# Impact of Wind Farms on the Radar Network of Météo-France.

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1 Impact of Wind Farms on the Radar Network of Météo-France. Jean-Louis Champeaux, Head of the ERF division Centre de Météorologie Radar, Météo France, Toulouse

2 Presentation plan Definition and modelling of the problem Impact on Meteo France radar products Mitigation strategies Comparison Qinetiq modelling-mf Observations

3 Definition and modelling of the problem

4 Why are windfarms such a problem for the meteo radar community? The number and the average size of the wind farm developments make that a non negligible part of the best radar data (< 80 km) is contaminated by clutters. Since the average windmill is 100m tall, wind farms situated too close to the radar not only induce clutter but also beam blockages that comprise entire sectors of radar scans. All the known methods of clutter identification and removal fail to remove or reduce the impact of wind farms due the wide, time-varying Doppler spectrum induced by the rotating blades.

5 Windmills returns through side lobes In situations where the main beam of the antenna is kept away from the wind farms, the meteorological data can still be contaminated by spurious returns through the side lobes. An impact model must take into consideration the secondary lobes of the antenna in order to truly represent the effect of wind farms on radar data.

6 Signal processing studies at Météo France Acquisition of «I et Q» values (real and complex value of the received power) from Falaise radar in T/6 =0,6 s The wind farm is situated at around 20km from the radar The acquisition time is close to the rotation period of the blades Maximum values (pseudo «flash») are recorded when one of the blade reach the vertical (Nadir and Zenith) 3 blades 6 occurrences per period (T = 3,6 s T/6 =0,6 s, RPM= 16,6) High signal variance Clutter Indicators (CI, SQI) fail High intensity (50 to 80 dbz in that case) side lobes are contaminated (80dBz-60 db = 20 dbz)

7 Impact of wind farms on radar products

8 Météo-France operational radar products - 2D and 3D reflectivity composites. - rainfall accumulation composite. - Wind shear composite. - Volume reflectivity and Doppler winds for assimilation in meteorological models.

9 False alarms on 24h accumulation products Abbeville h00 UTC Proposed location of the windfarms at the planning stage

10 False alarms on annual accumulation products More than 100% overestimations on wind farms

11 Météo France APIC warning service APIC = Warning of Intense Rain for town halls Fully automatised intense precipitation warnings Reliable QPE products of required to run the service. Warning are sent to customers (Town mayors ) via SMS and when rainfall accumulations reach a defined threshold in their area. Impact of wind farms = spurious reports during dry periods Wind farms related spurious accumulations impacts «éoliens» False alarms in APIC product impacts «éoliens» communes Warnings averties sent 12H accumulations APIC Product

12 Use of radar observations in model data assimilation - Radar observations account for 21 % of the data volume assimilated in AROME, it is second most important data type. - Radar observations carry detailed information on the 3D wind and humidity fields that is of particular importance for the fine scale modelling of the atmosphere. - Both radial wind and reflectivity data are used. Undetect is different from no data as the information held by un detect is used in data assimilation to constrain the model reflectivity towards the minimum detectable reflectivity.

13 Impact of missing data Radar data from the areas affected by wind farms must be blacklisted to avoid contamination of the forecast by spurious observations. But missing data affects : The quality of the forecast when the amount of missing data is large. The quality of the very short range forecast of dangerous weather phenomenon (convergence line, low level jet, gust winds, intense precipitation, tornado, squall line, intense storm, hail etc.), when a localised intense and small scale events develop in the vicinity of a wind farm Therefore, it is essential to limit the extend of data losses by imposing constrains on the localisation and the extension of the wind farms

14 Mitigation strategies

15 Coordination rules ZP : Protected area ZC Coordination area ZEM : Protected site xxxx : Unauthorised alignment yyy: Authorised alignment Beam blockage < 10% Impacted area < 10 km Distance between impacted areas > 10 km Further exclusion areas around sensitive sites

16 Derivation of the lowest usable elevation maps 3 months of 5 raw reflectivity and sigma data is collected at each elevation and for each radar 1st step : identification of pixels free of precipitation and clear air echoes we count the number of PRECIP pixels that verify in each radar scan : Z > 8 dbz sigma > 3 db For each elevation, we build a distribution of wet pixels per scans and we keep the scans that are in the lowest 10% of the distribution that are assumed to be free of precipitation and clear air echoes Step 2 : We build using the selected data a frequency of occurrence maps of residual echoes Step 3 : At each elevation we look at frequency of occurrence at a given pixel and we blacklist the elevation if the frequency is greater than 20%.

17 Occurrence frequencies and mean reflectivity during dry days 17 Occurrence frequencies during dry days Mean reflectivity during dry days

18 Lowest usable elevations map for Abbeville Advantage= reduction of false alarms Drawback = loss of data for DA and degradation of QPE

19 Comparison Qinetiq model- Meteo-France Observations

20 The framework of the impact model validation As shown in the previous presentation of C. Sauron, the impact models must be validated on a wind farm chosen by MEDDE/DGPR. The two wind farms (Longue Epine et Fond de Fresnes) comprise 10 turbines and are located at 17km SW of the Abbeville radar (C band radar). The first evaluated impact model was the model of the Qinetiq company The objectives were to validate the model by comparing simulations and radar observations with the same parameter settings (threshold measurement = 0dBz, definition of clutters,..)

21 21 Comparison Qinetiq model-meteo-france observations The predicted size and the shape of the impact zone simulated by QinetiQ for the Longue Epine and Fond de Fresnes windfarms is in good agreement with the measured data on the Abbeville Météo France weather radar The report will be available shortly

22 THANKS Questions?

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