A Novel Method for Forecasting Global High-Resolution Precipitable Water Vapor With the Pangu-Weather System
High-accuracy, real-time precipitable water vapor (PWV) products with high spatiotemporal resolution are of great significance for precipitation forecasting, extreme weather monitoring, and early warning. Global reanalysis data and global navigation satellite system (GNSS) face limitations in provid...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11015992/ |
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| author | Liangke Huang Zhouao Zheng Ge Zhu Haohang Bi Haojun Li Lilong Liu |
| author_facet | Liangke Huang Zhouao Zheng Ge Zhu Haohang Bi Haojun Li Lilong Liu |
| author_sort | Liangke Huang |
| collection | DOAJ |
| description | High-accuracy, real-time precipitable water vapor (PWV) products with high spatiotemporal resolution are of great significance for precipitation forecasting, extreme weather monitoring, and early warning. Global reanalysis data and global navigation satellite system (GNSS) face limitations in providing real-time, high spatiotemporal resolution PWV products owing to the latency of reanalysis data and the uneven distribution of GNSS stations. To overcome these challenges, a novel PWV retrieval approach was developed. The Huawei Cloud Pangu-Weather system was introduced to predict hourly real-time PWV globally, namely Pangu PWV, with a horizontal resolution of 0.25° from 2018 to 2020. The estimation result of Pangu PWV was evaluated using PWV dataset from 540 radiosonde stations and 12 292 global positioning system (GPS) stations worldwide. In this work, the gridded PWV product from the modern-era retrospective analysis for research and applications, version 2 (MERRA-2) was incorporated for comparison. To mitigate evaluation uncertainties, the previously developed global PWV vertical correction model was employed to adjust the gridded PWV for both products. The mean bias and root-mean-square error (RMSE) for MERRA-2 and Pangu PWV are −2.59/0.59 mm and 3.67/3.67 mm against radiosonde records, respectively, and mean bias and RMSE are −2.20/−0.01 mm and 3.17/3.37 mm against GPS PWV data, respectively. In addition, Pangu PWV has close performance to MERRA-2 PWV, further demonstrating that Pangu PWV is well suited for meteorological applications. Besides, the performance of the two products was tested under extreme weather conditions in a specific region. The mean bias and RMSE for MERRA-2 and Pangu PWV are −6.14/0.19 mm and 7.05/3.76 mm, respectively, indicating that the Pangu PWV still yields stable performance during extreme weather events. Thus, the Pangu PWV, exhibiting the merits, such as real time, great precision, and high spatiotemporal resolution on a global scale, holds great potential for applications in extreme weather monitoring and precipitation forecasting. |
| format | Article |
| id | doaj-art-21a3b30e957e4e70a42ac93bbcd225b7 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-21a3b30e957e4e70a42ac93bbcd225b72025-08-20T02:21:34ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118148431485410.1109/JSTARS.2025.357400611015992A Novel Method for Forecasting Global High-Resolution Precipitable Water Vapor With the Pangu-Weather SystemLiangke Huang0https://orcid.org/0000-0002-4241-3730Zhouao Zheng1https://orcid.org/0009-0005-8349-8565Ge Zhu2https://orcid.org/0000-0001-5215-3488Haohang Bi3Haojun Li4https://orcid.org/0009-0002-7537-2223Lilong Liu5College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaTongji University, Shanghai, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaHigh-accuracy, real-time precipitable water vapor (PWV) products with high spatiotemporal resolution are of great significance for precipitation forecasting, extreme weather monitoring, and early warning. Global reanalysis data and global navigation satellite system (GNSS) face limitations in providing real-time, high spatiotemporal resolution PWV products owing to the latency of reanalysis data and the uneven distribution of GNSS stations. To overcome these challenges, a novel PWV retrieval approach was developed. The Huawei Cloud Pangu-Weather system was introduced to predict hourly real-time PWV globally, namely Pangu PWV, with a horizontal resolution of 0.25° from 2018 to 2020. The estimation result of Pangu PWV was evaluated using PWV dataset from 540 radiosonde stations and 12 292 global positioning system (GPS) stations worldwide. In this work, the gridded PWV product from the modern-era retrospective analysis for research and applications, version 2 (MERRA-2) was incorporated for comparison. To mitigate evaluation uncertainties, the previously developed global PWV vertical correction model was employed to adjust the gridded PWV for both products. The mean bias and root-mean-square error (RMSE) for MERRA-2 and Pangu PWV are −2.59/0.59 mm and 3.67/3.67 mm against radiosonde records, respectively, and mean bias and RMSE are −2.20/−0.01 mm and 3.17/3.37 mm against GPS PWV data, respectively. In addition, Pangu PWV has close performance to MERRA-2 PWV, further demonstrating that Pangu PWV is well suited for meteorological applications. Besides, the performance of the two products was tested under extreme weather conditions in a specific region. The mean bias and RMSE for MERRA-2 and Pangu PWV are −6.14/0.19 mm and 7.05/3.76 mm, respectively, indicating that the Pangu PWV still yields stable performance during extreme weather events. Thus, the Pangu PWV, exhibiting the merits, such as real time, great precision, and high spatiotemporal resolution on a global scale, holds great potential for applications in extreme weather monitoring and precipitation forecasting.https://ieeexplore.ieee.org/document/11015992/Global navigation satellite system (GNSS)modern-era retrospective analysis for research and applicationsversion 2 (MERRA-2) PWVPangu weatherprecipitable water vapor (PWV) |
| spellingShingle | Liangke Huang Zhouao Zheng Ge Zhu Haohang Bi Haojun Li Lilong Liu A Novel Method for Forecasting Global High-Resolution Precipitable Water Vapor With the Pangu-Weather System IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Global navigation satellite system (GNSS) modern-era retrospective analysis for research and applications version 2 (MERRA-2) PWV Pangu weather precipitable water vapor (PWV) |
| title | A Novel Method for Forecasting Global High-Resolution Precipitable Water Vapor With the Pangu-Weather System |
| title_full | A Novel Method for Forecasting Global High-Resolution Precipitable Water Vapor With the Pangu-Weather System |
| title_fullStr | A Novel Method for Forecasting Global High-Resolution Precipitable Water Vapor With the Pangu-Weather System |
| title_full_unstemmed | A Novel Method for Forecasting Global High-Resolution Precipitable Water Vapor With the Pangu-Weather System |
| title_short | A Novel Method for Forecasting Global High-Resolution Precipitable Water Vapor With the Pangu-Weather System |
| title_sort | novel method for forecasting global high resolution precipitable water vapor with the pangu weather system |
| topic | Global navigation satellite system (GNSS) modern-era retrospective analysis for research and applications version 2 (MERRA-2) PWV Pangu weather precipitable water vapor (PWV) |
| url | https://ieeexplore.ieee.org/document/11015992/ |
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