Improved Regional Atmospheric Weighted Mean Temperature Modeling Using a Decadal Dataset and Machine Learning Methods over China.
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| Title: | Improved Regional Atmospheric Weighted Mean Temperature Modeling Using a Decadal Dataset and Machine Learning Methods over China. |
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| Authors: | Hu, Zuquan1,2 (AUTHOR), Liang, Hong2,3,4 (AUTHOR) liangh@cma.gov.cn, Zhang, Peng2,3,4 (AUTHOR), Cao, Yunchang2,3,4 (AUTHOR), Zhao, Panpan2,3,4,5 (AUTHOR), Li, Xinxin5,6 (AUTHOR), Qu, Meifang1,6 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1925. 20p. |
| Subjects: | Random forest algorithms, Machine learning, Meteorology, Meteorological databases, Atmospheric temperature measurements, Atmospheric water vapor |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? Developing regional Tm empirical models using two machine learning methods and one-decade hourly ERA5 and various surface observations from meteorological sites substantially improves the accuracy of atmospheric weighted mean temperature estimates. The random forest model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the Temporal Mixture of Experts with Sequential Attention model achieves reductions of 40% and 33%, respectively, compared with the traditional linear model. What are the implications of the main findings? Comprehensively addressing the limitation incurred by the availability of surface meteorological measurements, the accuracy and coverage of Tm datasets used during modeling, and the diurnal, seasonal, and geographical variations of Tm. The proposed machine learning models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Developing regional Tm empirical models using two machine learning methods and one-decade hourly ERA5 and various surface observations from meteorological sites substantially improves the accuracy of atmospheric weighted mean temperature estimates. The random forest model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the Temporal Mixture of Experts with Sequential Attention model achieves reductions of 40% and 33%, respectively, compared with the traditional linear model. What are the implications of the main findings? Comprehensively addressing the limitation incurred by the availability of surface meteorological measurements, the accuracy and coverage of Tm datasets used during modeling, and the diurnal, seasonal, and geographical variations of Tm. The proposed machine learning models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121925 |
