New Models of Zenith Tropospheric Delay for Chinese Mainland and Surrounding Areas Based on Convolutional Neural Network and Random Forest

• Published in: IEEE access Vol. 12; pp. 112864 - 112880
• Main Authors: Zhang, Jiahao, Liang, Qin, Huang, Yunqing
• Format: Journal Article
• Language: English
• Published: IEEE 2024
• Subjects: Atmospheric modeling; Chinese mainland and surrounding areas; Convolutional neural network; Convolutional neural networks; Data models; Feature extraction; Forecasting; GPT3 model; Meteorology; Predictive models; random forest; Random forests; Saastamoinen model; Weather forecasting; zenith tropospheric delay
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3441331

Accurate models of zenith tropospheric delay (ZTD) is crucial in meteorology as well as in navigation and positioning. In this study, we employ Convolutional Neural Network (CNN) and Random Forest (RF) models to establish six direct or compensation models for estimating ZTD in Chinese mainland and surrounding areas. The modeling process utilizes ZTD data from 205 stations spanning the period 2013 to 2018. Model validity is assessed using ZTD data from 202 stations in 2019. Comparative analysis, considering the overall Root Mean Square Error (RMSE), is conducted between these newly proposed CNN/RF-based models and Saastamoinen, A&N, GPT3 and RF-based models constructed by the methods presented in the previous study (ZTD-RF1, ZTD-RF3). The results demonstrate the superiority of the six CNN/RF-based models over the previously proposed models. In general, compensation models exhibit an improvement over direct models, and models incorporating meteorological parameterisation outperform models without such parameterisation. When the meteorological data are available, our proposed model provided a good representation of the instability of water vapour pressure in the ZTD, especially in monsoon climates. The optimal model is identified as the RF-based compensation model (ZTD-RF4). The ZTD-RF4 model achieves an overall RMSE of 3.24 cm, representing a 29.47% reduction of the RMSE compared to the Saastamoinen model (4.60 cm), a 26.75% reduction compared to the A&N model (4.43 cm), and slightly superior to the ZTD-RF3 model (3.28 cm). When the meteorological data are unavailable, the optimal choice is the CNN-based compensation model (ZTD-CNN2), which exhibits an overall RMSE of 4.21 cm, indicating a 7.89% reduction compared to the GPT3 model (4.57 cm) and significantly superior to the ZTD-RF1 model (4.34 cm). In contrast to current machine learning (ML)-based ZTD calculation models, we introduce the idea of compensation based on traditional models and a new CNN structure is constructed, which all proved to be capable of better performance in ZTD modeling.