Validation n 2 of the Wind Data Generator (WDG) software performance. Comparison with measured mast data - Flat site in Northern France

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1 Validation n 2 of the Wind Data Generator (WDG) software performance Comparison with measured mast data - Flat site in Northern France Mr. Tristan Fabre* La Compagnie du Vent, GDF-SUEZ, Montpellier, 34967, France Mr. Karim Fahssis**, Mr. Sushant Kumar, Mr. Malleswara Rao MeteoPole Renewable Energy Pvt. Ltd., Paris, 75018, France Category: Validation of correlation levels for long-term data adjustment to reduce wind resource annual variability uncertainties Key Words: WDG, Mesoscale, WRF, Correlation, MCP, GFS, ERA, MERRA, CFSR, NCEP, Validation Abstract The objective is to validate the performance of the Wind Data Generator software (WDG) by comparing simulated data from WDG (both WRF modeled and Global Reanalysis Data) against mast data measured on a flat site in Northern France.

, Paris, 75018, France Category: Validation of correlation levels for long-term data adjustment to reduce wind resource annual variability uncertainties Key Words: WDG, Mesoscale, WRF, Correlation,

2 Introduction Contremoulins wind farm is operated by La Compagnie du Vent, GDF-SUEZ and is on a relatively flat terrain in North France. La Compagnie du Vent, GDF-SUEZ and MeteoPole worked together on the validation of the performance of a mesoscale wind modeling software (The Wind Data Generator) by comparing the on-site measured wind data against simulated wind data. The Wind Data Generator (WDG) is a software calculating meteorological parameters based on the mesoscale model WRF. WRF is a fully compressible and non-hydrostatic model. Its vertical coordinate is a terrain-following hydrostatic pressure coordinate. The grid staggering is the Arakawa C-grid. The model uses the Runge-Kutta 2nd and 3rd order time integration schemes and 2nd to 6th order advection schemes in both horizontal and vertical directions. It uses a timesplit small step for acoustic and gravity-wave modes. The dynamics conserves scalar variables. La Compagnie du Vent develops and operates wind farms in the region. Due to the acquisition time on site, the met-mast is used to achieve long-term adjustment to the nearest hub height of future wind turbines. We can compare the quality of the correlation obtained against a correlation realized with 10 meters measurements from MétéoFrance stations and data modeled by a mesoscale model. The measurement tower is installed on the site since 14 years with data availability going up to 92.40%. Performing long-term adjustment of on-site measured wind data can be very beneficial to project owners as it enables reducing annual variability uncertainties and so it helps in building more bankable projects with higher financial returns.

wind data against simulated wind data. The Wind Data Generator (WDG) is a software calculating meteorological parameters based on the mesoscale model WRF.

Site Details Site Name Contremoulins Mast Location 49.7316N, 0.

May2013 Analysis period duration 18 months WDG-WRF* outputs (Spatial Resolution) GFS (5.6 km), NCEP (5.6 km), ERA (5.

3 Site Details Site Name Contremoulins Mast Location N, E Mast Elevation 111m Orography Flat Roughness Grassland and low scrubs Data Summary Figure 1 Site Satellite View Analysis period Dec 2011 May2013 Analysis period duration 18 months WDG-WRF* outputs (Spatial Resolution) GFS (5.6 km), NCEP (5.6 km), ERA (5.6 km) WDG-GDR* outputs (Temporal Resolution) MERRA (hourly), ERA-interim (6-hourly), CFSR (hourly) *WRF: Weather Research Forecast Model (automatic WRF computations with the WDG software) **GDR: Global Data Retrieval (automatic access to reanalysis data with the WDG software)

