downloading of data is unrestricted for licenced users, however, a "fair use" policy applies. Unlicenced users may download three months of data from any point, however, multiple downloads are not allowed from the same point.<br />
<br />
Visit [http://www.emd.dk/windpro/online-ordering/ EMD online ordering] to purchase the needed licences.<br />
<br />
== Acknowledgement == <br />
* Model development and integration of this dataset into EMD services - was co-supported through the WindPROSPER project. The windPROSPER project is co-funded by the Danish Innovation Fund and the EuroStars framework programme.<br />
<br />
== Attribution ==<br />
If data from this dataset is used within any private or public disseminations, then EMD and its data providers must be acknowledged. <br />
<pre><br />
Source: <br />
EMD-WRF South Korea (ERA5) - Copyright (c) - EMD International A/S, 2019. Distribution through EMD and windPRO.<br />
This dataset uses ERA5 which is being developed through the Copernicus Climate Change Service (C3S). <br />
Data processing and distribution for ERA5 is carried out by ECMWF.<br />
</pre><br />
<br />
== External Links == <br />
* WindPROSPER at [https://www.eurostars-eureka.eu/project/id/10037 Eurostars] <br />
* Innovation Fund Denmark - https://innovationsfonden.dk<br />
* ERA5 availability page: : https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=European_EMODnet_Bathymetry&diff=9642
European EMODnet Bathymetry
2019-06-17T08:21:24Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Bathymetry Models]]<br />
[[Image:EMODNetBathymetryCoverage.png|right|thumb|400px|Coverage of the EMODnet Bathymetry DTM]]<br />
[[Image:EMODNetBathymetryAnholt.png|right|thumb|400px|EMODnet Bathymetry data in windPRO - Offshore site near Anholt, Denmark]]<br />
== Introduction ==<br />
For windPRO users working with offshore wind farm development, the "EMODnet Digital Bathymetry (DTM)" is a multilayer bathymetric product for Europe’s sea basins containing water depths. The DTM is based upon more than 7700 bathymetric survey data sets and Composite DTMs that have been gathered from 27 data providers from 18 European countries. The DTM has a grid resolution of 1/8 * 1/8 arc minutes (roughly 230 * 230 meters).<br />
<br />
== Availability from within WindPRO and Usage Notes ==<br />
The data is available directly from within windPRO and can be accessed from the online-services in the Elevation Grid Object. <br><br />
Usage: <br />
* In order to access the data, please set the purpose of the Elevation Grid Object to 'Water depths'.<br />
* If you want to export the grid to contour lines: This can be done from within the grid-object, simply select the layer of interest, then choose 'Export Layer'.<br />
<br />
== Reference System ==<br />
The following reference system was used in the original data:<br />
* Geo [deg,min] - WGS84<br />
<br />
== Coverage ==<br />
* The Greater North Sea, including the Kattegat and stretches of water such as Fair Isle, Cromarty, Forth, Forties, Dover, Wight, and Portland<br />
* The English Channel and Celtic Seas<br />
* Western and Central Mediterranean Sea and Ionian Sea<br />
* Bay of Biscay, Iberian coast and North-East Atlantic<br />
* Adriatic Sea<br />
* Aegean - Levantine Sea (Eastern Mediterranean)<br />
* Azores - Madeira EEZ<br />
* Canary Islands<br />
* Baltic Sea<br />
* Black Sea<br />
* Norwegian – Icelandic seas<br />
<br />
== License and Attribution ==<br />
Please use the following attribution when using this dataset - and also consider and accept the [http://www.emodnet-bathymetry.eu/internal_html/disclaimer/10 disclaimer] from EMODnet.<br />
<pre><br />
EMODnet Bathymetry Consortium (2016): EMODnet Digital Bathymetry (DTM).<br />
http://doi.org/10.12770/c7b53704-999d-4721-b1a3-04ec60c87238. Distribution through windPRO and EMD.<br />
EMODnet Bathymetry Consortium (2018). EMODnet Digital Bathymetry (DTM 2018). <br />
EMODnet Bathymetry Consortium. https://doi.org/10.12770/18ff0d48-b203-4a65-94a9-5fd8b0ec35f6<br />
</pre><br />
<br />
== Acknowledgement ==<br />
The European Marine Observation and Data Network (EMODnet) is thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy.<br />
<br />
== External Links ==<br />
* Description of the EMODnet Bathymetry data set: http://www.emodnet-bathymetry.eu/data-products</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=UK-Wales_Elevation_Model&diff=9624
UK-Wales Elevation Model
2019-06-12T06:58:54Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:UKWales05.png|right|thumb|400px|Coverage for the DTM model for Wales – 5 m grid. Black dots represent existing wind turbines in Wales.]]<br />
