The PREFER Project: AI Predicting the Future of Weather

PREFER solutions improve weather prediction spatially and temporally for wide applications to all regions in the nation, with easy portability to offer short-term and fine spatial resolution prediction. They aim to address the important problems of meteorological forecast applications (e.g., landfalling of severe thunderstorms or tropical systems), flood warning alert enhancement, backwater wetland storage capacity investigation for river flood mitigation, among others. To this end, the project utilizes simple NN (neural network) models for meteorological and hydrological parameter predictions, called Meteo Modelets and Hydro Modelets, respectively, as depicted in Figure 1 below. Modelet-based regional forecasting works effectively because huge amounts of meteorological and hydrological datasets required for model training are available continuously by means of (1) weather parameter gathering facilities in existence nationally (such as Mesonets and personal weather stations) and (2) USGS streamgages deployed on rivers and streams nationwide to obtain their hydrologic parameters in realtime. PREFER focuses on developing and establishing suitable Meteo and Hydro Modelets, and then on accelerating Modelet training as well.