A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products

Abstract

Accurate tropospheric delay (TD) and weighted mean temperature (Tm) are important for Global Navigation Satellite System (GNSS) positioning and GNSS meteorology. For this purpose, plenty of empirical models have been built to provide estimates of TD and Tm. However, these models cannot resolve TD and Tm variations at synoptic timescales since they only model the average annual, semi-annual, and/or daily variations. As a result, the existed empirical models cannot perform well under extreme weather conditions. To address this limitation, we propose to estimate Zenith Hydrostatic Delay (ZHD), Zenith Wet Delay (ZWD), and Tm directly from the stratified numerical weather forecasting products of the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_MESO) of China. The GRAPES_MESO forecasting data has a temporal resolution of 3 h, which provides the opportunity to resolve the synoptic variation. However, it is found that the estimated ZWD and Tm exhibit apparent systematic deviation from in situ observation-based estimates, which is due to the inherent biases in the GRAPES_MESO data. To solve this problem, we propose to correct these biases using a linear model and a spherical cap harmonic model. The estimates after correction are termed as the “CTropGrid” products. When validated by the radiosonde data, the CTropGrid product has biases of 1.5 mm, −0.7 mm, and −0.1 K, and Root Mean Square (RMS) error of 8.9 mm, 20.2 mm, and 1.5 K for ZHD, ZWD, and Tm. Compared to the widely used GPT2w model, the CTropGrid products have improved the accuracies of ZHD, ZWD, and Tm by 11.9%, 55.6%, and 60.5% in terms of RMS. When validating the Zenith Tropospheric Delay (ZTD) products (the sum of ZHD and ZWD) using the IGS ZTD data, the CTropGrid ZTD has a bias of −0.7 mm and an RMS of 35.8 mm, which is 22.7% better than the GPT2w model in terms of RMS. Besides the accuracy improvements, the CTropGrid products well model the synoptic-scale variations of ZHD, ZWD, and Tm. Compared to the existing empirical models that only capture the tidal (seasonal and/or diurnal) variations, the CTropGrid products capture well the non-tidal variations of ZHD, ZWD, and Tm, which enhances the tropospheric delay corrections and GNSS water vapor monitoring at synoptic timescales. Therefore, the CTropGrid product is an important progress in GNSS positioning and GNSS meteorology.

A New Approach for the Development of Grid Models Calculating Tropospheric Key Parameters over China

“…The height correction plays an important role in As mentioned above, temperature, T m , pressure, and water vapor pressure exhibit evident seasonal, latitudinal, and longitudinal characteristics over China, which should be taken into account to obtain a high-precision model. The equation in each window is expressed as follows: (10) where MP is the meteorological parameters, such as temperature, T m , pressure, and water vapor pressure; ϕ is the latitude; θ is the longitude; α 1 is the annual average value of the meteorological parameters; α 2 is the latitude correction; α 3 is the longitude correction; α 4 and α 5 are the annual amplitude coefficients of the meteorological parameters; α 6 and α 7 are the semiannual amplitude coefficients of the meteorological parameters, and DOY is the day of the year. The elevation of the grid points in the atmospheric reanalysis data is inconsistent with the elevation of GNSS stations.…”

“…Therefore, developing regional or global empirical tropospheric delay models based on atmosphere analysis data has attracted widespread attention [10][11][12][13][14][15][16]. Empirical meteorological models, such as the University of New Brunswick (UNB) models [17,18], the European Geo-stationary Navigation Overlay System (EGNOS) model [19], the TropGrid model [20,21], and the GPT models [22][23][24][25] have been developed, aimed at directly obtaining high-precision tropospheric parameters through the model without measured meteorological parameters.…”

A Novel Global Grid Model for Atmospheric Weighted Mean Temperature in Real-Time GNSS Precipitable Water Vapor Sounding

“…The model made significant improvements at high-altitude pressure levels. Cao et al [38] proposed to correct the bias of the GRAPES_MESO forecasting data using a linear model and a spherical cap harmonic model. Compared to the existing empirical models that only capture the tidal variations, the CTropGrid products capture well the non-tidal variations of Tm.…”

Editorial for the Special Issue ″Climate Modelling and Monitoring Using GNSS″

“…Several papers [1][2][3] discuss different aspects in dealing with the estimation of long-term GNSS-derived water vapor trends and intercomparisons with external sources and NWP models. Other papers [4] use GNSS-estimated tropospheric parameters to evaluate NWP models and use these parameters for building ZTD climatological [5][6][7] or precipitation [8,9] models. Paper [10] focuses on GNSS-radiooccultation-retrieved temperature and specific humidity profiles.…”