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 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.