Key points are not available for this paper at this time.
Accurate estimation of precipitation is crucial for crop yield assessment, flood and drought monitoring, and water structures management. Precipitation is subject to both temporal and spatial variability. While recording rain gauges support temporal resolution, they measure point rainfall and require dense network and application of interpolation techniques to provide spatial dimension. On the other hand, remote-sensing products cover regional and global spatial scales. Building upon the Tropical Rainfall Measuring Mission (TRMM) heritage, the Global Precipitation Measurement (GPM) mission is an international net of satellites that present the next-generation global observations of rain and snow at a spatial resolution of 0.1° × 0.1° with a half-hour temporal resolution. In this study, March–December 2014 3-hourly TRMM data (3B42V7) and half-hourly Integrated Multi-satellite Retrievals for GPM (IMERG) data are compared with the 3-hourly rain gauges data in Khorasan Razavi province, located in northwest of Iran. Coefficient of determination (R2), Bias, MBias, RBias, mean absolute error (MAE), root mean square error (RMSE) as well as probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics were measured for validation purposes. The result showed that correlation between IMERG data and rain gauge rainfall data is higher than those of 3B42V7 data. In addition, the values of MBias, Bias, and RBias confirmed that both of 3B42V7 and IMERG underestimated rainfall over the study area, whereas MBias of IMERG was higher than 3B42V7. Furthermore, MAE and RMSE values of 3B42V7 and IMERG were similar while IMERG evaluation turned out a better correlation coefficient (r) and POD than 3B42V7. This study showed that IMERG generally had reasonable agreement with the gauge data.
Siuki et al. (Wed,) studied this question.