A Statistical Approach to Ground Radar-Rainfall Estimation

Abstract

This paper presents development of a statistical procedure for estimation of ensemble rainfall fields from a combination of ground radar observations and in situ rain gauge measurements. The uncertainty framework characterizes radar-rainfall estimation algorithm limitation accounting for rain gauge sampling uncertainty. The procedure is applied on a multicomponent rainfall estimation algorithm, which utilizes a rain-path attenuation correction technique, a power-law reflectivity-to-rainfall (Z?R) relationship, and a parameter to differentiate between convective (C) and stratiform (S) regimes in the Z?R conversion. Uncertainty is explicitly accounted for by evaluating the algorithm?s parameter set posterior probability density function (known as parameters? equifinality) on the basis of the Generalized Likelihood Uncertainty Estimation (GLUE) framework. The study is facilitated by NASA?s C-band Doppler radar [named the Tropical Ocean Global Atmosphere (TOGA)] observations and four dense rain gauge clusters available from the Tropical Rainfall Measuring Mission (TRMM)-Large-Scale Biosphere?Atmosphere (LBA) experiment, conducted between January and February of 1999 in Southwest Amazon. Statistics are proposed for jointly evaluating the wideness of radar retrieval uncertainty limits [uncertainty ratio (UR)] and the percentage of observations that fall within those error bounds [exceedance ratio (ER)]. Results show that the parameter range selected in GLUE could characterize the radar-rainfall estimation uncertainty. Combined assessment of UR and ER for a varying range of parameters? equifinality provides an objective basis for comparing rain retrieval algorithms and determining uncertainty bounds. Ensemble radar-rainfall fields derived on the basis of this procedure can be used to statistically assess satellite rain retrieval algorithms and derive ensemble hydrologic predictions driven by radar-rainfall input (e.g., runoff and soil moisture).