EMD-WRF On-Demand ICING

Example of a cold-climate wind farm.

Icing Map in windPRO with IEA Loss Percentage AEP as Legend.

Heated cup anemometer at iced mast.

Histograms during meteorological icing (example results from report).

Seasonal variation of modelled instrumental and meteorological icing (example results from report).

NOTE:
This dataset and service is currently being integrated with windPRO 3.6 (beta).
Please contact our technical specialist Marie Cecilie Pedersen (mcp@emd.dk) for more information and schedule.
We will be attending winterwind 2022 in Skellefteå, Sweden, so you are also welcome to meet with us at that event.

Introduction

EMD-WRF OD ICING is the name of EMD’s icing model. It is available as a time-series product, similar to the well known EMD-WRF OD service. The model is fully validated: A technical note with results from from recent validation study on EMD-WRF OD ICING will be available on request - and after the WinterWind 2022 and IWAIS 2022 conferences in spring 2022.

The EMD-WRF OD ICING model is configured with the following setup:

• Driven by an icing configuration of the standard EMD WRF [1] On-Demand service [2].
• Run with a spatial resolution of 3x3 km and an hourly temporal resolution.
• Using the ERA-5 reanalysis data from ECMWF as global boundary data [3], see Table 2 below.
• Microphysics from Thompson scheme is used for parameterization of the cloud physics and the MYJ scheme for the planetary boundary layer physics [4], [5].
• The median volume diameter (MVD) by [6] is used, with a constant droplet concentration (Nc) of (default) 100 cm-3 and the liquid water content (LWC) in kg/m3 [7].
• Atmospheric data feeds into the standard cylinder-based model [8], [9] including melting and shedding [10].
• The WRF grid point (latitude, longitude) closest to the at mast location or site location is used as a default.
• The grid point holds a certain elevation above sea level and icing is modelled as a default for 15 heights in the vertical direction above ground level (agl.).
• The modelled ice load (kg) is used to identify hours of instrumental icing based on the industry standard thresholds of 10 g [11]. And similar from the modelled ice accretion rate (g/h), hours of meteorological icing [12] is found using the threshold of 10 g/h [9].

The final step of EMD’s modelling chain, is an estimate of the expected production loss of a site which is found by using the IEA Ice Classification system seen in Table 1 below. A wide range of climate parameters will be availabe form a model-run; the complete list of parameters are seen in the Table 2 further below.

Table 1: IEA Task 19 Ice Classes: Production Loss Estimate as a Percentage of the Annual Energy Production. From [12].

IEA Ice-Class	Meteorological Icing (% of year)	Instrumental Icing (% of year)	Production loss (% of AEP)
5	> 10.0	> 20.0	> 20.0
4	5.0 - 10.0	10.0 - 30.0	10.0 - 25.0
3	3.0 - 5.0	6.0 - 15.0	3.0 - 12.0
2	0.5 - 3.0	1.0 - 9.0	0.5 - 5.0
1	0.0 - 0.5	< 1.5	0.0 - 0.5

What you get and how to order?

To get pointwise-timeseries data, you need meso-credits (one credit is worth one month of data). Credits can be ordered here: http://www.emd.dk/windpro/online-ordering/

Please note, that a time period of 10 years will include the complete icing analysis, whereas shorter time periods include only raw timeseries. Complete icing analysis includes:

• Icing reports as pdfs at three hub-heights - 100m, 150m and 200m
  • Including predicted AEP loss
• Icing maps at three hub-heights - 100m, 150m and 200m
  • IEA ice class, IEA Ice loss (% AEP) and modelled meteorological icing (icing rate > 10 g/h)
• The possibility to use your icing results and timeseries directly in your windPRO project
• Monthly, yearly and seasonal icing analysis and bin-sector analysis as csv-files
• Timeseries of raw WRF data and modelled icing

Data Availability

All EMD-WRF OD ICING is available with global spatial coverage. The temporal availability and update frequency depends on a number of factors such as availability from the boundary data providers, bandwidth and download times, as well as availability on EMD high-performance computing and storage systems. EMD-WRF OD ICING is availability with the ERA5 only, see table below.

Table 2: Data Source for the EMD-WRF OD Icing Configuration

Dataset	First date	Most recent date
EMD-WRF OD (ERA5)	1999.01.01	2-3 months from present day

Set of Standard Dataset Parameters for EMD-WRF OD ICING

A large quantity of useful parameters are available directly in WindPRO to aid in your analysis. The different parameters in each On-Demand ICING dataset is shown in the list below.
Unless other specification, x is the vertical heights of: 10m, 25m, 50m, 75m, 100m, 150m, 200m, 300m, 400m, 500m, 600m, 1000m.

Table 3: EMD-WRF On-Demand ICING dataset. Including raw WRF data and modelled icing parameters; iceInten.x, MIce.x, MeteoSignal.x, InstruSignal.x, MVD.x, LWC.x.

