Spatial and Temporal Evaluation of Radar Rainfall Nowcasting Techniques on 1,533 Events

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 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‐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.

Skilful precipitation nowcasting using deep generative models of radar

“…We use a single case study to compare the nowcasting performance of the generative method DGMR to three strong baselines: PySTEPS, a widely used precipitation nowcasting system based on ensembles, considered to be state-of-the-art 3,4,13 ; UNet, a popular deep learning method for nowcasting 15 ; and an axial attention model, a radar-only implementation of MetNet 19 . For a meteorologically challenging event, Figs.…”

“…In these models, motion fields are estimated by optical flow, smoothness penalties are used to approximate an advection forecast, and stochastic perturbations are added to the motion field and intensity model 3,4,12 . These stochastic simulations allow for ensemble nowcasts from which both probabilistic and deterministic forecasts can be derived and are applicable and consistent at multiple spatial scales, from the kilometre scale to the size of a catchment area 13 .…”

Rainfall Nowcasting Using Commercial Microwave Links

“…It has a modular nowcasting framework and is based on S‐PROG (Seed, 2003) and STEPS (Bowler et al, 2006; Seed et al, 2013). We used the same pySTEPS setup as Imhoff et al (2020): the Lucas‐Kanade optical flow method (Lucas & Kanade, 1981) using the QPE from t −3 to t , a backward semi‐Lagrangian advection method (Germann & Zawadzki, 2002), and the STEPS nowcasting procedure with a nonparametric noise method (Bowler et al, 2006; Seed et al, 2013). See Imhoff et al (2020) for the performance of this setup under different circumstances for a large sample of rainfall events in the Netherlands.…”

GPM Annual and Daily Precipitation Data for Real-Time Short-Term Nowcasting: A Pilot Study for a Way Forward in Data Assimilation

“…Correspondingly, more studies are required to assess the performance of GPM IMERG against other satellite and reanalysis products to find the advantages and limitations of the GPM satellite. Furthermore, other precipitation specific properties such as spatial and temporal heterogeneity, spatial and temporal intermittence, extreme variability and multi-fractal comparisons is necessary to overcome substantial uncertainties rainfall estimations [57][58][59]. Cristiano et al [60] also depicted in their review that the rainfall variability may need wider range of conditions and scenario based in observational datasets for urban hydrological basins, which need proper analysis for development of better hydrological response system with higher sensitivity.…”