The Modeling Of Key Parameters In Ground-based GNSS Water Vapor Inversion And Its Application To Typhoons

PWV（Precipitable Water Vapor）is an important indicator of relevant weather in the lower atmosphere and an essential factor in the formation and evolution of various weather extremes（e.g.,typhoons,heavy rainfall,strong winds,ocean waves,and storm surges）,and plays an important role in the global water cycle and energy balance.Ground-based GNSS（Global Navigation Satellite System）inverted PWV has the advantages of high spatial and temporal resolution,low cost,high accuracy,and all-weather compared with traditional water vapor detection methods,and provides a new method for the detection of precipitable Water Vapor,which can meet all the current needs of water vapor detection.Recently,it has become a hot spot for domestic and international research to apply high-precision PWV products to predict extreme weather such as typhoons,rainstorms,strong winds,waves,and storm surges.In this research context,this paper conducts a study on the modeling of key parameters of ground-based GNSS water vapor inversion and its application in typhoons.The main research and conclusions of this paper are as follows:1.To address the problem that the empirical model of atmospheric weighted mean temperature（Tm）has not taken into account the relationship between Tm and multiple meteorological factors due to the vast size of China’s region,variable climate,and large north-south fluctuations,the empirical model of Tm was refined using radiosonde data from2015-2017,and a refined model of Tm that takes into account the influence of multiple factors was established.The new model reduced the RMS by 32.8%,39.1%,and 29.2%compared to the GPT3w-1 model（1°×1°grid）,GPT3w-5 model（5°×5°grid）,and Bevis model,respectively.The annual average RMS and BIAS of the new model were 3.15 K and0.04 K,respectively.2.A random forest algorithm-based atmospheric weighted average temperature model(RF_Tm model)is proposed to improve the accuracy of Tm in the Chinese region in response to the problem that the current mainstream models cannot fit the nonlinear relationship between Tm and meteorological and temporal factors well and have limited accuracy.The annual average RMS and BIAS of the RF_Tm model were 2.87 K and 0.13 K,respectively,and their annual average RMS were reduced by 35.5%,38.8%,and 44.7%compared with the Bevis,GPT3-1,and GPT3-5 models,respectively.The stability and applicability of the RF_Tm model at different latitudes,elevations,and times are better than the GPT3 and Bevis models.3.Using the data provided by the sounding station,the surface RS PWV is obtained by the integration method,and the accuracy of the surface ERA5 PWV obtained by different PWV empirical vertical correction models is compared and analyzed with the RS PWV as the reference value.At the same time,the accuracy of GNSS PWV obtained by inversion is examined with the reference of PWV of sounding stations co-located with GNSS stations,and its RMS is 2.95 mm,which can meet the accuracy threshold of PWV in weather short-time forecasting.4.The anomalous variations of PWV,pressure（P）,precipitation,and wind speed during typhoons Mangkhut,Barijat,Hato,and Pakhar were discussed using the PWV and atmospheric parameters derived from ERA5.Finally,we summarized the critical factors that respond to the typhoon movement,PWV and P,and developed an improved multi-factor typhoon monitoring mode（IMTM）.The model consists of two models:the IMTM-I model is used when only the Numerical Weather Prediction（NWP）information is available for the region,and the IMTM-II model is used if the density of GNSS observation sites in the region is high.Using the typhoon movement speed provided by the China Meteorological Observatory Typhoon Network（CMOTN）as the reference value:the IMTM-Ⅰmodel improves the RMS by 26.3%/38.5%compared with the P/PWV-based single-factor typhoon movement model,respectively.The estimated typhoon movement speed and residuals of the IMTM-Ⅱmodel are 31.35 km/h and 0.35 km/h,respectively.The IMTM model is overall better than the traditional single-factor model and has better agreement with the movement velocities of Hato and Mangkhut provided by the China Meteorological Typhoon Network,which is expected to provide a complementary tool to the traditional typhoon monitoring.