A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China
Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/21/5255 |
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| author | Zihao Pang Yu Zhang Chunxiang Shi Junxia Gu Qingjun Yang Yang Pan Zheng Wang Bin Xu |
| author_facet | Zihao Pang Yu Zhang Chunxiang Shi Junxia Gu Qingjun Yang Yang Pan Zheng Wang Bin Xu |
| author_sort | Zihao Pang |
| collection | DOAJ |
| description | Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research. |
| first_indexed | 2024-03-11T11:22:12Z |
| format | Article |
| id | doaj.art-4913c7582af94515b583de92b4b8773d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T11:22:12Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4913c7582af94515b583de92b4b8773d2023-11-10T15:11:32ZengMDPI AGRemote Sensing2072-42922023-11-011521525510.3390/rs15215255A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in ChinaZihao Pang0Yu Zhang1Chunxiang Shi2Junxia Gu3Qingjun Yang4Yang Pan5Zheng Wang6Bin Xu7National Meteorological Information Center, Beijing 100081, ChinaHenan Meteorological Observation Data Center, Zhengzhou 450003, ChinaNational Meteorological Information Center, Beijing 100081, ChinaNational Meteorological Information Center, Beijing 100081, ChinaQinghai Meteorological Information Center, Xining 810012, ChinaNational Meteorological Information Center, Beijing 100081, ChinaNational Meteorological Information Center, Beijing 100081, ChinaNational Meteorological Information Center, Beijing 100081, ChinaPrecipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research.https://www.mdpi.com/2072-4292/15/21/5255extreme rainstormradarsatelliteERA5multi-source data mergingprecipitation monitoring |
| spellingShingle | Zihao Pang Yu Zhang Chunxiang Shi Junxia Gu Qingjun Yang Yang Pan Zheng Wang Bin Xu A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China Remote Sensing extreme rainstorm radar satellite ERA5 multi-source data merging precipitation monitoring |
| title | A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China |
| title_full | A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China |
| title_fullStr | A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China |
| title_full_unstemmed | A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China |
| title_short | A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China |
| title_sort | comprehensive assessment of multiple high resolution precipitation grid products for monitoring heavy rainfall during the 7 20 extreme rainstorm event in china |
| topic | extreme rainstorm radar satellite ERA5 multi-source data merging precipitation monitoring |
| url | https://www.mdpi.com/2072-4292/15/21/5255 |
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