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 initiation 4 and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation 5 . We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a…

• NN
  National Natural Science Foundation of China

  Awards: 62022050, 62021002
• CM
  China Meteorological Administration
• SF
  Science Fund for Creative Research Groups