Improving Wind Forecasts Using a Gale-Aware Deep Attention Network


  • Numerical weather prediction of wind speed requires statistical postprocessing of systematic errors to obtain reliable and accurate forecasts. However, use of postprocessing models is often undesirable for extreme weather events such as gales. Here, we propose a postprocessing algorithm based on a gale-aware deep attention network to simultaneously improve wind speed forecasts and gale area warnings. Specifically, the algorithm includes both a gale-aware loss function that focuses the model on potential gale areas, and an observation station supervision strategy that alleviates the problem of missing extreme values caused by data gridding. The effectiveness of the proposed model was verified by using data from 235 wind speed observation stations. Experimental results show that our model can produce wind speed forecasts with a root-mean-square error of 1.1547 m s−1, and a Hanssen–Kuipers discriminant score of 0.517, performance that is superior to that of the other postprocessing algorithms considered.
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