دورية أكاديمية
Retrieving Precipitable Water Vapor Over Land From Satellite Passive Microwave Radiometer Measurements Using Automated Machine Learning
| العنوان: | Retrieving Precipitable Water Vapor Over Land From Satellite Passive Microwave Radiometer Measurements Using Automated Machine Learning |
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| المؤلفون: | Xinran Xia, Disong Fu, Wei Shao, Rubin Jiang, Shengli Wu, Peng Zhang, Dazhi Yang, Xiangao Xia |
| المصدر: | Geophysical Research Letters, Vol 50, Iss 22, Pp n/a-n/a (2023) |
| بيانات النشر: | Wiley, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | precipitable water vapor, passive microwave, automated machine learning, AMSR‐2, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | Abstract Accurately retrieving precipitable water vapor (PWV) over wide‐area land surface remains challenging. Unlike passive infrared remote sensing, passive microwave (PMW) remote sensing provides almost all‐weather PWV retrievals. This study develops a PMW‐based land PWV retrieval algorithm using automated Machine learning (ML) (AutoML). Data from the Advanced Microwave Scanning Radiometer 2 serve as the main predictor variables and high‐quality Global Positioning System (GPS) PWV data as the target variable. Unprecedentedly large GPS training samples (over 50 million) from more than 12,000 stations worldwide are used to train the AutoML model. New predictors with clear physical mechanisms enable PWV retrieval over almost any land surface type, including snow cover and near open water. Validation shows good agreement between PWV retrievals and ground observations, with a root mean square error of 3.1 mm. This encouraging outcome highlights the potential of the algorithm for application with other PMW radiometers with similar wavelengths. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1944-8007 0094-8276 |
| Relation: | https://doaj.org/toc/0094-8276; https://doaj.org/toc/1944-8007 |
| DOI: | 10.1029/2023GL105197 |
| URL الوصول: | https://doaj.org/article/4ff843fd86b049978685d9a27c9dd4fc |
| رقم الأكسشن: | edsdoj.4ff843fd86b049978685d9a27c9dd4fc |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19448007 00948276 |
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| DOI: | 10.1029/2023GL105197 |