Combining high-resolution wind downscaling with numerical weather prediction models

High resolution wind speed forecasts are crucial for a range of applications, including the management of onshore wind power generation. Conventional wind speed forecasting is bound to the coarse spatial resolution of NWP models of 2-30 km. The wind speed complementarity model (WiCoMo)^* is a high-resolution wind downscaling model that provides distributions of annual wind speeds across Germany at a horizontal resolution of 25 m x 25 m. This work aims to combine high resolution wind downscaling with numerical weather prediction (NWP) models to improve accuracy and resolve local effects, particularly in complex terrain. Quantile mapping was used to derive a transfer function at each 25 m x 25 m grid cell based on annual historical wind speeds calculated by WiCoMo and the NWP models respectively. The function was then applied to hourly time series of NWP models to simulate downscaled predictions. In addition, power curves of wind turbines were used to calculate the onshore wind power output of Germany from the high-resolution forecast. Validation metrics were used to compare the performance of the WiCoMo-enhanced NWP models with raw NWP outputs. The analysis demonstrates that the WiCoMo-enhanced NWP models outperform raw NWP across all tested models. For the year 2022, the MAE of NEMS4 was reduced from 1.66 m/s to 1.13 m/s and for NEMSGLOBAL it improved by almost 33%. The MBE was reduced to near 0 in all cases. Furthermore, spatial evaluations show that local wind speed effects often falling below the grid size in NWP models, such as hilltop speed-up or sheltering valleys, are resolved by the downscaling. The study suggests that localized wind speeds at wind turbine sites improve the accuracy of wind power output predictions. However, several limitations are identified, including challenges in applying corrections during specific weather conditions. Additionally, the modelled wind power output could not be validated at single turbine sites, limiting the validity of estimates for the entire country. The study demonstrates the potential of WiCoMo-enhanced NWP models in improving wind speed forecasting capabilities. The findings have important implications for various applications, including renewable energy planning and risk assessment.

 

^* Christopher Jung and Dirk Schindler. Introducing a new wind speed complementarity model. Energy, 265:126284, 2023. ISSN 0360-5442. doi: https://doi.org/10.1016/j.energy.2022.126284.