Toward the Predictability of a Radar-Based Nowcasting System for Different Precipitation Systems

Abstract

Precipitation nowcasting is an important operational service for protecting public property losses and people’s safety. Short-Term Ensemble Prediction System (STEPS) is a probabilistic nowcasting system which has been widely used in the research community (commonly referred as PySTEPS). This study investigates the predictability of PySTEPS during different precipitation systems, i.e., convective and stratiform events. In particular, two study domains, namely, Dallas-Fort Worth (DFW) area in northern Texas and San Francisco Bay Area in northern California, are selected to represent these two typical precipitation patterns, respectively. The experimental nowcasting results show that PySTEPS works well in both areas, especially during stratiform rainfall events in the Bay Area. In addition, PySTEPS exhibits different performance for different precipitation patterns. For convective cases in the DFW area, PySTEPS tends to underestimate rain rate for high-intensity precipitation regions. For stratiform cases in the Bay Area, PySTEPS can predict the precipitation intensity more accurately. With the increase of nowcasting lead time, the qualitative evaluation scores (POD - Probability of Detection, and CSI - Critical Success Index) of PySTEPS decrease slowly during stratiform events compared with convective events, which is also in line with the quantitative evaluation results.