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

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.

Comparative performance analysis of climate re-analysis approaches in Angola

“…These results highlight the limitations of re-analysis data to undertake precipitation variability studies over Angola. Previous studies in areas with dense gauge networks or radar coverage suggested that re-analysis exercises perform better in higher latitudes and cooler regions, where precipitation arises from frontal systems, and that convective precipitation, common in lower and warmer latitudes such as in Angola, is better characterized by satellite precipitation (Ebert et al 2007, Sapiano and Arkin 2009, Vila et al 2009). Pombo et al (2015) studied satellite precipitation estimates for Angola and the results generally outperformed the re-analysis estimates at the annual and seasonal levels presented in this paper.…”

“…The use of remote sensors installed in satellites can be very useful in overcoming this problem, and there are now a number of standard products that routinely offer estimates of precipitation and surface air temperature at regularly spaced locations (grid) and at regular time intervals (few hours), which may be used to improve the description of the spatial-temporal changes of these variables (Sapiano andArkin 2009, Maidment et al 2012). Several studies have evaluated the performance of these standard remote sensing products in Africa by comparing monthly and annual estimates with ground gauge records (Nicholson et al 2003, Hughes 2006, Beighley et al 2011.…”

Avaliação de um Modelo Estocástico de Erro Multidimensional Aplicado a Estimativas de Precipitação por Satélite

“…Por isso, diversos estudos quantificaram as incertezas associadas nas estimativas de precipitação por satélite (como por exemplo Huffman 1997, Tian et al, 2007, Sapiano and Arkin 2009. Mais recentemente, estudos investigaram o erro de precipitação por satélite em escala de tempo relevantes e sua propagação através de modelos hidrológicos.…”

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

“…These studies can be categorized into two groups: those that investigate and evaluate precipitation products (Morrissey and Greene 1998;McPhee and Margulis 2005;Tian and Peters-Lidard 2007;Habib et al 2009;Sapiano and Arkin 2009;Tian et al 2009;Tian and Peters-Lidard 2010;Shen et al 2010;Anagnostou et al 2010;Stampoulis and Anagnostou 2012;Habib et al 2012;Kirstetter et al 2013a;S. Chen et al 2013a,b;Joshi et al 2013;Alemohammad et al 2014;Tang et al 2014;Alemohammad et al 2015) and those that model the errors associated with these retrievals (Bellerby 2007;AghaKouchak et al 2009;Villarini et al 2009;AghaKouchak et al 2010b;Tian et al 2013;Kirstetter et al 2013b;Seyyedi et al 2014a,b;Maggioni et al 2014).…”