A predicting ZWD model based on multi-source data and generalized regression neural network

doi:10.11947/j.AGCS.2023.20220084

Abstract

Abstract: Tropospheric wet delay is a more difficult part of GNSS error sources to be corrected. Most of the approved empirical models of zenith wet delay (ZWD) are based on single-source data (i.e. radiosonde data or reanalysis data), and the variation patterns of ZWD on different scales are characterized by preset model functions, so it is difficult to accurately describe the nonlinearly complex variations of ZWD on different scales, and the accuracy needs to be further improved. To address this issue, a predicting ZWD model is constructed based on multi-source data with higher spatiotemporal resolution and a generalized regression neural network (GRNN) with strong nonlinear approximation capability. Firstly, grid ZWD of two different data sources is optimized and downsampled by a GRNN model, and merged with radiosonde ZWD to obtain high-quality ZWD dataset. Then, the input and the output vectors of the GRNN training model is designed according to the characteristics that ZWD is greatly affected by time and space and the characteristics of machine learning. Finally, a posteriori optimization method is used to determine the model parameters, and then the optimal forecasting model is obtained. Validated by the radiosonde ZWD, in comparison with the approved empirical GPT2w model and the single-source (i.e. radiosonde) data model with the same method, the accuracy of the proposed model is improved by 25.3% and 11.1% respectively in terms of RMS. And the accuracy of the proposed model has good spatiotemporal stability. In addition, the computational efficiency and PPP application experimental results show that the computational efficiency of the proposed model can meet the needs of GNSS real-time applications, and the improvement effect of PPP is better than that of GPT2w.The proposed model obtains high ZWD forecasting accuracy without setting the model function, which provides an idea for ZWD modeling.

Key words: tropospheric wet delay, multi-source data, GRNN, nonlinear approximation, prediction

CLC Number:

P228

LI Junyu, YAO Yibin, LIU Lilong, ZHANG Bao, HUANG Liangke, CAO Liying. A predicting ZWD model based on multi-source data and generalized regression neural network[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(9): 1492-1503.

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