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CERRA is based on the HARMONIE-ALADIN data assimilation system, which for CERRA uses a 3D variational analysis (3D-VAR). Assimilation is run with 8 cycles per day at 00, 03, 06, 09, 12, 15, 18 and 21 UTC. While quite many of the mesoscale datasets used in wind energy industry are developed using the excellent WRF model, here the numerical weather prediction model used is HARMONIE–ALADIN. As such, the CERRA dataset provides a welcome and alternative modelling chain to what is commonly used for wind-energy modelling - providing valuable input for uncertainty modelling and second-opinion analyses. The HARMONIE-ALADIN model is developed by the ACCORD consortium - and used by many meteorological institutes in Europe. | CERRA is based on the HARMONIE-ALADIN data assimilation system, which for CERRA uses a 3D variational analysis (3D-VAR). Assimilation is run with 8 cycles per day at 00, 03, 06, 09, 12, 15, 18 and 21 UTC. While quite many of the mesoscale datasets used in wind energy industry are developed using the excellent WRF model, here the numerical weather prediction model used is HARMONIE–ALADIN. As such, the CERRA dataset provides a welcome and alternative modelling chain to what is commonly used for wind-energy modelling - providing valuable input for uncertainty modelling and second-opinion analyses. The HARMONIE-ALADIN model is developed by the ACCORD consortium - and used by many meteorological institutes in Europe. | ||
==Dataset Overview== | |||
* '''Content:''' Limited feature and spatial-domain subset of the CERRA mesoscale dataset <br>(we provide about 973.000 nodes in a distance of approx. 300km from the coastlines). | * '''Content:''' Limited feature and spatial-domain subset of the CERRA mesoscale dataset <br>(we provide about 973.000 nodes in a distance of approx. 300km from the coastlines). | ||
* '''Spatial resolution:''' 5.5km | * '''Spatial resolution:''' 5.5km |
Revision as of 13:16, 27 December 2022
NOTE 2022-02-27: This dataset is currently being integrated in windPRO.
Introduction
The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis (RRA) for a pan-European domain. It is being provided by the European Copernicus programme, though a contract with the Swedish Meteorological and Hydrological Institute (SMHI) and with Meteo-France and the Norwegian Meteorological Institute as subcontractors. The model holds data at 5.5km resolution. CERRA is downscaling to is 5.5km resolution with initial conditions and boundary conditions provided from ERA5 reanalysis data. The CERRA dataset is making extensive use of remote sensing data as well as local observations, sea-ice records and sea-surface temperature fields.
CERRA is based on the HARMONIE-ALADIN data assimilation system, which for CERRA uses a 3D variational analysis (3D-VAR). Assimilation is run with 8 cycles per day at 00, 03, 06, 09, 12, 15, 18 and 21 UTC. While quite many of the mesoscale datasets used in wind energy industry are developed using the excellent WRF model, here the numerical weather prediction model used is HARMONIE–ALADIN. As such, the CERRA dataset provides a welcome and alternative modelling chain to what is commonly used for wind-energy modelling - providing valuable input for uncertainty modelling and second-opinion analyses. The HARMONIE-ALADIN model is developed by the ACCORD consortium - and used by many meteorological institutes in Europe.
Dataset Overview
- Content: Limited feature and spatial-domain subset of the CERRA mesoscale dataset
(we provide about 973.000 nodes in a distance of approx. 300km from the coastlines). - Spatial resolution: 5.5km
- Temporal resolution: Timestep 1 hour
- Coverage: See the map to the right
- Period: In windPRO: 20+ years of data from 1999-01-01 to 2021-12-31
(source data dates back to 1984 from the Copernicus system) - Update schedule: Unknown. The original contract (2017/C3S_322_Lot1_SMHI/SC2) between ECMWF/Copernicus and SMHI initially lasted 4 years and is now terminated.
We expect updated data in late 2023, and will update when availble when more recent data becomes available.
Usage Notes
- I am interested in other parameters and model ensembles - where can I get those? The data is freely available from the Copernicus Climate Data Store (C3S) - here.
- How do I find more on model details? See the external references below.