== Introduction ==<br />
The UK/Wales Elevation model in 5m grid resolution is a digital terrain model produced from higher resolution LiDAR scans originally obtained from the Natural Resources Wales. The original model at 2m resolution is named ‘LiDAR Composite Dataset – DTM – version 2m.’ The derived elevation model delivered with windPRO is at 5m resolution – and is bi-linearly resampled by EMD to obtain a slightly coarser resolution - 5m – this is done in order for the data to be operational for the much larger areas needed in the context of wind farm analysis’. Coverage for the data is approximately 70% of Wales. <br />
<br />
== Usage Notes ==<br />
Higher resolution data exists for selected areas in the region based on more recent LiDAR surveys: These are in resolutions from 0.25m to 2m – and may be accessed directly from the geodata-portal of Wales (see link below).<br />
More information about the Wales Lidar products can be found in the [[:File:LiDAR_Guidance_2018.pdf|LiDAR Guidance]].<br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO in 5-meter grid resolution. The data can be accessed from the online-services in the following objects: <br />
* Line Object (with purpose to height contour lines) <br />
* Elevation Grid Object <br />
<br />
== Reference System ==<br />
The following reference systems were used in the original data:<br />
* Horizontal: OSGB36 British National Grid (EPSG: 27700)<br />
* Vertical: Elevations recorded above Ordnance Datum Newlyn<br />
<br />
== License and Attribution ==<br />
The product belongs to the open geospatial data of the Natural Resources Wales. The data are license under a under a ‘Open Government License for Public Sector Information’. This license is compatible with the Creative Commons Attribution License 4.0. Please use the following attribution when using this dataset: <br />
<br />
Contains Natural Resources Wales information ©. Natural Resources Wales and database right. Adapted and distributed by EMD and windPRO.<br />
<br />
== Acknowledgements==<br />
* The Welsh Government, Natural Resources Wales and the public of Wales are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External Links ==<br />
* Geodata from the Wales: http://lle.gov.wales/home<br />
* InnoWind project - http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=UK-Wales_Elevation_Model&diff=9623
UK-Wales Elevation Model
2019-06-12T06:57:28Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:UKWales05.png|right|thumb|400px|Coverage for the DTM model for Wales – 5 m grid. Black dots represent existing wind turbines in Wales.]]<br />
== Introduction ==<br />
The UK/Wales Elevation model in 5m grid resolution is a digital terrain model produced from higher resolution LiDAR scans originally obtained from the Natural Resources Wales. The original model at 2m resolution is named ‘LiDAR Composite Dataset – DTM – version 2m.’ The derived elevation model delivered with windPRO is at 5m resolution – and is bi-linearly resampled by EMD to obtain a slightly coarser resolution - 5m – this is done in order for the data to be operational for the much larger areas needed in the context of wind farm analysis’. Coverage for the data is approximately 70% of Wales. <br />
<br />
== Usage Notes ==<br />
Higher resolution data exists for selected areas in the region based on more recent LiDAR surveys: These are in resolutions from 0.25m to 2m – and may be accessed directly from the geodata-portal of Wales (see link below).<br />
More information about the Wales Lidar products can be found in the [[http://help.emd.dk/mediawiki/images/f/f6/LiDAR_Guidance_2018.pdf|LiDAR Guidance]].<br />
<br />
<br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO in 5-meter grid resolution. The data can be accessed from the online-services in the following objects: <br />
* Line Object (with purpose to height contour lines) <br />
* Elevation Grid Object <br />
<br />
== Reference System ==<br />
The following reference systems were used in the original data:<br />
* Horizontal: OSGB36 British National Grid (EPSG: 27700)<br />
* Vertical: Elevations recorded above Ordnance Datum Newlyn<br />
<br />
== License and Attribution ==<br />
The product belongs to the open geospatial data of the Natural Resources Wales. The data are license under a under a ‘Open Government License for Public Sector Information’. This license is compatible with the Creative Commons Attribution License 4.0. Please use the following attribution when using this dataset: <br />
<br />
Contains Natural Resources Wales information ©. Natural Resources Wales and database right. Adapted and distributed by EMD and windPRO.<br />
