A neural network method for estimating weighted mean temperature over China and adjacent areas

To improve the applicability of the global pressure and temperature 2 wet(GPT2w)model in estimating the weighted mean temperature in China and adjacent areas,the error compensation technology based on the neural network was proposed,and a total of 374800 meteorological profiles measured from 2006 to 2015 of 100 radiosonde stations distributed in China and adjacent areas were used to establish an enhanced empirical model for estimating the weighted mean temperature in this region.The data from 2016 to 2018 of the remaining 92 stations in this region was used to test the performance of the proposed model.Results show that the proposed model is about 14.9%better than the GPT2w model and about 7.6%better than the Bevis model with measured surface temperature in accuracy.The performance of the proposed model is significantly improved compared with the GPT2w model not only at different height ranges,but also in different months throughout the year.Moreover,the accuracy of the weighted mean temperature estimation is greatly improved in the northwestern region of China where the radiosonde stations are very rarely distributed.The proposed model shows a great application potential in the nationwide real-time ground-based global navigation satellite system(GNSS)water vapor remote sensing.

Empirical model for mean temperature and assessment of precipitable water vapor derived from GPS

The estimation of Precipitable Water Vapor （PWV） derived from Global Positioning System （GPS） data at the IGS site WUHN is assessed by comparing with PWV obtained from radiosonde data （No.57494） in Wuhan. The applicability of Saastamoinen （SAAS）, Hopfield and Black models used for estimating Zenith Hydrostatic Delay （ZHD） and Zenith Wet Delay （ZWD） and different models is verified in the estimation of GPS-derived PWV for the applied area. The experimental results demonstrated that ： 1 ） the precision of PWV estimated from Black model used for calculating ZHD （ ZHDs ） is lower than that of SAAS （ ZHDsAAs ） model and Hopfield model （ZHDn） with the RMS of 4. 16 ram; 2） the RMS of PWV estimated from SAAS model used for calculating ZWD （SAAS） is 3.78 ram; 3 ） the well-known Bevis model gives similar accuracy compared with the site-specific models for Tm in terms of surface temperature （ Ts ） and surface pressure （Ps）, which can reach the accuracy inside 1 mm in the GPS-derived PWV estimates.

Empirical T_m modeling in the region of Guangxi

This paper presents three strategies for modeling the regional empirical Tm （the weighted mean tem- perature of the atmosphere） to obtain more accurate determinations in a regional empirical model that is better adapted to the geographical and climatic characteristics of the applied area. The proposed models utilize data from four radiosonde stations in Guangxi, at Nanning, Guilin, Wuzhou and Baise, over an 11 month period （from Jan. to Nov. of 2011 ）. The experimental results demonstrated the following： （1） there is no significant

Analysis of the Weighted Mean Temperature of China Based on Sounding and ECMWF Reanalysis Data

Weighted mean temperature （Tin） is one of the most important conversion parameters for calculating precipitable water vapor by the signal path wet delay in ground-based GPS meteorology. This paper first discusses the Tm regression models for Hong Kong （HK） and the associated error statistics relative to the true values of Tm from the numerical method. The results show that there is little difference in precision between annual and seasonal Tm regression models for HK. The Bevis Tm-Ts （surface temperature） regression model is more suitable for northeastern China and the Qinghai-Tibetan Plateau than the local models. For areas lack of historical sounding data, the Kriging interpolation method and the ECMWF reanalysis product ERA-interim were employed to set up local Tm-Ts models. The results indicate that the Tm derived by the ERA-interim data coincides well with that by the sounding data, and the Kriging interpolation method can successfully obtain the coefficients of local Tm-Ts models, suggesting that these two approaches may serve as effective ways in the acquisition and localization of Tin.

Establishment and analysis of global gridded Tm-Ts relationship model

In ground-based GPS meteorology, Tm is a key parameter to calculate the conversion factor that can convert the zenith wet delay（ZWD） to precipitable water vapor（PWV）. It is generally acknowledged that Tm is in an approximate linear relationship with surface temperature Ts, and the relationship presents regional variation. This paper employed sliding average method to calculate correlation coefficients and linear regression coefficients between Tm and Ts at every 2°× 2.5° grid point using Ts data from European Centre for Medium-Range Weather Forecasts（ECMWF） and Tm data from ＂GGOS Atmosphere＂, yielding the grid and bilinear interpolation-based Tm Grid model. Tested by Tm and Ts grid data, Constellation Observation System of Meteorology, Ionosphere, and Climate（COSMIC） data and radiosonde data, the Tm Grid model shows a higher accuracy relative to the Bevis Tm-Ts relationship which is widely used nowadays. The Tm Grid model will be of certain practical value in high-precision PWV calculation.

THE APPROACH TO REMOTE SENSING OF WATER VAPOR BASED ON GPS AND LINEAR REGRESSION T_m IN EASTERN REGION OF CHINA

The approach to remote sensing of water vapor by using global positioning systems(GPS)is discussed.In order to retrieve the vertical integrated water vapor(IWV)or the precipitable water (PW),the weighted“mean temperature”of the atmosphere,T_m would be estimated to the specific area and season.T_m depends on surface temperature,tropospheric temperature profile,and the vertical distribution of water vapor.The surface temperature dependence is borne out by a comparison of T_m and the values of surface temperature T_s using radiosonde profiles of Beijing Station(No.54511)throughout 1992.The analysis of radiosonde profiles spanning a one-year interval(1992)from sites in eastern region of China with a latitude range of 20-50°N and a longitude range of 100-130°E yields the coefficients α and b of a linear regression equation T_m= α+bT_s.

Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin,China

Precipitable Water Vapor(PWV),as an important indicator of atmospheric water vapor,can be derived from Global Navigation Satellite System(GNSS)observations with the advantages of high precision and all-weather capacity.GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in mete-orology,particularly for severe weather conditions,for water vapor is not well sampled in the current meteorological observing systems.In this study,an empirical atmospheric weighted mean temperature(Tm)model for Guilin is estab-lished using the radiosonde data from 2012 to 2017.Then,the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017.The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square(RMS)of−0.51 and 2.12 K,respectively,compared with other widely used models.Moreover,the GNSS PWV estimates are validated with the data at Guilin radiosonde station.Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of−0.9 and 3.53 mm,respectively.Finally,an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed.It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements,and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall.It also reveals the moisture variation in several regions of Guilin during a heavy rainfall,which is significant for the moni-toring of rainfalls and weather forecast.