New paradigm for statistical validation of satellite precipitation estimates: Application to a large sample of the TMPA 0.25° 3-hourly estimates over Oklahoma

It is well acknowledged that space-based and ground-based radar estimates of rainfall are both affected by several sources of uncertainties; nevertheless, the latter are generally used as ground reference for the validation of the former. In this way, it is difficult to assess what portion of the observed discrepancies is solely due to the targeted satellite errors and what is the impact of the radar-rainfall errors. The authors propose a novel approach for the validation of satellite-based rainfall estimates, in which an empirically based radar-rainfall error model is used to account for deterministic and random uncertainties in radar-rainfall estimates. Using an easy-to-interpret metric, the approach can be used to answer probabilistic questions such as "Given radar and satellite rainfall estimates, what is the probability that the true rainfall is larger (smaller) than the satellite estimate"? The results are based on a large sample (6 years) of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 0.25-degree 3-hourly satellite-rainfall estimates and hourly radar-rainfall product (aggregated at the satellite space-time scale) from the Oklahoma City WSR-88D radar (KTLX). This study shows how the TMPA satellite product tends to overestimate the high rainfall values and underestimate the low values and how it tends to perform better during the hot season (June to September). Moreover, the authors demonstrate how the presence of errors in the ground-based radar could significantly affect the results of the satellite product evaluation.