<br />
== Acknowledgements==<br />
* The Welsh Government, Natural Resources Wales and the public of Wales are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External Links ==<br />
* Geodata from the Wales: http://lle.gov.wales/home<br />
* InnoWind project - http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=UK-Wales_Elevation_Model&diff=9622
UK-Wales Elevation Model
2019-06-12T06:57:01Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:UKWales05.png|right|thumb|400px|Coverage for the DTM model for Wales – 5 m grid. Black dots represent existing wind turbines in Wales.]]<br />
== Introduction ==<br />
The UK/Wales Elevation model in 5m grid resolution is a digital terrain model produced from higher resolution LiDAR scans originally obtained from the Natural Resources Wales. The original model at 2m resolution is named ‘LiDAR Composite Dataset – DTM – version 2m.’ The derived elevation model delivered with windPRO is at 5m resolution – and is bi-linearly resampled by EMD to obtain a slightly coarser resolution - 5m – this is done in order for the data to be operational for the much larger areas needed in the context of wind farm analysis’. Coverage for the data is approximately 70% of Wales. <br />
<br />
== Usage Notes ==<br />
Higher resolution data exists for selected areas in the region based on more recent LiDAR surveys: These are in resolutions from 0.25m to 2m – and may be accessed directly from the geodata-portal of Wales (see link below).<br />
More information about the Wales Lidar products can be found in the [[LiDAR_Guidance_2018.pdf|LiDAR Guidance]].<br />
<br />
http://help.emd.dk/mediawiki/images/f/f6/LiDAR_Guidance_2018.pdf<br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO in 5-meter grid resolution. The data can be accessed from the online-services in the following objects: <br />
* Line Object (with purpose to height contour lines) <br />
* Elevation Grid Object <br />
<br />
== Reference System ==<br />
The following reference systems were used in the original data:<br />
* Horizontal: OSGB36 British National Grid (EPSG: 27700)<br />
* Vertical: Elevations recorded above Ordnance Datum Newlyn<br />
<br />
== License and Attribution ==<br />
The product belongs to the open geospatial data of the Natural Resources Wales. The data are license under a under a ‘Open Government License for Public Sector Information’. This license is compatible with the Creative Commons Attribution License 4.0. Please use the following attribution when using this dataset: <br />
<br />
Contains Natural Resources Wales information ©. Natural Resources Wales and database right. Adapted and distributed by EMD and windPRO.<br />
<br />
== Acknowledgements==<br />
* The Welsh Government, Natural Resources Wales and the public of Wales are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External Links ==<br />
* Geodata from the Wales: http://lle.gov.wales/home<br />
* InnoWind project - http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=UK-Wales_Elevation_Model&diff=9621
UK-Wales Elevation Model
2019-06-12T06:56:30Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:UKWales05.png|right|thumb|400px|Coverage for the DTM model for Wales – 5 m grid. Black dots represent existing wind turbines in Wales.]]<br />
== Introduction ==<br />
The UK/Wales Elevation model in 5m grid resolution is a digital terrain model produced from higher resolution LiDAR scans originally obtained from the Natural Resources Wales. The original model at 2m resolution is named ‘LiDAR Composite Dataset – DTM – version 2m.’ The derived elevation model delivered with windPRO is at 5m resolution – and is bi-linearly resampled by EMD to obtain a slightly coarser resolution - 5m – this is done in order for the data to be operational for the much larger areas needed in the context of wind farm analysis’. Coverage for the data is approximately 70% of Wales. <br />
<br />
== Usage Notes ==<br />
Higher resolution data exists for selected areas in the region based on more recent LiDAR surveys: These are in resolutions from 0.25m to 2m – and may be accessed directly from the geodata-portal of Wales (see link below).<br />
More information about the Wales Lidar products can be found in the [[:File:LiDAR_Guidance_2018.pdf|LiDAR Guidance]].<br />
<br />
http://help.emd.dk/mediawiki/images/f/f6/LiDAR_Guidance_2018.pdf<br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO in 5-meter grid resolution. The data can be accessed from the online-services in the following objects: <br />
* Line Object (with purpose to height contour lines) <br />
