Abstract
AbstractShort-term automated forecasting (nowcasting) of precipitation has traditionally been done using radar reflectivity data; recent research, however, indicates that using specific differential phase Kdp has several advantages over using reflectivity for estimating rainfall. This paper presents an evaluation of the characteristics of nowcasting Kdp-based rainfall fields using the Collaborative Adaptive Sensing of the Atmosphere Kdp estimation and nowcasting methods applied to approximately 42 h of X-band radar network data. The results show that Kdp-based rainfall fields exhibit lifetimes of ~17 min as compared with ~15 min for rainfall fields derived from reflectivity Zh in a continuous (cross correlation based) sense. Categorical (skill score based) lifetimes of ~26 min were observed for Kdp-based rainfall fields as compared with ~30 min for Zh-based rainfall fields. Radar–rain gauge verification showed that Kdp-based rainfall estimates consistently outperformed Zh-based estimates out to a lead time of 30 min, but the difference between the two estimators decreased in terms of normalized standard error with increasing lead time.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call