Open AccessNowcasting Rainfall Fields Derived from Specific Differential Phase
Author(s) -
Evan Ruzanski,
V. Chandrasekar
Publication year - 2012
Publication title -
journal of applied meteorology and climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.079
H-Index - 134
eISSN - 1558-8432
pISSN - 1558-8424
DOI - 10.1175/jamc-d-11-081.1
Subject(s) - nowcasting , radar , precipitation , estimator , environmental science , rain gauge , categorical variable , meteorology , differential phase , remote sensing , phase (matter) , computer science , statistics , mathematics , physics , geology , quantum mechanics , telecommunications
Short-term automated forecasting (nowcasting) of precipitation has traditionally been done using radar reflectivity data; recent research, however, indicates that using specific differential phase K dp has several advantages over using reflectivity for estimating rainfall. This paper presents an evaluation of the characteristics of nowcasting K dp -based rainfall fields using the Collaborative Adaptive Sensing of the Atmosphere K dp estimation and nowcasting methods applied to approximately 42 h of X-band radar network data. The results show that K dp -based rainfall fields exhibit lifetimes of ~17 min as compared with ~15 min for rainfall fields derived from reflectivity Z h in a continuous (cross correlation based) sense. Categorical (skill score based) lifetimes of ~26 min were observed for K dp -based rainfall fields as compared with ~30 min for Z h -based rainfall fields. Radar–rain gauge verification showed that K dp -based rainfall estimates consistently outperformed Z h -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.