An Intercomparison and Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly Gauge Data

Abstract

Abstract The last several years have seen the development of a number of new satellite-derived, globally complete, high-resolution precipitation products with a spatial resolution of at least 0.25° and a temporal resolution of at least 3-hourly. These products generally merge geostationary infrared data and polar-orbiting passive microwave data to take advantage of the frequent sampling of the infrared and the superior quality of the microwave. The Program to Evaluate High Resolution Precipitation Products (PEHRPP) was established to evaluate and intercompare these datasets at a variety of spatial and temporal resolutions with the intent of guiding dataset developers and informing the user community regarding the error characteristics of the products. As part of this project, the authors have performed a subdaily intercomparison of five high-resolution datasets [Climate Prediction Center morphing (CMORPH) technique; Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA); Naval Research Laboratory (NRL) blended technique; National Environmental Satellite, Data, and Information Service Hydro-Estimator; and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] with existing subdaily gauge data over the United States and the Pacific Ocean. Results show that these data are effective at representing high-resolution precipitation, with correlations against 3-hourly gauge data as high as 0.7 for CMORPH, which had the highest correlations with the validation data. Biases are relatively high for most of the datasets over land (apart from the TMPA, which is gauge adjusted) and ocean, with a general tendency to overestimate warm season rainfall over the United States and to underestimate rainfall over the tropical Pacific Ocean. Additionally, all the products studied faithfully resolve the diurnal cycle of precipitation when compared with the validation data.