资源环境科技发展态势分析平台

Radar rainfall nowcasting, the process of statistically extrapolating the most recent rainfall observation, is increasingly used for very‐short‐range rainfall forecasting (less than six hours ahead). We performed a large‐sample analysis of 1533 events, systematically selected for four event durations and twelve lowland catchments (6.5—957 km²), to determine the predictive skill of nowcasting. Four algorithms are tested and compared with Eulerian Persistence: Rainymotion Sparse, Rainymotion DenseRotation, Pysteps deterministic and Pysteps probabilistic with 20 ensemble members. We focus on the dependency of nowcast skill on: event duration, season, catchment size and location. Maximum skillful lead times increase for longer event durations, due to the more persistent character of these events. For all four event durations, Pysteps deterministic attains the longest average decorrelation times, with 25 min for 1‐h durations, 40 min for 3 h, 56 min for 6 h and 116 min for 24 h. During winter, with more persistent stratiform precipitation, we find three times lower mean absolute errors than for convective summer precipitation. Higher skill is also found after spatially upscaling the forecast. Catchment location matters too: given the prevailing storm movement, two times higher skillful lead times are found downwind than upwind towards the edge of the domain. In most cases, Pysteps algorithms outperform the Rainymotion benchmark algorithms. We speculate that most errors originate from growth and dissipation processes which are not or only partially (stochastically) accounted for.