Dataset Parameters
A total of 80 parameters are available from the CERRA mesoscale data-subset in windPRO.
Parameter | Unit | Description | Type |
---|---|---|---|
time | UTC time stamp (YYYY-MM-DD HH:MM) | ||
wSpeed.x | m/s | Wind speeds at different heights above ground (x). Heights (x): 15,50,75,100,150,200,300,500m |
Instantaneous |
wDir.x | deg | Wind directions at different heights above ground (x). Heights (x): 15,50,75,100,150,200,300,500m |
Instantaneous |
sqrtTKE.x | m/s | Wind speed given as standard deviation in m/s. Derived from the turbulent kinetic energy (TKE) at different heights above ground (x). Use with caution, and avoid use for site assessment and loads as variability is underestimated. Heights (x): 15,50,75,100,150,200,300,500m |
Instantaneous |
press.x | Pa | Pressure at different heights above ground (x). Heights (x): 15,50,75,100,150,200,300,500m |
Instantaneous |
temp.x | celcius | Temperature at different heights above ground (x). Heights (x): 15,50,75,100,150,200,300,500m |
Instantaneous |
rh.x | % | Relative humidity at different heights above ground (x). Heights (x): 15,50,75,100,150,200,300,500m |
Instantaneous |
cloudWater.x | mg/kg | Cloud water content at different heights above ground (x). Heights (x): 15,50,75,100,150,200,300,500m |
Instantaneous |
cloudIce.x | mg/kg | Cloud icing content at different heights above ground (x). Heights (x): 15,50,75,100,150,200,300,500m |
Instantaneous |
wSpeed.10 | m/s | Wind speeds at 10m. | Instantaneous |
wDir.10 | deg | Wind direction at 10m. | Instantaneous |
wGust.10 | deg | Wind gust at 10m. | Instantaneous |
temp.2 | celcius | Temperatures at 2m | Instantaneous |
rh.2 | % | Relative humidity at 2m | Instantaneous |
psfc | Pa | Pressure at site | Instantaneous |
msl | Pa | Pressure at mean sea level | Instantaneous |
z0 | m | Rougnhess length | Instantaneous |
totalCloudCover | % | Total cloud cover in atmosphere | 1h Average |
totPrecip | kg/m2 | Total Precipitation at surface | 1h Accumulated |
sensHeatFlux | W/m2 | Sensible Heat Flux at surface | Instantaneous |
momFluxU | N/m2 | Instantaneous northward turbulent surface stress | Instantaneous |
momFluxV | N/m2 | Instantaneous eastward turbulent surface stress | Instantaneous |
zust | m/s | U-star (friction velocity) | Instantaneous |
ssr | W/m2 | Surface net solar radiation | 1h Accumulated |
ssrc | W/m2 | Surface net solar radiation, clear sky | 1h Accumulated |
ssrd | W/m2 | Surface solar radiation downwards | 1h Accumulated |
License and Attribution
If data from this dataset is used within any private or public disseminations, then EMD and its data providers must be acknowledged.
Source: CERRA: The Copernicus Regional Reanalysis for Europe. Distribution through EMD and windPRO - EMD International A/S, 2022. This dataset uses CERRA which is being developed through the Copernicus Climate Change Service (C3S). Data processing for CERRA is carried out by SMHI and distribution by ECMWF.
More details on free and open Copernicus licensing conditions here in this pdf (version 1.2 from November 2019).
Required windPRO License and Modules
This dataset can be accessed from the most recent version of windPRO given that you hold an active service agreement and a license to the following modules:
- BASIS
- METEO
External Links and References
CERRA Data
CERRA Developers
- ACCORD Consortium home-page (developers of HARMONIE-ALADIN model) - here
- Swedish Meteorological and Hydrological Instutute (SMHI) - here
- Meteo France - here
- Norwegeian Meteorological Institute - here
Acknowledgement and Credits
Credits and Acknowledgement: The European Commission, the ECMWF, the Copercius Climate Change Service, SMHI, MeteoFrance and MET-NO are acknowledged for the development and release of the free and open CERRA data. Thanks a lot: this dataset will surely aid the development of renewable energy and help us adressing some of the uncertainties in our modelling of wind-energy.