* Elevation Grid Object <br />
<br />
== Reference System ==<br />
The following reference systems were used in the original data:<br />
* Horizontal: OSGB36 British National Grid (EPSG: 27700)<br />
* Vertical: Elevations recorded above Ordnance Datum Newlyn<br />
<br />
== License and Attribution ==<br />
The product belongs to the open geospatial data of the Natural Resources Wales. The data are license under a under a ‘Open Government License for Public Sector Information’. This license is compatible with the Creative Commons Attribution License 4.0. Please use the following attribution when using this dataset: <br />
<br />
Contains Natural Resources Wales information ©. Natural Resources Wales and database right. Adapted and distributed by EMD and windPRO.<br />
<br />
== Acknowledgements==<br />
* The Welsh Government, Natural Resources Wales and the public of Wales are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External Links ==<br />
* Geodata from the Wales: http://lle.gov.wales/home<br />
* InnoWind project - http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:LiDAR_Guidance_2018.pdf&diff=9620
File:LiDAR Guidance 2018.pdf
2019-06-12T06:54:53Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=UK-Wales_Elevation_Model&diff=9619
UK-Wales Elevation Model
2019-06-12T06:54:13Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:UKWales05.png|right|thumb|400px|Coverage for the DTM model for Wales – 5 m grid. Black dots represent existing wind turbines in Wales.]]<br />
== Introduction ==<br />
The UK/Wales Elevation model in 5m grid resolution is a digital terrain model produced from higher resolution LiDAR scans originally obtained from the Natural Resources Wales. The original model at 2m resolution is named ‘LiDAR Composite Dataset – DTM – version 2m.’ The derived elevation model delivered with windPRO is at 5m resolution – and is bi-linearly resampled by EMD to obtain a slightly coarser resolution - 5m – this is done in order for the data to be operational for the much larger areas needed in the context of wind farm analysis’. Coverage for the data is approximately 70% of Wales. <br />
<br />
== Usage Notes ==<br />
Higher resolution data exists for selected areas in the region based on more recent LiDAR surveys: These are in resolutions from 0.25m to 2m – and may be accessed directly from the geodata-portal of Wales (see link below).<br />
More information about the Wales Lidar products can be found in the LiDAR Guidance [pdf].<br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO in 5-meter grid resolution. The data can be accessed from the online-services in the following objects: <br />
* Line Object (with purpose to height contour lines) <br />
* Elevation Grid Object <br />
<br />
== Reference System ==<br />
The following reference systems were used in the original data:<br />
* Horizontal: OSGB36 British National Grid (EPSG: 27700)<br />
* Vertical: Elevations recorded above Ordnance Datum Newlyn<br />
<br />
== License and Attribution ==<br />
The product belongs to the open geospatial data of the Natural Resources Wales. The data are license under a under a ‘Open Government License for Public Sector Information’. This license is compatible with the Creative Commons Attribution License 4.0. Please use the following attribution when using this dataset: <br />
<br />
Contains Natural Resources Wales information ©. Natural Resources Wales and database right. Adapted and distributed by EMD and windPRO.<br />
<br />
== Acknowledgements==<br />
* The Welsh Government, Natural Resources Wales and the public of Wales are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External Links ==<br />
* Geodata from the Wales: http://lle.gov.wales/home<br />
* InnoWind project - http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:UKWales05.png&diff=9618
File:UKWales05.png
2019-06-12T06:53:31Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Luxembourg_Elevation_Model&diff=9590
Luxembourg Elevation Model
2019-06-03T05:24:26Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:LUX05.jpg|right|thumb|350px|Coverage of the Luxembourgish Elevation Model]]<br />
<br />
<br />
== Introduction ==<br />
The Luxembourgish elevation model in 5m grid resolutions is a digital terrain model produced by the Luxembourgish government through its ‘Administration du cadastre et de la topographie’. The model is named BD-L-MNT5. Spatial coverage is for the national territory of Grand Duchy of Luxembourg. The data available in windPRO was accessed during early 2019.<br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO in 5-meter resolution. The data can be accessed from the online-services in the following objects: <br />