Parameter	Unit	Description	Type
time		UTC time stamp
psfc	Pa	Pressure at site	Instantaneous
msl	Pa	Pressure at mean sea level	Instantaneous
wSpeed.x	m/s	Wind speeds	Instantaneous
wDir.x	deg	Wind direction	Instantaneous
wSpeed.0-30mb	m/s	Wind speeds at pressure level 0-30mb.	Instantaneous
wDir.0-30mb	deg	Wind speeds at pressure levels 0-30mb.	Instantaneous
wSpeed.850hpa	m/s	Wind speeds at pressure level 850hPa.	Instantaneous
wDir.850hpa	deg	Wind speeds at pressure levels 850hPa.	Instantaneous
temperature.2	celcius	Temperatures at height 2m	Instantaneous
waterTemp	celcius	Water temperature	Instantaneous
soilTemp.0-10cm	celcius	The temperature in the upper 10cm of the soil	Instantaneous
relHumidity.2	%	Relative humidity in height 2m above ground level	Instantaneous
snowDepth	m	Snow depth (if present)	Instantaneous
vis.s	m	Visibility at surface	Instantaneous
sensHeatFlux.s	w/m2	Sensible Heat Flux at surface	Instantaneous
totPrecip.s	kg/m^2	Total Precipitation at surface	1h Accumulated
downShortWaveFlux.s	w/m^2	Downward shortwave irradiance at surface	1h Average
totalCloudCover.a	%	Total cloud cover in atmosphere	1h Average
convCloudCover.a	%	Convective cloud cover in atmosphere	1h Average
4LFTX	K	N/A
rmol	1/m	Inverse Monin-Obukhov-Length
znt	m	Rougnhess length
sqrtTKE.x	m/s	Wind speed given as standard deviation in m/s. Derived from the turbulent kinetic energy (TKE)	Instantaneous
cloudWater.x	mg/kg	Cloud water content	Instantaneous
cloudIce.x	mg/kg	Cloud ice content	Instantaneous
press.x	Pa	Pressure	Instantaneous
temperature.x	celcius	Temperatures	Instantaneous
rh.x	%	Relative humidity	Instantaneous
vis.x	m	Visibility	Instantaneous
cloudBottom	m	Distance from ground level to the bottom of cloud	Instantaneous
cloudTop	m	Distance from ground level to the top of cloud	Instantaneous
waterTemp	celcius	Water temperature	Instantaneous
Ice model output parameters.
iceInten.x	g/h	Ice accretion rate (intensity) [8][9]	Instantaneous
MIce.x	kg	Ice load on a standard cylinder [13]	Instantaneous
MeteoSignal.x		Meteorological icing, iceInten.x > 10g/h [9]	Instantaneous
InstruSignal.x		Instrumental Icing, MIce.x > 10g [11]	Instantaneous
MVD.x	m	Median Volume Diameter [6]	Instantaneous
LWC.x	kg/m^3	Liquid Water Content [10]	Instantaneous

References

1. W. C. Skamarock, J. B. Klemp, J. G. D. O. Dudhia, D. M. Barker, W. Wang and J. G. Powers, “A description of the advanced research WRF version 2,” NCAR Technical Note, Boulder, Colorado, USA, 20005.
2. M. L. Thøgersen, “help.emd.dk,” EMD International A/S, 2019. [Online]. Available: https://help.emd.dk/mediawiki/index.php?title=EMD-WRF_On-Demand_and_Custom-Area. [Accessed 30 November 2021].
3. ECMWF, “Advancing global NWP through international collaboration,” ECMWF, [Online]. Available: https://www.ecmwf.int/. [Accessed 23 March 2022].
4. G. Thompson, P. R. Field, R. M. Rasmussen and W. D. Hall, “Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization,” American Meteorological Society, vol. 136, no. Monthly Weather review, pp. 5095-5115, 2008.
5. Z. I. Janjic, “Nonsingular Implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model,” National Centers for Environmental Prediction, Washington, 2001.
6. K. Finstad, E. Lozowski and L. Makkonen, “On the median volume diameter approximation for droplet collision efficiency,” Journal of Atmospheric Sciences, vol. 45, pp. 4008-4012, 1988.
7. G. Thompson, B. E. Nygaard, L. Makkonen and S. Dierer, “Using the Weather Research and Forecasting (WRF) model to predict ground/structural icing,” in International Workshop on Atmospheric Icing on Structures (IWAIS), 2009.
8. L. Makkonen, "Models for the Growth of Rime Glaze Icicles and Wet Snow on Structures," Royal Society, vol. 1776, no. Ice and Snow Accretion on Structures, pp. 2913 - 2939, 2000.
9. ISO, "DS/ISO 12494:2017 Atmospheric icing on structures," Danish Standard Association, København, 2017.
10. K. Harstveit, “Using Metar-data to Calculate In-cloud Icing on a Mountain Site Near by the airport,” in 13th International Workshop on Atmospheric Icing on Structures (IWAIS), Andermat, Switzerland, 2009.
11. K. Hämäläinen and S. Niemelä, “Production of a Numerical Icing Atlas for Finland,” Wind Energy, vol. 20, pp. 171-189, 2017.
12. I. Baring-Gould, R. Cattin, M. Durstewitz, M. Hulkkonen, A. Krenn, T. Laakso, A. Lacroix, E. Peltola, G. Ronsten, L. Tallhaug and T. Wallenius, "13 Wind Energy Projects in Cold Climate 1st edition," IEA Wind Task 19, 2011.
13. L. Makkonen, "Modelling of Ice Accretion on Wires," Climate Appl. Meteor., vol. 23, pp. 929-939, 1984.