Establishment and analysis of a refinement method for the GNSS empirical weighted mean temperature model

doi:10.11947/j.AGCS.2022.20210269

Abstract

Abstract: The weighted mean temperature (T_m) as a key parameter for the conversion of tropospheric wet delay to precipitable water vapor, plays an important role in the field of GNSS meteorology. Several empirical T_m models were established, which can provide T_m estimates by using the location and time information of the site as input parameters. However, the accuracy of these models is often limited, especially in some local areas. This paper proposed a refinement method for the empirical models, which introduced surface temperature, obtained the refined coefficient by using least squares and achieved the error compensation effect for estimating T_m. Based on the 2011—2015 data of 180 radiosonde sites in China and its nearby regions, this paper carried out the establishment and analysis of the GPT3 refined model. Numerical results show that the GPT3 refined model outperformed the other three models, and improved the T_m accuracy by 16.2%, 13.5% and 21.1% compared with the Bevis model, regional linear model and GPT3 model, respectively. In addition, the T_m estimated by the GPT3 refined model appeared the best spatio-temporal distribution, which significantly improved the accuracy of T_m estimated by other models in high latitudes, and effectively solved the defect that the GPT3 model can only describe the seasonal variation of T_m. The formula of the proposed method is simple, which can be quickly extended to any empirical T_m model.

Key words: GNSS meteorology, weighted mean temperature, GPT3 model, precipitable water vapor

CLC Number:

P228

YANG Fei, GUO Jiming, CHEN Ming, ZHANG Di. Establishment and analysis of a refinement method for the GNSS empirical weighted mean temperature model[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(11): 2339-2345.

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