Characterization of Surface Pressure Accuracy and Spatial and Temporal Variations of The GPT3 And HGPT2 Models in The Chinese Region

Abstract

The accuracy of surface pressure is crucial for obtaining high-precision zenith hydrostatic delay(ZHD). In order to verify the accuracy of the Global Pressure and Temperature model (HGPT2 ) and the Global Pressure and Temperature (GPT3) in obtaining ground air pressure in China, as well as the spatiotemporal variation characteristics of the errors of the two models, this paper uses the ERA5 reanalysis dataset and combines it with actual meteorological data from weather stations to evaluate their performance. The results show that the results show that the HGPT2 model has better accuracy in simulating atmospheric pressure in China than the GPT3 model, with atmospheric pressure bias of -0.54 hPa and -2.09 hPa, and RMS of 4.42 hPa and 4.91 hPa, respectively. This article analyzes the spatial and temporal characteristics of model errors based on China's climate types. The performance of the GPT3 and HGPT2 models varies in different regions, and their accuracy in simulating atmospheric pressure changes is poorer in northern and northwestern China.