Capability of satellite data to estimate observed precipitation in southeastern South America

Author:

Benítez Victoria D.1ORCID,Forgioni Fernando P.2ORCID,Lovino Miguel A.13ORCID,Sgroi Leandro1ORCID,Doyle Moira E.456ORCID,Müller Gabriela V.13ORCID

Affiliation:

1. Centro de Estudios de Variabilidad y Cambio Climático (CEVARCAM) Universidad Nacional del Litoral Santa Fe Argentina

2. Departamento de Fisica. Centro de Ciências Naturais e Exatas Universidade Federal de Santa María Santa María Brasil

3. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Buenos Aires Argentina

4. Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina

5. CONICET‐Universidad de Buenos Aires Centro de Investigaciones del Mar y la Atmósfera (CIMA) Buenos Aires Argentina

6. CNRS‐IRD‐CONICET‐UBA Instituto Franco‐Argentino para el Estudio del Clima y sus Impactos (IRL 3351 IFAECI) Buenos Aires Argentina

Abstract

AbstractPrecipitation is a fundamental component of the water cycle. Satellite‐derived precipitation estimates with high spatial resolution and daily to subdaily temporal resolution become very important in regions with a limited ground‐based measurement network, such as southeastern South America (SESA). This study evaluates the performance of four state‐of‐the‐art satellite products, including IMERG V.06 Final Run, PERSIANN, PERSIANN CCS‐CDR and PDIR‐NOW in representing observed precipitation over SESA during the 2001–2020 period. The ability of each product to represent observed annual and seasonal precipitation patterns was assessed. Statistical and categorical evaluation metrics were used to evaluate the performance of satellite precipitation estimates at monthly and daily timescales. Our results report that IMERG and CCS‐CDR achieve the best performance in estimating observed precipitation patterns at annual and seasonal timescales. While all satellite products effectively identify autumn and spring precipitation patterns, they struggle to represent winter and summer patterns. Notably, all satellite precipitation products have a better agreement with observed precipitation in wetter regions compared to drier regions, as indicated by the spatial distribution of continuous validation metrics. IMERG stands out as the most accurate product, reaching the highest correlation coefficients (0.75 < CC < 0.95) and Kling–Gupta efficiencies (0.65 < KGE < 0.85, rate as good to very good performance). Regarding categorical statistical metrics, IMERG correctly estimates the fraction of observed rainy days (POD > 0.7, CSI > 0.6) and shows the lowest fraction of estimated precipitation events that did not occur. PERSIANN, CCS‐CDR and PDIR‐NOW exhibit lower performances, mainly in drier areas. Moreover, PERSIANN and PDIR‐NOW tend to overestimate observed precipitation in almost the entire SESA region. We expect this validation study will provide greater reliability to satellite precipitation estimates, in order to provide an alternative that complement the scarce observed information available for decision‐making in water management and agricultural planning.

Funder

Agencia Santafesina de Ciencia, Tecnología e Innovación

Universidad Nacional del Litoral

Consejo Nacional de Investigaciones Científicas y Técnicas

Publisher

Wiley

Subject

Atmospheric Science

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