Real-Time High-Resolution Global PWV Retrieval Based on Weather Forecast Foundation Models and Cross-Validation With Radiosonde, GNSS, and ERA5

Real-Time High-Resolution Global PWV Retrieval Based on Weather Forecast Foundation Models and Cross-Validation With Radiosonde, GNSS, and ERA5

High-quality precipitable water vapor (PWV) plays a vital role in climate change and weather prediction studies. This research introduces a novel scheme for retrieving high-resolution surface-domain PWV with real-time and forecasting capabilities with global coverage, utilizing weather forecast foun...

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Bibliographic Details
Main Authors: Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
High resolution
precipitable water vapor (PWV)
real time
surface-domain
weather forecast foundation models
Online Access:https://ieeexplore.ieee.org/document/11039644/
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Summary:High-quality precipitable water vapor (PWV) plays a vital role in climate change and weather prediction studies. This research introduces a novel scheme for retrieving high-resolution surface-domain PWV with real-time and forecasting capabilities with global coverage, utilizing weather forecast foundation models represented by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. The accuracy of the new scheme is cross-validated against PWVs from radiosondes, Global Navigation Satellite Systems (GNSSs), and the fifth generation ECMWF reanalysis (ERA5). Results show the new scheme achieves 3.01 mm global root mean square error in real time, and the value reduce to 2.25 mm when focusing only on land areas, which is more accurate than most existing methods that rely on postprocessed surface-domain data. The poor accuracy in low-latitude and midlatitude ocean regions limits the accuracy of the new scheme and future integration of GNSS PWV data from ocean sources is expected to improve it. Overall, the proposed scheme demonstrates very satisfactory global PWV accuracy and has the potential for further improvement with the development of artificial intelligence.
ISSN:1939-1404
2151-1535

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