Evaluation of satellite-retrieved extreme precipitation rates across the central United States

Abstract

Water resources management, forecasting, and decision making require reliable estimates of precipitation. Extreme precipitation events are of particular importance because of their severe impact on the economy, the environment, and the society. In recent years, the emergence of various satellite‐retrieved precipitation products with high spatial resolutions and global coverage have resulted in new sources of uninterrupted precipitation estimates. However, satellite‐based estimates are not well integrated into operational and decision‐making applications because of a lack of information regarding the associated uncertainties and reliability of these products. In this study, four satellite‐derived precipitation products (CMORPH, PERSIANN, TMPA‐RT, and TMPA‐V6) are evaluated with respect to their performance in capturing precipitation extremes. The Stage IV (radar‐based, gauge‐adjusted) precipitation estimates are used as reference data. The results show that with respect to the probability of detecting extremes and the volume of correctly identified precipitation, CMORPH and PERSIANN data sets lead to better estimates. However, their false alarm ratio and volume are higher than those of TMPA‐RT and TMPA‐V6. Overall, no single precipitation product can be considered ideal for detecting extreme events. In fact, all precipitation products tend to miss a significant volume of rainfall. With respect to verification metrics used in this study, the performance of all satellite products tended to worsen as the choice of extreme precipitation threshold increased. The analyses suggest that extensive efforts are necessary to develop algorithms that can capture extremes more reliably.

Dry/Wet Conditions Monitoring Based on TRMM Rainfall Data and Its Reliability Validation over Poyang Lake Basin, China

“…Narayanan et al [30] validated TRMM 3B42 data with India Meteorological Department (IMD) rain gauges data and showed that the satellite algorithm does not pick up very high and very low daily rainfalls. Similar perspectives were also shown in research by Tian et al [31], AghaKouchak et al [32] and Sorooshian [33]. However, many researchers also show that the TRMM data performed perfectly at monthly or a longer time scale.…”

Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China

“…Moreover, in some areas, such as mountainous regions, the quality of radar datasets is not high due to beam blockage, propagation errors, and vertical variability of reflectivity. On the other hand, radar data can be obtained from only limited areas [2,7,8]. However, precipitation data extracted from satellites overcome the previous problems, giving us a chance to acquire abundant precipitation information [9].…”

Rainfall Comparison from Different Precipitation Estimates over West Africa

“…The results of [12] showed that CMORPH and PERSIANN rainfall products indicated better precipitation estimates. However, the false alarm ratio (FAR) and volume of CMORPH and PERSIANN data sets are higher than those of TMPA-RT and TMPA-V6.…”

“…However, the false alarm ratio (FAR) and volume of CMORPH and PERSIANN data sets are higher than those of TMPA-RT and TMPA-V6. According to [12], no single rainfall product can be considered perfect for detecting extreme precipitation events. [13] showed that at seasonal or annual time scales, CMORPH has much higher biases and RMS errors than TRMM 3B42.…”