Precise modeling of tropospheric delay and weighted mean temperature (T m ) is critical for Global Navigation Satellite System (GNSS) positioning and meteorology. However, the model data in previous models cover a limited time span, which limits the accuracy of these models. Besides, the vertical variations of tropospheric delay and T m are not perfectly modeled in previous studies, which affects the performance of height corrections. In this study, we used the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis from 1979 to 2017 to build a new empirical model. We first carefully modeled the lapse rates of tropospheric delay and T m . Then we considered the temporal variations by linear trends, annual, and semi-annual variations and the spatial variations by grids. This new model can provide zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and T m worldwide with a spatial resolution of 1 • × 1 • . We used the ECMWF ERA-Interim data and the radiosonde data in 2018 to validate this new model in comparison with the canonical GPT2w model. The results show that the new model has higher accuracies than the GPT2w model in all parameters. Particularly, this new model largely improves the accuracy in estimating ZHD and T m at high-altitude (relative to the grid point height) regions.

Tropospheric delay is an important error source in Global Navigation Satellite System (GNSS) positioning and can also be used in water vapor monitoring. Many models have been built to correct tropospheric delays or to convert zenith wet delays to precipitable water vapor. However, these models suffer from limited resolutions (spatial resolution lower than 1°and temporal resolution lower than 6 hr), which affects their performance. The release of European Centre for Medium-Range Weather Forecasts ReAnalysis 5 (ERA5) provides the opportunity to lift this limit. In this study, we use the ERA5 hourly 0.5°× 0.5°data to build a new model over China, which integrates tropospheric delay correction for GNSS positioning and weighted mean temperature calculation for GNSS meteorology. By modeling the diurnal variations of zenith hydrostatic delay, zenith wet delay, and weighted mean temperature and the seasonal variations in their lapse rates, this model has the state-of-the-art spatial resolution of 0.5°× 0.5°and temporal resolution of 1 hr. We validate this new model by the ERA5 data, the radiosonde data, and the GNSS data in comparison with the canonical GPT2w model. The results show that the new model has better accuracies in terms of root-mean-square than the GPT2w model in all parameters. Especially, the new model well captures the diurnal variations in tropospheric delay and weighted mean temperature. This new model provides accurate tropospheric delays and weighted mean temperature simultaneously, which enables GNSS receivers to measure precipitable water vapor directly and also benefits GNSS positioning.

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Pressure, temperature, and water vapor pressure are basic meteorological parameters that are frequently required in Global Navigation Satellite System (GNSS) positioning/navigation and GNSS meteorology. Although models like Global Pressure and Temperature (GPT) and Global Pressure and Temperature 2 wet (GPT2w) were developed for these demands, their spatial resolutions are lower than 0.75° and temporal resolutions are below 6 h, which limits their achievement. The publication of European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 hourly 0.25° × 0.25° data offers the opportunity to lift this limitation. In this work, the ERA5 surface data are used to evaluate the temporal variabilities of pressure, temperature, and water vapor pressure in the area of China. We characterize their diurnal variations using hourly data and take into account their geographical variations by 0.25° × 0.25° grids. In addition, we improve the height corrections for the three parameters employing the ERA5 pressure level data. Through these efforts, we build a new regional model named Chinese pressure, temperature, and water vapor pressure (CPTw), which has the advanced resolution of 0.25° × 0.25° and temporal resolution of 1 h. We evaluate the performance using ERA5 data and radiosonde data compared with the approved GPT2w model. Results demonstrate that the accuracies of the new model are superior to the GPT2w model in all meteorological parameters. The validation with the radiosonde data shows RMS for pressure, temperature, and water vapor pressure of the CPTw model is reduced by 14.1%, 25.8%, and 4.8%, compared with that of the GPT2w model. The new model catches especially well the diurnal changes in pressure, temperature, and water vapor pressure, which have never been realized before. Since the CPTw model can provide accurate empirical pressure, temperature, and water vapor pressure for any time and location in China and its surrounding areas, it can not only meet the need of empirical meteorological parameters in real-time geodetic applications like GNSS positioning and navigation, but it is also useful for GNSS meteorology.