Improved tropospheric delay model for China using RBF neural network and meteorological data

doi:10.11947/j.AGCS.2022.20210480

Abstract

Abstract: Single-level meteorological products (ERA5 single-level data and measured meteorological parameters) and multi-level meteorological products (ERA5 pressure level data and COSMIC data) are used to estimate the ZTD of 236 CMONOC stations,namely ERA5S_ZTD,MET_ZTD,ERA5P_ZTD and RO_ZTD,based on the model method and the integration method respectively.Four ZTD estimations are evaluated with the reference of GNSS_ZTD;the results show that the average monthly RMSE are 42.8,53.6,16.1 and 62.3 mm,respectively.The accuracy of ERA5P_ZTD estimated by the integral method is the highest,that of ERA5S_ZTD and MET_ZTD calculated by the model method are the next.Estimating RO_ZTD with the integral method has the lowest accuracy.In order to further improve the accuracy of ZTD estimations,improved model of tropospheric delay is proposed with the RBF neural network in this paper.The calculation results show that:the average monthly RMSE between the ZTD from four improved models and GNSS_ZTD are 23.5,32.1,14.2 and 40.8 mm,which are reduced 43.4%,36.3%,10.0% and 34.4% than raw ZTD estimations.The overall modified effect of the improved model is obvious,and the improvement rate is related to the density of station distribution.

Key words: GNSS, RBF neural network, ERA5, COSMIC, tropospheric delay

CLC Number:

P228

XU Tianhe, LI Song, WANG Shuaimin, JIANG Nan. Improved tropospheric delay model for China using RBF neural network and meteorological data[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(8): 1690-1707.

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