* Line Object (with purpose to height contour lines) <br />
* Elevation Grid Object <br />
<br />
== Reference Coordinate System ==<br />
Raw data was delivered as geotiff files with the following data:<br />
* Projection: Luxembourg 1930 / Gauss (EPSG:2169)<br />
* Vertical reference system: Luxembourg (NG95) system - referring to the tide gauge in Amsterdam (Normal Null - Pegel Amsterdam)<br />
<br />
Accuracy of the model is stated to be in the order of 1 to several meters.<br />
<br />
== License and Attribution ==<br />
The product belongs to the open data of the Grand Duchy of Luxembourg through its administration of cadastre and topography (l'administration du cadastre et de la topographie, ACT). The data have been released through the Creative Commons Zero (CC0) license. While the CC0 license does not require attribution, EMD recommends to use one – such as the following:<br />
<br />
Contains elevation data from the administration of cadastre and topography in Luxembourg (ACT). Distribution through EMD and windPRO. <br />
<br />
== Acknowledgements ==<br />
* The Luxembourgish government through its ‘Administration du cadastre et de la topographie’ are thanked for producing this digital elevation dataset – and disseminating it in the public domain - and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External Links ==<br />
* Administration du cadastre et de la topographie, Luxembourg: https://act.public.lu/fr/index.html <br />
* InnoWind project: http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:LUX05.jpg&diff=9589
File:LUX05.jpg
2019-06-03T05:21:27Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Italy-Tuscany_Elevation_Model&diff=9588
Italy-Tuscany Elevation Model
2019-06-03T05:19:19Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:ITA10_Tuscany.png|right|thumb|350px|Coverage for the DTM model for Tuscany – 10 m grid.]]<br />
<br />
<br />
== Introduction ==<br />
The Italy/Tuscany 10m elevation model is a digital terrain model produced by the Tuscany Region through its Direzione Generale Governo del Territorio. The elevation data is in a 10m x 10m grid and have a spatial coverage for the whole of Tuscany. The model was produced from base maps in scale 1:10000 in years 1993-1998.<br />
<br />
== Usage Notes ==<br />
Higher resolution data exists for selected areas in the region based on more recent LiDAR surveys: These 1m and 2 m gridded data may be accessed directly from the geodata-portal of Tuscany: http://www502.regione.toscana.it/geoscopio/cartoteca.html <br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO in 10-meter resolution. The data can be accessed from the online-services in the following objects: <br />
* Line Object (with purpose to height contour lines) <br />
* Elevation Grid Object <br />
<br />
== Reference System ==<br />
The following reference systems were used in the original data:<br />
* Gauss-Boaga Fuso Ovest (EPSG:3003)<br />
<br />
== License and Attribution ==<br />
The product belongs to the open data of the region of Tuscany. The data are license under a under a Creative Commons Attribution 4.0 International License. Please use the following attribution when using this dataset: <br />
<br />
Contains elevation data from the Region of Tuscany 05/2019. Distribution through EMD and windPRO.<br />
<br />
== Acknowledgements ==<br />
* The Region of Tuscany and its Direzione Generale Governo del Territorio are thanked for producing this digital elevation dataset – and disseminating it in the public domain and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External Links ==<br />
<br />
* Geodata from the Tuscany Geoportal: http://www502.regione.toscana.it/geoscopio/cartoteca.html<br />
* InnoWind project - http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:ITA10_Tuscany.png&diff=9587
File:ITA10 Tuscany.png
2019-06-03T05:16:53Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Estonian_Canopy_Heights&diff=9504
Estonian Canopy Heights
2019-05-27T06:41:58Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:ESTForest.png|right|thumb|350px|Example of Estonian Canopy Heights 2012-2015 – Wind Farm at Southern part of Saaramaa. Canopy height data: Estonian Land Board 2012-2017.]]<br />
<br />
== Introduction ==<br />
The Estonian Land Board is maintaining maps of canopy heights with Estonian National coverage. The maps are developed using LiDAR scans of Estonia – where 1/4 of the country is being scanned each year. The map of forest heights in windPRO is – as such – based on measurements from multiple years 2012-2015. The Estonian forest heights in windPRO comes with a 10m grid resolution. <br />
