Deep learning approximations of a CFD model for operational wind and turbulence forecasting

The Norwegian Meteorological Institute has for many years applied a CFD model to downscale operational NWP forecasts to 100-200m spatial resolution for wind and turbulence forecasting for about 20 Norwegian airports. Due to high computational costs, however, the CFD model can only be run twice per day, each time producing a 12-hour forecast. An approximate approach requiring far less compute resources using deep learning has therefore been developed. In this, the relation between relevant NWP forecast variables at grids of 2.5 km spatial resolution and wind and turbulence from the CFD model has been approximated using neural networks with basic convolutional and dense layers. The deep learning models have been trained on approximately two year of the data separately for each airport. The results show that the models are to a large extent able to capture the characteristics of their corresponding CDF simulations, and the method is in due time intended to fully replace the current operational solution.