Inter‐comparison of radar‐based nowcasting schemes in the Jianghuai River Basin, China

The primary objective of this study is to compare the forecasting skill of two nowcasting schemes, the Multi-scale Tracking Radar Echoes by Cross-correlation (MTREC) in current usage and the newly developed Multi-scale Tracking and Forecasting Radar Echoes (MTaFRE) used by the State Key Laboratory of Severe Weather (LaSW) of the Chinese Academy of Meteorological Science (CAMS), with the Eulerian Persistence Model (EPM) scheme as a benchmark, and the state-of-the-art Watershed-Clustering Nowcasting (WCN) scheme, which is part of the Warning Decision Support System-Integrated Information (WDSS-II) developed at the University of Oklahoma and the National Severe Storms Laboratory (NSSL). The inter-comparison considers six heavy-rain events and one month of radar data observed by radar networks of the Chinese Meteorological Administration (CMA) located in the Jianghuai River Basin. Four sets of forecast fields up to the next 180min with an interval of 15min were generated by the four nowcasting algorithms, and the forecast performances were evaluated as a function of lead time. At an individual event level, the results show that no single model outperforms all others consistently in cross-skill categories at all lead-time intervals of the six events. Overall, EPM performs worse than the three Lagrangian persistent models (LPMs). The MTREC scheme performs slightly worse than the WCN scheme used in WDSS-II, and the MTaFRE scheme is most comparable to the WCN scheme. More importantly, this study confirms that the MTaFRE shows an improvement over its predecessor MTREC by using multi-scale moving mean windows effectively for different lead times.

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