<br />
== Data Applicability and Availability within windPRO ==<br />
The forest height data are used as input for the dedicated sub-models in windPRO which takes into account the forest impact on the wind flow. This is the displacement height calculator and the Objective Roughness Approach (ORA) tool. Both forest models are available from windPRO 3.2+. <br />
<br />
The forest data is accessed from the online-services from the ‘elevation grid object’ with data-type set to ‘Heights above terrain (a.g.l) for elements’.<br />
<br />
== Technical Details (EMD processed data) ==<br />
Dataformat: <br />
GeoTiff<br />
<br />
Coordinate System:<br />
Estonian Coordinate System of 1997 (EPSG: 3301)<br />
<br />
Pixel values:<br />
Canopy height 2012, 2013, 2014 or 2015 (m)<br />
-32767 is a null value: the pixel does not belong to forestry land or canopy height is outside boundaries set (0.5m to 50m)<br />
<br />
Spatial Resolution:<br />
10m<br />
<br />
For the data distributed in windPRO, the null values and missing data are neglected.<br />
<br />
== License ==<br />
The product belongs to the open data of the Estonian Land Board. The data have been licensed under a ‘License of open data by Estonian Land Board’ – dated 2018.07.01 – or at this pdf [PDF-LINK]. Please accept the license conditions and use a proper attribution when using this dataset, such as: <br />
<br />
Contains: Canopy height data: Estonian Land Board 2012-2017. Distribution through EMD and windPRO. <br />
<br />
== Attribution ==<br />
If data are derived from this dataset and is used within a published product the following notification should be given: <br />
<br />
Source: Contains: Canopy height data: Estonian Land Board 2012-2017. Distribution through EMD and windPRO. <br />
<br />
== Acknowledgements ==<br />
* The Estonian Land Board and the pubic of Estonia are thanked for producing this digital elevation dataset – and disseminating it in the public domain - and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External References ==<br />
https://geoportaal.maaamet.ee<br />
<br />
InnoWind project:http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:ESTForest.png&diff=9503
File:ESTForest.png
2019-05-27T06:39:32Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=Estonian_Elevation_Models&diff=9502
Estonian Elevation Models
2019-05-27T06:28:32Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:EST05.png|right|thumb|350px|Coverage of Estonian DTM]]<br />
== Introduction ==<br />
The Estonian elevation models in 5m and 25m grid resolutions are digital terrain models produced by the Republic of Estonia Land Board. Spatial coverage is for the whole of Estonia. The model is created from LIDAR surveys – where 1/4 of Estonian territory is laser-scanned every year. The data available in windPRO was procured during early 2019. <br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO in 5 or 25-meter resolution. The data can be accessed from the online-services in the following objects: <br />
* Line Object (with purpose to height contour lines) <br />
* Elevation Grid Object <br />
<br />
== Reference Systems ==<br />
The following reference systems were used in the source data:<br />
* Estonian Coordinate System of 1997 (EPSG: 3301)<br />
* Vertical: Vertical datum used is EH2000 height system (EVRS - European Vertical Reference System) - based on NAP (Normaal Amsterdam Peil).<br />
<br />
== License and Attribution ==<br />
The product belongs to the open data of the Estonian Land Board. The data have been licensed under a ‘License of open data by Estonian Land Board’ – dated 2018.07.01 – or at this pdf [PDF-LINK]. <br />
<br />
Please accept the license conditions and use a proper attribution when using this dataset, such as: <br />
Contains: Elevation data: Estonian Land Board 2012-2017. Distribution through EMD and windPRO. <br />
<br />
== Acknowledgements ==<br />
* The Estonian Land Board and the pubic of Estonia are thanked for producing this digital elevation dataset – and disseminating it in the public domain - and thus for aiding the development of renewable energy.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund<br />
<br />
== External Links ==<br />
Description of the elevation models: <br />
https://geoportaal.maaamet.ee/eng/Maps-and-Data/Topographic-Data/Elevation-data-p308.html <br />
<br />
InnoWind project:<br />
http://www.innowind.dk</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=File:EST05.png&diff=9501
File:EST05.png
2019-05-27T06:26:00Z
<p>Ronnie: </p>
<hr />
<div></div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=French_Elevation_Models&diff=9500
