Evaluating the benefits of merging near-real-time satellite precipitation products
| Autor: | Dimitri Solomatine, Mohamed Rasmy, Yoshihiro Shibuo, Gonzalo E. Espinoza-Dávalos, Nikolaos Mastrantonas, Biswa Bhattacharya |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Rainfall
Atmospheric Science Microwave observations 010504 meteorology & atmospheric sciences 0207 environmental engineering 02 engineering and technology Precipitation Structural basin Satellite precipitation 01 natural sciences Satellite observations Bias Error analysis Climatology Environmental science Spatiotemporal resolution Hydrology 020701 environmental engineering Global Precipitation Measurement 0105 earth and related environmental sciences |
| Zdroj: | Journal of Hydrometeorology, 20(6) |
| ISSN: | 1525-755X |
| Popis: | After the launch of the Global Precipitation Measurement (GPM) mission in 2014, many satellite precipitation products (SPPs) are available at finer spatiotemporal resolution and/or with reduced latency, potentially increasing the applicability of SPPs for near-real-time (NRT) applications. Therefore, there is a need to evaluate the NRT SPPs in the GPM era and investigate whether bias-correction techniques or merging of the individual products can increase the accuracy of these SPPs for NRT applications. This study utilizes five commonly used NRT SPPs, namely, CMOPRH RT, GSMaP NRT, IMERG EARLY, IMERG LATE, and PERSIANN-CCS. The evaluation is done for the Kinu basin region in Japan, an area that provides observed rainfall data with high accuracy in space and time. The selected bias correction techniques are the ratio bias correction and cumulative distribution function matching, while the merged products are derived with the error variance, inverse error variance weighting, and simple average merging techniques. Based on the results, all SPPs perform best for lower-intensity rainfall events and have challenges in providing accurate estimates for typhoon-induced rainfall (generally more than 50% underestimation) and at very fine temporal scales. Although the bias correction techniques successfully reduce the bias and improve the performance of the SPPs for coarse temporal scales, it is found that for shorter than 6-hourly temporal resolutions, both techniques are in general unable to bring improvements. Finally, the merging results in increased accuracy for all temporal scales, giving new perspectives in utilizing SPPs for NRT applications, such as flood and drought monitoring and early warning systems. |
| Databáze: | OpenAIRE |
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