6th International Conference Energy & Meteorology: Abstract Submission

Nested Mesoscale Simulations for Wind Assessment over a Complex Site in India forced by Different Reanalysis (757)

Eric TROMEUR 1 , Chinmay Kulkarni 2
  2. METEODYN Climatology and Dynamics India Pvt. Ltd, Pune, INDIA

An accurate simulation of wind is subject of utmost importance in Wind Resource Assessment (WRA). For a particular site, locally acquired wind measurements for a span of minimum one year is required to represent climatology for wind assessment. However, it takes huge cost in the operations of wind measurements, and there is possibility of losses in collected data.

Nested mesoscale model WRF and Large-Eddy Simulation (LES) framework within it can be used for wind energy applications to generate realistic wind data at the site. In this study, the performance of the WRF mesoscale model and Large-eddy simulation for wind assessment was assessed and evaluated under different initial and boundary forcing conditions. Due to continuous evolution in the development of available reanalysis data, this work aims to compare the performance of reanalysis datasets with WRF model and LES simulations.

WRF-LES simulations with 6 nested domains and resolution 30km, 10km, 3km, 1km, 300m and 100m respectively, have been carried out with datasets such as ERAI, ERA5, CFSR, MERRA, NNRP reanalysis and FNL analysis datasets. These datasets are operated by different agencies and have different spatial and temporal resolution. The observation data in the study is obtained from National Institute of Wind Energy (NIWE), India. Based on the results it can be concluded that, WRF-LES is capable tool to generate realistic time series data. All the datasets succeeded to catch the trend of observation data. The statistical analysis of RMSE, MAE and BIAS of FNL and ERA5 shows the smallest variations with observations compared to statistics from other reanalysis data used. Finally, WRF simulations and LES feed with FNL and ERA5 reanalysis performed better than other reanalysis data and can be considered as reliable forcing to generate accurate results.