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Remote Sensing
Volume 16
Issue 18
10.3390/rs16183522
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Article

Exploration of Deep-Learning-Based Error-Correction Methods for Meteorological Remote-Sensing Data: A Case Study of Atmospheric Motion Vectors

by
Hang Cao
1,†,
Hongze Leng
1,*,†,
Jun Zhao
1,
Xiaodong Xu
2,
Jinhui Yang
1,
Baoxu Li
1,
Yong Zhou
1 and
Lilan Huang
1
1
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
2
Guangxi Academy of Sciences Nanning, Nanning 530007, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2024, 16(18), 3522; https://doi.org/10.3390/rs16183522
Submission received: 11 July 2024 / Revised: 10 September 2024 / Accepted: 20 September 2024 / Published: 23 September 2024
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Abstract

Meteorological satellite remote sensing is important for numerical weather forecasts, but its accuracy is affected by many things during observation and retrieval, showing that it can be improved. As a standard way to measure wind from space, atmospheric motion vectors (AMVs) are used. They are separate pieces of information spread out in the troposphere, which gives them more depth than regular surface or sea surface wind measurements. This makes rectifying problems more difficult. For error correction, this research builds a deep-learning model that is specific to AMVs. The outcomes show that AMV observational errors are greatly reduced after correction. The root mean square error (RMSE) drops by almost 40% compared to ERA5 true values. Among these, the optimization of solar observation errors exceeds 40%; the discrepancies at varying atmospheric pressure altitudes are notably improved; the degree of optimization for data with low QI coefficients is substantial; and there remains potential for enhancement in data with high QI coefficients. Furthermore, there has been a significant enhancement in the consistency coefficient of the wind’s physical properties. In the assimilation forecasting experiments, the corrected AMV data demonstrated superior forecasting performance. With more training, the model can fix things better, and the changes it makes last for a long time. The results show that it is possible and useful to use deep learning to fix errors in meteorological remote-sensing data.
Keywords: deep learning; atmospheric motion vectors; error correction; data assimilation; meteorological remote sensing deep learning; atmospheric motion vectors; error correction; data assimilation; meteorological remote sensing

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MDPI and ACS Style

Cao, H.; Leng, H.; Zhao, J.; Xu, X.; Yang, J.; Li, B.; Zhou, Y.; Huang, L. Exploration of Deep-Learning-Based Error-Correction Methods for Meteorological Remote-Sensing Data: A Case Study of Atmospheric Motion Vectors. Remote Sens. 2024, 16, 3522. https://doi.org/10.3390/rs16183522

AMA Style

Cao H, Leng H, Zhao J, Xu X, Yang J, Li B, Zhou Y, Huang L. Exploration of Deep-Learning-Based Error-Correction Methods for Meteorological Remote-Sensing Data: A Case Study of Atmospheric Motion Vectors. Remote Sensing. 2024; 16(18):3522. https://doi.org/10.3390/rs16183522

Chicago/Turabian Style

Cao, Hang, Hongze Leng, Jun Zhao, Xiaodong Xu, Jinhui Yang, Baoxu Li, Yong Zhou, and Lilan Huang. 2024. "Exploration of Deep-Learning-Based Error-Correction Methods for Meteorological Remote-Sensing Data: A Case Study of Atmospheric Motion Vectors" Remote Sensing 16, no. 18: 3522. https://doi.org/10.3390/rs16183522

APA Style

Cao, H., Leng, H., Zhao, J., Xu, X., Yang, J., Li, B., Zhou, Y., & Huang, L. (2024). Exploration of Deep-Learning-Based Error-Correction Methods for Meteorological Remote-Sensing Data: A Case Study of Atmospheric Motion Vectors. Remote Sensing, 16(18), 3522. https://doi.org/10.3390/rs16183522

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