High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network Data
Satellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| author | Seungwoo Baek Soorok Ryu Choeng-Lyong Lee Francisco J. Tapiador Gyuwon Lee |
| author_facet | Seungwoo Baek Soorok Ryu Choeng-Lyong Lee Francisco J. Tapiador Gyuwon Lee |
| author_sort | Seungwoo Baek |
| collection | DOAJ |
| description | Satellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation estimates but suffer from lower temporal resolution due to their limited observation frequency. This study proposes an efficient algorithm for integrating and enhancing precipitation estimates from multiple satellite observations. The target domain includes the Full Disk (FD) and the extended East Asia (EA) regions, both of which are observable by GEO satellites, such as Himawari-8, serving as the GEO platform in this study. The algorithm involves four steps: pre-data preparation, LEO morphing, adjustment, and final merging. It produces Early and Late composite products with 10-min temporal and up to 2 km spatial resolution and significantly reduces latency compared to IMERG. Specifically, the Early and Late products can be generated with approximate latencies of 90 min and 270 min, respectively—much faster than Integrated Multi-satellite Retrievals for GPM (IMERG)’s Early (4-h) and Late (14-h) products. A key feature of the proposed method is the use of accuracy-based weighting derived from radar-based validation, enabling dynamic merging that reflects the reliability of each satellite observation. Statistical validation using Global Telecommunication System (GTS) precipitation data confirmed the positive impact of the proposed bias correction and merging method. In particular, the Late product achieved accuracy comparable to or higher than that of IMERG Early and IMERG Late, despite its significantly shorter latency. However, its accuracy was still lower than that of IMERG Final, which benefits from additional gauge-based correction but is released with a delay of several months. |
| format | Article |
| id | doaj-art-06e429a443fb40f1afdbe02e9f9cbdf9 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-06e429a443fb40f1afdbe02e9f9cbdf92025-08-20T02:34:01ZengMDPI AGRemote Sensing2072-42922025-05-011710170210.3390/rs17101702High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network DataSeungwoo Baek0Soorok Ryu1Choeng-Lyong Lee2Francisco J. Tapiador3Gyuwon Lee4BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric Remote Sensing (CARE), Kyungpook National University, Daegu 41566, Republic of KoreaBK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric Remote Sensing (CARE), Kyungpook National University, Daegu 41566, Republic of KoreaBK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric Remote Sensing (CARE), Kyungpook National University, Daegu 41566, Republic of KoreaDepartment of Environmental Sciences, Institute of Environmental Sciences (ICAM), Earth and Space Science (ESS) Research Group, University of Castilla-La Mancha, 45071 Toledo, SpainBK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric Remote Sensing (CARE), Kyungpook National University, Daegu 41566, Republic of KoreaSatellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation estimates but suffer from lower temporal resolution due to their limited observation frequency. This study proposes an efficient algorithm for integrating and enhancing precipitation estimates from multiple satellite observations. The target domain includes the Full Disk (FD) and the extended East Asia (EA) regions, both of which are observable by GEO satellites, such as Himawari-8, serving as the GEO platform in this study. The algorithm involves four steps: pre-data preparation, LEO morphing, adjustment, and final merging. It produces Early and Late composite products with 10-min temporal and up to 2 km spatial resolution and significantly reduces latency compared to IMERG. Specifically, the Early and Late products can be generated with approximate latencies of 90 min and 270 min, respectively—much faster than Integrated Multi-satellite Retrievals for GPM (IMERG)’s Early (4-h) and Late (14-h) products. A key feature of the proposed method is the use of accuracy-based weighting derived from radar-based validation, enabling dynamic merging that reflects the reliability of each satellite observation. Statistical validation using Global Telecommunication System (GTS) precipitation data confirmed the positive impact of the proposed bias correction and merging method. In particular, the Late product achieved accuracy comparable to or higher than that of IMERG Early and IMERG Late, despite its significantly shorter latency. However, its accuracy was still lower than that of IMERG Final, which benefits from additional gauge-based correction but is released with a delay of several months.https://www.mdpi.com/2072-4292/17/10/1702Geostationary Orbit (GEO)Low Earth Orbit (LEO)GPMIMERGsatellite precipitation |
| spellingShingle | Seungwoo Baek Soorok Ryu Choeng-Lyong Lee Francisco J. Tapiador Gyuwon Lee High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network Data Remote Sensing Geostationary Orbit (GEO) Low Earth Orbit (LEO) GPM IMERG satellite precipitation |
| title | High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network Data |
| title_full | High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network Data |
| title_fullStr | High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network Data |
| title_full_unstemmed | High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network Data |
| title_short | High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network Data |
| title_sort | high resolution low latency multi satellite precipitation merging by correcting with weather radar network data |
| topic | Geostationary Orbit (GEO) Low Earth Orbit (LEO) GPM IMERG satellite precipitation |
| url | https://www.mdpi.com/2072-4292/17/10/1702 |
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