Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific–Andean region of Ecuador and Peru
- 1KU Leuven, Department of Civil Engineering, Hydraulics Laboratory, 3001 Leuven, Belgium
- 2Departamento de Geología, Minas e Ingeniería Civil, Universidad Técnica Particular de Loja, S. Cayetano, Loja, Ecuador
- 3Departamento de Recursos Hídricos y C. Ambientales & Fac. C. Agropecuarias, Universidad de Cuenca, Cuenca, Ecuador
- 4Vrije Universiteit Brussel, Department of Hydrology and Hydraulic Engineering, 1050 Brussels, Belgium
Abstract. The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP).
In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6° S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6° S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day−1 to 0.2 for rates higher than 20 mm day−1. Compared to its predecessor, 3B42 V7 shows modest region-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.