Premium
Spatial and Temporal Evaluation of Radar Rainfall Nowcasting Techniques on 1,533 Events
Author(s) -
Imhoff R. O.,
Brauer C. C.,
Overeem A.,
Weerts A. H.,
Uijlenhoet R.
Publication year - 2020
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2019wr026723
Subject(s) - nowcasting , precipitation , storm , environmental science , radar , lead time , meteorology , climatology , convective storm detection , benchmark (surveying) , probabilistic logic , event (particle physics) , weather radar , statistics , computer science , mathematics , geology , geography , cartography , telecommunications , physics , marketing , quantum mechanics , business
Abstract Radar rainfall nowcasting, the process of statistically extrapolating the most recent rainfall observation, is increasingly used for very short range rainfall forecasting (less than 6 hr ahead). We performed a large‐sample analysis of 1,533 events, systematically selected for 4 event durations and 12 lowland catchments (6.5–957 km 2 ), 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‐hr durations, 40 min for 3 hr, 56 min for 6 hr, and 116 min for 24 hr. 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 toward 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.