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Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)

Ensemble‐based data assimilation of radar observations across inner‐core regions of tropical cyclones (TCs) in tandem with satellite all‐sky infrared (IR) radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, int...

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Autores principales: Zhang, Yunji, Sieron, Scott B., Lu, Yinghui, Chen, Xingchao, Nystrom, Robert G., Minamide, Masashi, Chan, Man‐Yau, Hartman, Christopher M., Yao, Zhu, Ruppert, James H., Okazaki, Atsushi, Greybush, Steven J., Clothiaux, Eugene E., Zhang, Fuqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286819/
https://www.ncbi.nlm.nih.gov/pubmed/35865360
http://dx.doi.org/10.1029/2021GL096410
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author Zhang, Yunji
Sieron, Scott B.
Lu, Yinghui
Chen, Xingchao
Nystrom, Robert G.
Minamide, Masashi
Chan, Man‐Yau
Hartman, Christopher M.
Yao, Zhu
Ruppert, James H.
Okazaki, Atsushi
Greybush, Steven J.
Clothiaux, Eugene E.
Zhang, Fuqing
author_facet Zhang, Yunji
Sieron, Scott B.
Lu, Yinghui
Chen, Xingchao
Nystrom, Robert G.
Minamide, Masashi
Chan, Man‐Yau
Hartman, Christopher M.
Yao, Zhu
Ruppert, James H.
Okazaki, Atsushi
Greybush, Steven J.
Clothiaux, Eugene E.
Zhang, Fuqing
author_sort Zhang, Yunji
collection PubMed
description Ensemble‐based data assimilation of radar observations across inner‐core regions of tropical cyclones (TCs) in tandem with satellite all‐sky infrared (IR) radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all‐sky microwave (MW) radiances using Hurricane Harvey (2017) as an example. Assimilating Global Precipitation Measurement constellation all‐sky MW radiances in addition to GOES‐16 all‐sky IR radiances reduces the forecast errors in the TC track, rapid intensification (RI), and peak intensity compared to assimilating all‐sky IR radiances alone, including a 24‐hr increase in forecast lead‐time for RI. Assimilating all‐sky MW radiances also improves Harvey's hydrometeor fields, which leads to improved forecasts of rainfall after Harvey's landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC‐associated hazards in the future.
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spelling pubmed-92868192022-07-19 Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017) Zhang, Yunji Sieron, Scott B. Lu, Yinghui Chen, Xingchao Nystrom, Robert G. Minamide, Masashi Chan, Man‐Yau Hartman, Christopher M. Yao, Zhu Ruppert, James H. Okazaki, Atsushi Greybush, Steven J. Clothiaux, Eugene E. Zhang, Fuqing Geophys Res Lett Research Letter Ensemble‐based data assimilation of radar observations across inner‐core regions of tropical cyclones (TCs) in tandem with satellite all‐sky infrared (IR) radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all‐sky microwave (MW) radiances using Hurricane Harvey (2017) as an example. Assimilating Global Precipitation Measurement constellation all‐sky MW radiances in addition to GOES‐16 all‐sky IR radiances reduces the forecast errors in the TC track, rapid intensification (RI), and peak intensity compared to assimilating all‐sky IR radiances alone, including a 24‐hr increase in forecast lead‐time for RI. Assimilating all‐sky MW radiances also improves Harvey's hydrometeor fields, which leads to improved forecasts of rainfall after Harvey's landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC‐associated hazards in the future. John Wiley and Sons Inc. 2021-12-26 2021-12-28 /pmc/articles/PMC9286819/ /pubmed/35865360 http://dx.doi.org/10.1029/2021GL096410 Text en © 2021. The Authors. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Letter
Zhang, Yunji
Sieron, Scott B.
Lu, Yinghui
Chen, Xingchao
Nystrom, Robert G.
Minamide, Masashi
Chan, Man‐Yau
Hartman, Christopher M.
Yao, Zhu
Ruppert, James H.
Okazaki, Atsushi
Greybush, Steven J.
Clothiaux, Eugene E.
Zhang, Fuqing
Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)
title Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)
title_full Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)
title_fullStr Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)
title_full_unstemmed Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)
title_short Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)
title_sort ensemble‐based assimilation of satellite all‐sky microwave radiances improves intensity and rainfall predictions for hurricane harvey (2017)
topic Research Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286819/
https://www.ncbi.nlm.nih.gov/pubmed/35865360
http://dx.doi.org/10.1029/2021GL096410
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