Fig. 7: Multisource integration and predictive analytics in precipitation forecasting.
From: Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
A demonstrates the reduction of prediction skill or information content of precipitation forecasts as lead time (shown in logarithmic scale) increases, comparing (a) persistence, (b) nowcasting, (c) mesoscale and (d) synoptic scale numerical weather prediction (NWP), (e) merged approach within the boundary of (f) limit of predictability44,63,64,65. Merged forecasts can be a combination of nowcasting, NWP models, satellite information, etc. B demonstrates generation of precipitation forecasts using a deep generative model (DGM). The proposed DGM combines observed remotely sensed data from radar and geostationary satellites, ground sensors, ancillary information from terrain properties, physics of precipitation22,66 and NWP state variables to enhance forecast accuracy.
