| Abstract: | Convective storms and lightning are among the most important weather phenomena that are challenging to forecast. In this study, a novel multi-task learning (MTL) encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min, using GOES-16 geostationary satellite infrared brightness temperatures (IRBTs), lightning flashes from Geostationary Lightning Mapper (GLM), and vertically integrated liquid (VIL) from Next Generation Weather Radar (NEXRAD). To cope with the heavily skewed distribution of lightning data, a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed. The effects of MTL, single-task learning (STL), and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated. The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event. The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting, particularly for intense lightning events. The MTL also helped delay the lightning forecast performance decay with the lead times. Furthermore, incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting, but produced little difference in VIL forecasting. Finally, the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells. |