French Elevation Models
2019-05-27T06:01:36Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:FRA.png|right|thumb|350px|French National Elevation Model - 75m grid. Image showing part of Bretagne.]]<br />
== Introduction ==<br />
A number of French digital terrain models DTM’s (MNT - Modèle Numérique de Terrain) are available with windPRO – one covering the whole of France in 75m grid resolution – and others from selected administrative regions in higher resolutions, typically 5m grid or 10m grid. The French DTM datasets are currently freely available for windPRO users with an active subscription. Currently the following French elevation models are available: <br />
<br />
1. National DTM/MNT Model – 75m:<br/><br />
This model is a medium scale (75m grid resolution) version of the national elevation model of France (BD Alti). The model is released from the Institut National de l’Information Geographique et Forestiére (IGN). It is based on digitization of maps and stereo-imagery with data from years 1987-2001. Higher resolution models can be purchased from the IGN. Read more on BD Alti (in French) [PDF-LINK].<br />
<br />
2. Auvergne-Rhône-Alpes DTM/MNT Model – 5m: <br/><br />
Elevation from five departments are available: Puy-de-Dôme, Cantal, Loire, Haute-Loire and Allier. Grid resolution is 5m with data from 2016.<br />
<br />
3. Bretagne DTM/MNT Model – 5m:<br/><br />
Elevation model in 5m resolution – with data obtained in the following years: Morbihan (2016), lle et Vilaine (2017), Côtes d'Armor (2015), Finistère (2015).<br />
<br />
4. Nord-Pas de Calais DTM/MNT Model – 10m:<br/><br />
Model was derived from stereo-photos during years 2012-2013.<br />
<br />
== Version ==<br />
Data in windPRO were accessed, downloaded and prepared/converted for windPRO distribution during spring 2019.<br />
<br />
== Coordinate Reference Systems ==<br />
Horizontal: RGF93 / Lambert-93 (EPSG:2154)<br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO and may be accessed from the online-services from the following objects:<br />
* Line Object (with purpose to height contour lines)<br />
* Elevation Grid Object<br />
<br />
== Attributions ==<br />
These products belong to the open data of France. Please use the following attributions when using one of the datasets:<br />
<br />
French National DTM/MNT Model – 75m:<br />
Contains elevation data from the Institut National de l’Information Geographique et Forestiére (IGN) – 05/2019. Distribution through EMD and windPRO.<br />
<br />
France - Auvergne-Rhône-Alpes DTM/MNT Model – 5m:<br />
Contains elevation data from the Centre Régional Auvergne-Rhône-Alpes de l'Information Géographique (CRAIG) – 05/2019. Distribution through EMD and windPRO.<br />
<br />
France - Bretagne DTM/MNT Model – 5m:<br />
Contains elevation data from GéoBretagne – 05/2019. Distribution through EMD and windPRO.<br />
<br />
France – Nord-Pas de Calais – 10m:<br />
Contains elevation data from the Plateforme Publique de l'Information Géographique de la Région Nord - Pas de Calais (PPIGE) – 05/2019. Distribution through EMD and windPRO.<br />
<br />
== Acknowledgements == <br />
* The French public, state (with IGN as its representative) and regions of Auvergne-Rhône-Alpes, Bretagne and Nord-Pas de Calais are thanked for producing these digital elevation datasets and disseminating them with an open-data licence - and thus aiding the development of renewable energy – and wind energy in particular.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund.<br />
<br />
==License==<br />
Unless otherwise stated, these data have been released under the French License Ouverte / Open License – a license used for open data from the State of France. The license is designed to be compatible to be compatible with Creative Commons Licenses, Open Government License (UK) and the Open Data Commons Attribution License [read here, link to pdf]. It is required that the source of information is mentioned when using these data – see above.<br />
<br />
== External Links ==<br />
Institut National de l’Information Geographique et Forestiére (IGN)<br />
http://professionnels.ign.fr <br />
<br />
Centre Régional Auvergne-Rhône-Alpes de l'Information Géographique (CRAIG):<br />
https://www.craig.fr/<br />
<br />
Le partenariat GéoBretagne<br />
https://cms.geobretagne.fr <br />
<br />
Equipe PPIGE Plateforme Publique de l'Information Géographique - Nord-Pas de Calais<br />
https://www.ppige-npdc.fr/portail/geocatalogue</div>
Ronnie
http://help.emd.dk/mediawiki/index.php?title=French_Elevation_Models&diff=9499
French Elevation Models
2019-05-27T05:59:16Z
