A comparative performance analysis of TRMM 3B42 (TMPA) versions 6 and 7 for hydrological applications over Andean-Amazon river basins

• Autores: Zulkafli, Z., Buytaert, W., Onof, C., Manz, B., Tarnavsky, E., Lavado-Casimiro, W., Guyot, J.L.
• Formato: artículo
• Fecha de Publicación: 2014
• Institución: Servicio Nacional de Meteorología e Hidrología del Perú
• Repositorio: SENAMHI-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.senamhi.gob.pe:20.500.12542/59
• Enlace del recurso: http://repositorio.senamhi.gob.pe/handle/20.500.12542/59; https://doi.org/ 10.1175/JHM-D-13-094.1; https://hdl.handle.net/20.500.12542/59
• Nivel de acceso: acceso abierto
• Materia: Algorithm; Comparative study; Performance assessment; Precipitation assessment; River basin; Streamflow; TRMM; https://purl.org/pe-repo/ocde/ford#1.05.11; precipitacion - Clima y Eventos Naturales

The Tropical Rainfall Measuring Mission 3B42 precipitation estimates are widely used in tropical regions for hydrometeorological research. Recently, version 7 of the product was released. Major revisions to the algorithm involve the radar reflectivity-rainfall rate relationship, surface clutter detection over high terrain, a new reference database for the passive microwave algorithm, and a higher-quality gauge analysis product for monthly bias correction. To assess the impacts of the improved algorithm, the authors compare the version 7 and the older version 6 products with data from 263 rain gauges in and around the northern Peruvian Andes. The region covers humid tropical rain forest, tropical mountains, and arid-to-humid coastal plains. The authors find that the version 7 product has a significantly lower bias and an improved representation of the rainfall distribution. They further evaluated the performance of the version 6 and 7 products as forcing data for hydrological modeling by comparing the simulated and observed daily streamflow in nine nested Amazon River basins. The authors find that the improvement in the precipitation estimation algorithm translates to an increase in the model Nash-Sutcliffe efficiency and a reduction in the relative bias between the observed and simulated flows by 30%-95%.