Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method

In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the d...

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Published in:Remote Sensing
Main Authors: Bingxu Li, Xi Guo, Hao Liu, Donghao Han, Gang Li, Ji Wu
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2024
Subjects:
Q
Online Access:https://doi.org/10.3390/rs16101715
https://doaj.org/article/fc45770a03ed4486bde2c144873e2451
id ftdoajarticles:oai:doaj.org/article:fc45770a03ed4486bde2c144873e2451
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spelling ftdoajarticles:oai:doaj.org/article:fc45770a03ed4486bde2c144873e2451 2024-09-09T19:23:11+00:00 Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method Bingxu Li Xi Guo Hao Liu Donghao Han Gang Li Ji Wu 2024-05-01T00:00:00Z https://doi.org/10.3390/rs16101715 https://doaj.org/article/fc45770a03ed4486bde2c144873e2451 EN eng MDPI AG https://www.mdpi.com/2072-4292/16/10/1715 https://doaj.org/toc/2072-4292 doi:10.3390/rs16101715 2072-4292 https://doaj.org/article/fc45770a03ed4486bde2c144873e2451 Remote Sensing, Vol 16, Iss 10, p 1715 (2024) Atmospheric Motion Vector (AMV) polar meteorology microwave radiometers water-vapor absorption band Science Q article 2024 ftdoajarticles https://doi.org/10.3390/rs16101715 2024-08-05T17:49:20Z In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the derived wind for each channel is determined by calculating the weighing function peak using monthly averaged ERA5 reanalysis data. By utilizing Microwave Humidity Sounder-II (MWHS-II) brightness temperatures from the five channels centered around 183.31 GHz, wind vectors are retrieved within the Arctic region for the entire year of 2022. The retrieval quality is evaluated through comparative analysis with ERA5 reanalysis data and the Visible Infrared Imaging Radiometer Suite (VIIRS) wind product. The resultant vector root mean square errors (RMSEs) are approximately 4.5 m/s for the three lower-height channels and 5.5 m/s for the two upper-height channels. These findings demonstrate a wind retrieval performance comparable to the existing methods, highlighting its potential for augmenting wind availability at lower height levels. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Triplets ENVELOPE(-59.750,-59.750,-62.383,-62.383) Remote Sensing 16 10 1715
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Atmospheric Motion Vector (AMV)
polar meteorology
microwave radiometers
water-vapor absorption band
Science
Q
spellingShingle Atmospheric Motion Vector (AMV)
polar meteorology
microwave radiometers
water-vapor absorption band
Science
Q
Bingxu Li
Xi Guo
Hao Liu
Donghao Han
Gang Li
Ji Wu
Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method
topic_facet Atmospheric Motion Vector (AMV)
polar meteorology
microwave radiometers
water-vapor absorption band
Science
Q
description In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the derived wind for each channel is determined by calculating the weighing function peak using monthly averaged ERA5 reanalysis data. By utilizing Microwave Humidity Sounder-II (MWHS-II) brightness temperatures from the five channels centered around 183.31 GHz, wind vectors are retrieved within the Arctic region for the entire year of 2022. The retrieval quality is evaluated through comparative analysis with ERA5 reanalysis data and the Visible Infrared Imaging Radiometer Suite (VIIRS) wind product. The resultant vector root mean square errors (RMSEs) are approximately 4.5 m/s for the three lower-height channels and 5.5 m/s for the two upper-height channels. These findings demonstrate a wind retrieval performance comparable to the existing methods, highlighting its potential for augmenting wind availability at lower height levels.
format Article in Journal/Newspaper
author Bingxu Li
Xi Guo
Hao Liu
Donghao Han
Gang Li
Ji Wu
author_facet Bingxu Li
Xi Guo
Hao Liu
Donghao Han
Gang Li
Ji Wu
author_sort Bingxu Li
title Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method
title_short Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method
title_full Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method
title_fullStr Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method
title_full_unstemmed Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method
title_sort arctic winds retrieved from fy-3d microwave humidity sounder-ii 183.31 ghz brightness temperature using atmospheric motion vector method
publisher MDPI AG
publishDate 2024
url https://doi.org/10.3390/rs16101715
https://doaj.org/article/fc45770a03ed4486bde2c144873e2451
long_lat ENVELOPE(-59.750,-59.750,-62.383,-62.383)
geographic Arctic
Triplets
geographic_facet Arctic
Triplets
genre Arctic
genre_facet Arctic
op_source Remote Sensing, Vol 16, Iss 10, p 1715 (2024)
op_relation https://www.mdpi.com/2072-4292/16/10/1715
https://doaj.org/toc/2072-4292
doi:10.3390/rs16101715
2072-4292
https://doaj.org/article/fc45770a03ed4486bde2c144873e2451
op_doi https://doi.org/10.3390/rs16101715
container_title Remote Sensing
container_volume 16
container_issue 10
container_start_page 1715
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