{"title":"A High-Precision Real-Time PWV Grid Model for the China Region and Its Preliminary Performance in WRF Assimilation","authors":"Pengfei Xia;Biyan Chen;Ning Huang;Xin Xie;Qinglan Zhang","doi":"10.1109/JSTARS.2025.3525770","DOIUrl":null,"url":null,"abstract":"Precipitable water vapor (PWV) is a key parameter in studying water vapor variations during severe weather phenomena. The high-quality PWV maps are also of significant value for monitoring and early warning of geological disasters, such as landslides and debris flows. This study presents a high-precision real-time PWV grid model for the China region, utilizing global navigation satellite system (GNSS) observations and surface meteorological data. The model addresses the limitations of existing PWV retrieval methods by incorporating an improved altitude correction model for pressure and temperature using ERA5 reanalysis data. The model achieves a spatial resolution of 0.5° × 0.5° and incorporates real-time updates for accurate monitoring of atmospheric moisture variations. The model's performance was evaluated using surface meteorological observations and compared with the HGPT2 model. Results showed that the new model outperforms HGPT2 in terms of accuracy, particularly in low-latitude regions. In addition, the model was successfully assimilated into the weather research and forecasting (WRF) model, significantly improving the accuracy of the initial atmospheric field for numerical weather prediction. This study demonstrates the potential of GNSS and surface meteorological data in constructing high-resolution, real-time PWV models. The developed model provides valuable insights into atmospheric moisture variations and enhances the accuracy of weather forecasting and climate research in the China region.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3433-3447"},"PeriodicalIF":4.7000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824939","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10824939/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Precipitable water vapor (PWV) is a key parameter in studying water vapor variations during severe weather phenomena. The high-quality PWV maps are also of significant value for monitoring and early warning of geological disasters, such as landslides and debris flows. This study presents a high-precision real-time PWV grid model for the China region, utilizing global navigation satellite system (GNSS) observations and surface meteorological data. The model addresses the limitations of existing PWV retrieval methods by incorporating an improved altitude correction model for pressure and temperature using ERA5 reanalysis data. The model achieves a spatial resolution of 0.5° × 0.5° and incorporates real-time updates for accurate monitoring of atmospheric moisture variations. The model's performance was evaluated using surface meteorological observations and compared with the HGPT2 model. Results showed that the new model outperforms HGPT2 in terms of accuracy, particularly in low-latitude regions. In addition, the model was successfully assimilated into the weather research and forecasting (WRF) model, significantly improving the accuracy of the initial atmospheric field for numerical weather prediction. This study demonstrates the potential of GNSS and surface meteorological data in constructing high-resolution, real-time PWV models. The developed model provides valuable insights into atmospheric moisture variations and enhances the accuracy of weather forecasting and climate research in the China region.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.