Use of Storm Life Cycle Information and Lightning Data in Radar-Rainfall Estimation

Abstract

Correcting real-time radar-rainfall estimates for mean field systematic errors (bias) is normally accomplished through gauge-based adjustment procedures. This study explores two auxiliary data sources derived from cloud-to-ground (CG) lightning measurements and storm tracking applied on radar images in terms of providing microphysical information useful for improving the efficiency of gauge-based bias adjustment techniques. The CG information is used to classify storms into thunderstorms (T-storms) versus showers (i.e., storms without lightning) and the tracking algorithm is used to classify storms according to their stage of maturity (i.e., growing, maturing, and decaying). Data for this study are based on high-resolution radar-rainfall estimates (2-km spatial grid resolution at 15-min intervals) available over the South Florida Water Management District for a period of 11 months, along with corresponding rain gauge measurements from 120 gauges and CG occurrences from the National Lightning Detection Network. The radar error analysis for T-storms versus showers and for the different storm maturity stages indicate that storm tracking and CG contain significant microphysical information that can improve radar-rainfall estimation. It is shown that radar rain estimates tend to underestimate convective rainfall, primarily associated with the growing stage of the storms or the occurrences of CG lightning; showers and storms at mature or decay stages are shown to be better represented by the standard reflectivity-rainfall (Z-R) relationship used for convective and tropical storms in Florida. Results from this study indicate that information regarding storm maturity stage derived from tracking radar images, and to a lesser extent, CG observations, can be used to reduce variability in the Z-R conversion and consequently improve accuracy in real-time radar-rainfall estimation.