Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)
The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atm...
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| Published in | Remote sensing (Basel, Switzerland) Vol. 15; no. 2; p. 498 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.01.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2072-4292 2072-4292 |
| DOI | 10.3390/rs15020498 |
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| Abstract | The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other. |
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| AbstractList | The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other. |
| Author | Liu, Zijing Zhu, Linyan Di, Di Zhou, Ronglian Bai, Wenguang |
| Author_xml | – sequence: 1 givenname: Linyan surname: Zhu fullname: Zhu, Linyan – sequence: 2 givenname: Ronglian surname: Zhou fullname: Zhou, Ronglian – sequence: 3 givenname: Di orcidid: 0000-0003-0475-9690 surname: Di fullname: Di, Di – sequence: 4 givenname: Wenguang surname: Bai fullname: Bai, Wenguang – sequence: 5 givenname: Zijing surname: Liu fullname: Liu, Zijing |
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| Copyright | 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | 1DVAR Accuracy AHI Algorithms Atmospheric moisture Atmospheric water case studies Computational efficiency Computer applications Computing time cost effectiveness Cyclones Datasets Humidity Information retrieval Machine learning Meteorological satellites Moisture effects moisture retrieval Multilayers Precipitation Rain random forest Remote sensing satellites Sea level Spatial discrimination Spatial resolution Synchronous satellites Temperature Typhoons Water vapor Weather forecasting |
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| Title | Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808) |
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