Application Of GNSS Precise Point Positioning Water Vapor Inversion In Typhoon Monitoring

Chongming is located at the mouth of the Yangtze River,and its unique geographical position makes it important in the development of Shanghai and even the Yangtze River Delta region,but also makes it vulnerable to typhoons and other extreme weather events.Precipitable Water Vapor(PWV)is an important indicator for typhoon monitoring.The Global Navigation Satellite System(GNSS)is capable of inversion of atmospheric precipitable water vapor,which has obvious advantages of high accuracy and high spatial and temporal resolution compared with traditional detection methods such as radio soundings and satellite remote sensing.Precise Point Positioning(PPP)has high operational flexibility,simple model,and high practicality,and can obtain high accuracy Zenith Tropospheric Delay(ZTD)without joint measurement with known points.In this paper,we conduct a research on small-scale regional typhoon monitoring based on PPP PWV with Chongming region as an example.The main work includes three aspects of PPP multipath error correction,construction of regional weighted average temperature model and application of PPP PWV in typhoon monitoring,as follows:(1)The Multipath Hemispherical Map(MHM)is applied to the inversion of PPP ZTD to obtain higher accuracy ZTD,and the multipath correction speeds up the convergence of PPP and improves the accuracy of ZTD inversion.Based on the inversion accuracy of the ZTD of the continuously operating reference station in Hong Kong during the typhoon,the evaluation results show that the root mean square error of the PPP ZTD of the Hong Kong CORS station is reduced by 0.43 mm after the multi-path correction compared with that of the PPP ZTD without the multi-path correction.The root mean square error is improved by 22.9%.(2)A high-precision,high temporal resolution weighted average temperature(_mT)model for Chongming region is constructed.Based on the traditional_mT seasonal model based on spectral analysis,the accuracy of the_mT model is improved by introducing sparse kernel learning to model its residuals.The validation results show that compared with the seasonal model,the sparse kernel learning_mT accuracy can be improved by about 66.2%when ERA5_mT data is used to assess the accuracy,and by about 48.7%when sounding_mT data is used to assess the accuracy.(3)Based on the observation data of Chongming CORS station in 2021-2022,the inversions of"In-fa","Chanthu","Songda","Hinnamnor"and"Muifa",and analyze the variation characteristics of PWV and various meteorological parameters(temperature,pressure and rainfall)during typhoons to investigate the warning signals during typhoons.The PWV time series are analyzed to investigate the warning signals during typhoons.A typhoon motion model is constructed based on the peak time difference of PWV at each station,and the typhoon path and speed capture in small-scale regions are realized.The typhoon speed released by China Typhoon Network is chosen as the reference value,and the maximum difference between the real speed of typhoon movement and the calculated average speed is less than 3 km/h.