4 Selection of the WDG output Automatic WRF computations are run with the WDG software for 3 different input scenarios (GFS, NCEP and ERA data). Time series data from different reanalysis products MERRA (hourly), ERA-interim (6-hourly) and CFSR (hourly) are automatically retrieved from the Global Data Retrieval feature of the WDG. As ERA-interim has 6-hour temporal resolution, it has been decided to consider only the data sets which have hourly frequency to make the comparison with on-site measurement data consistent. After doing the quality test of resulting dataset, mean wind speed and correlation coefficient of different datasets are calculated and compared with mast data for a concurrent period of 6 months (Dec May 2012). The table below summarizes it as follows. WDG outputs WRF*-GFS WRF*-NCEP WRF*-ERA GDR**- MERRA GDR**- CFSR Correlation Coefficient (R²) with mast data for N/A concurrent period one year *WRF: Weather Research Forecast Model (automatic WRF computations with the WDG software) **GDR: Global Data Retrieval (automatic access to reanalysis data with the WDG software) The experiment output shows that GDR-MERRA is the WDG output with the highest correlation level with concurrent mast data. It is therefore decided to use this WDG output as reference data set for MCP (Measure-Correlate-Predict) process. MCP calculation is then performed to generate wind data for the next 6 months period and we validate the post-processed data with the mast observation data.

As ERA-interim has 6-hour temporal resolution, it has been decided to consider only the data sets which have hourly frequency to make the comparison with on-site measurement data consistent.

Comparison of Long-Term Adjusted data with Mast data After performing MCP with 6 months measurement data and One-Year WDG selected output (GDR-MERRA), a One-Year hourly data time series is generated

5 Comparison of Long-Term Adjusted data with Mast data After performing MCP with 6 months measurement data and One-Year WDG selected output (GDR-MERRA), a One-Year hourly data time series is generated and correlation coefficient and mean wind speed are compared with the valid data measured at mast location for the same period. It is noted that 12 months of measurement data are valid during this comparison year and the comparison is thus done for this concurrent period. Figure 2 Regression summary and correlation coefficient between mast and post-processed data

data measured at mast location for the same period.

Figure 3 Summary of wind data comparison From figure 1 and 2, a correlation coefficient on hourly data of 0.875 (R²) is observed between the two datasets.

6 Figure 3 Summary of wind data comparison From figure 1 and 2, a correlation coefficient on hourly data of (R²) is observed between the two datasets. Relative error on mean wind speed for the concurrent period is 0.35%. Diurnal profile, frequency distribution, monthly wind speed and wind rose are also compared. The analysis concludes that data quality thus generated considering GDR-MERRA as reference could be considered as the best for this location for long term adjustment of on-site measured wind speeds.

7 Figure 4 Wind rose, sectorial energy and probability distribution function from WDG post-processed (left) and mast (right)

8 In figure 4, probability distribution functions from the two datasets are compared and it is observed that WDG post-processed data has managed to represent the measurement data consistently. Wind roses are also compared for wind direction and sector-wise energy yield. WDG has simulated them very well. The table below summarizes the different statistical parameters in predominant wind directions. WDG Mast Direction sector ( ) Mean wind speed (m/s) Median (m/s) Std Dev (m/s) Weibull k (-) Weibull c (m/s) Long-Term Validation Figure 5 Long term correlation To check the consistency of WDG output we further validated the data for long term. For this location we got 19 months of concurrent data. The correlation coefficient of GDR-MERRA and mast for this period is 0.75 (figure 5).

The table below summarizes the different statistical parameters in predominant wind directions.

9 Conclusion By selecting the most appropriate WDG output and then using this output as reference data for MCP process, long-term adjustment process can be optimized with excellent levels of correlation and reduced relative errors on annual mean wind speed. The WDG can be used for long-term data adjustment even in complex sites and can help project owners in reducing project uncertainties by considering the long-term annual variability in the wind resource assessment process. Further improvement MeteoPole suggests considering ensemble mean of Mesoscale/Reanalysis datasets rather relying on one data output if each of them is well correlated with the observation. In this analysis, considering the ensemble mean of GDR-MERRA, GDR- CFSR, WDG-GFS and WDG-NCEP correlation coefficient with mast data for initial 6 months increases from 0.81 to Thus, generated one year post-mcp data has a correlation with mast data for the same period increased from 0.87 to 0.89.

The WDG can be used for long-term data adjustment even in complex sites and can help project owners in reducing project uncertainties by considering the long-term annual variability in the wind

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