<p>Ronnie: </p>
<hr />
<div>[[Category: Online Data]][[Category: Digital Elevation Models]][[Category: InnoWind]]<br />
[[Image:FRA.png|right|thumb|350px|French National Elevation Model - 75m grid. Image showing part of Bretagne.]]<br />
== Introduction ==<br />
A number of French digital terrain models DTM’s (MNT - Modèle Numérique de Terrain) are available with windPRO – one covering the whole of France in 75m grid resolution – and others from selected administrative regions in higher resolutions, typically 5m grid or 10m grid. The French DTM datasets are currently freely available for windPRO users with an active subscription. Currently the following French elevation models are available: <br />
<br />
1. National DTM/MNT Model – 75m<br />
This model is a medium scale (75m grid resolution) version of the national elevation model of France (BD Alti). The model is released from the Institut National de l’Information Geographique et Forestiére (IGN). It is based on digitization of maps and stereo-imagery with data from years 1987-2001. Higher resolution models can be purchased from the IGN. Read more on BD Alti (in French) [PDF-LINK].<br />
<br />
2. Auvergne-Rhône-Alpes DTM/MNT Model – 5m <br />
Elevation from five departments are available: Puy-de-Dôme, Cantal, Loire, Haute-Loire and Allier. Grid resolution is 5m with data from 2016.<br />
<br />
3. Bretagne DTM/MNT Model – 5m:<br />
Elevation model in 5m resolution – with data obtained in the following years: Morbihan (2016), lle et Vilaine (2017), Côtes d'Armor (2015), Finistère (2015).<br />
<br />
4. Nord-Pas de Calais DTM/MNT Model – 10m: <br />
Model was derived from stereo-photos during years 2012-2013.<br />
<br />
== Version ==<br />
Data in windPRO were accessed, downloaded and prepared/converted for windPRO distribution during spring 2019.<br />
<br />
== Coordinate Reference Systems ==<br />
Horizontal: RGF93 / Lambert-93 (EPSG:2154)<br />
<br />
== Availability from within windPRO ==<br />
The data are available directly from within windPRO and may be accessed from the online-services from the following objects:<br />
* Line Object (with purpose to height contour lines)<br />
* Elevation Grid Object<br />
<br />
== Attributions ==<br />
These products belong to the open data of France. Please use the following attributions when using one of the datasets:<br />
<br />
French National DTM/MNT Model – 75m:<br />
Contains elevation data from the Institut National de l’Information Geographique et Forestiére (IGN) – 05/2019. Distribution through EMD and windPRO.<br />
<br />
France - Auvergne-Rhône-Alpes DTM/MNT Model – 5m:<br />
Contains elevation data from the Centre Régional Auvergne-Rhône-Alpes de l'Information Géographique (CRAIG) – 05/2019. Distribution through EMD and windPRO.<br />
<br />
France - Bretagne DTM/MNT Model – 5m:<br />
Contains elevation data from GéoBretagne – 05/2019. Distribution through EMD and windPRO.<br />
<br />
France – Nord-Pas de Calais – 10m:<br />
Contains elevation data from the Plateforme Publique de l'Information Géographique de la Région Nord - Pas de Calais (PPIGE) – 05/2019. Distribution through EMD and windPRO.<br />
<br />
== Acknowledgements == <br />
* The French public, state (with IGN as its representative) and regions of Auvergne-Rhône-Alpes, Bretagne and Nord-Pas de Calais are thanked for producing these digital elevation datasets and disseminating them with an open-data licence - and thus aiding the development of renewable energy – and wind energy in particular.<br />
* Integration of this dataset into EMD services - was co-supported through the InnoWind project (www.innowind.dk) - which is co-funded by the Danish Innovation Fund.<br />
<br />
==License==<br />
Unless otherwise stated, these data have been released under the French License Ouverte / Open License – a license used for open data from the State of France. The license is designed to be compatible to be compatible with Creative Commons Licenses, Open Government License (UK) and the Open Data Commons Attribution License [read here, link to pdf]. It is required that the source of information is mentioned when using these data – see above.<br />
<br />
== External Links ==<br />
Institut National de l’Information Geographique et Forestiére (IGN)<br />
http://professionnels.ign.fr <br />
Centre Régional Auvergne-Rhône-Alpes de l'Information Géographique (CRAIG):<br />
https://www.craig.fr/<br />
Le partenariat GéoBretagne<br />
https://cms.geobretagne.fr <br />
Equipe PPIGE Plateforme Publique de l'Information Géographique - Nord-Pas de Calais<br />
https://www.ppige-npdc.fr/portail/geocatalogue</div>
Ronnie