Learning physically interpretable deep networks from reanalysis data for medium-term regional PM 2.5 forecasts

Abstract Medium-term regional forecasts of fine particulate matter (PM 2.5 ) are critical to mitigate its hazardous environmental effects. Deep learning (DL) has recently emerged as a promising technique to improve traditional forecasts based on chemistry-transport models (CTMs). However, challenges