Using an Artificial Neural Network to improve operational wind prediction in a small unresolved valley

Forecasting valley winds over complex terrain using a coarse horizontal resolution mesoscale model is a challenging task. Mesoscale models such as
the Weather Research and Forecasting (WRF) model tend to perform poorly over such regions. In this study, we assess the added value of downscaling
WRF wind forecasts using artificial neural networks (ANN) over the Cadarache Valley which is located in southeast France. Wind forecasts over the Cadarache valley are generated using WRF with a horizontal resolution of 3km on a daily basis. We used performance metrics such as Directional ACCuracy (DACC) and mean absolute error (MAE) for the evaluation of the WRF and ANN. WRF horizontal wind components at 110m and the near surface vertical potential temperature gradient were used as input data and observed horizontal wind components at 10m within the valley as targets during ANN training. We found an increase of DACC from 56% to 79% after post-processing WRF forecasts with ANN. Further analysis show that the ANN performed well during day and night, but poorly during morning and afternoon transition. The performance of WRF has a huge influence on ANN performance with bad WRF forecasts affecting ANN performance. However, the ANN improves the poor WRF forecasts to a DACC exceeding 60%. A change in lead time and domain resolution showed negligible impact suggesting that 3km resolution and a lead time of 24-47h is effective and relatively cheap to apply. Additionally, WRF performs well in near-neutral conditions and poorly in other atmospheric stability conditions. However ANN showed a consistent improvement in wind forecast during all stability classes with a DACC of nearly 80%. The study clearly demonstrates the ability to improve Cadarache valley wind forecasts using ANN from WRF simulations on a daily basis.