Day-Ahead Forecasting for the Tropics with Numerical Weather Prediction and Machine Learning

Numerical weather prediction (NWP) and machine learning (ML) methods are popular for weather forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. This is further complicated when regional NWP models are used with global climate models, each with multiple possible parameterizations. In this study, a hybrid numerical-statistical approach is proposed and evaluated for four radiation models. Weather Research and Forecasting (WRF) model is run in both global and regional mode to provide an estimate for solar irradiance. This estimate is then post-processed using ML to provide a final prediction. Normalized root-mean-square error from WRF is reduced by up to 40-50% with this ML error correction model. Results obtained using CAM, GFDL, New Goddard and RRTMG radiation models were comparable after this correction, negating the need for WRF parameterization tuning. Other models incorporating nearby locations and an ensemble set-up are also evaluated, although they produced much smaller improvements.