Skilful nowcasting of extreme precipitation with NowcastNet.

ABSTRACT: Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details^1-3. Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation⁴ and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation⁵. We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10356617 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature