Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite

Abstract Himawari‐8, a next‐generation geostationary meteorological satellite, was launched on 7 October 2014 and became operational on 7 July 2015. The advanced imager on board Himawari‐8 is equipped with 16 observational bands (including three visible and three near‐infrared bands) that enable ret...

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Main Authors: K. Yumimoto, T.M. Nagao, M. Kikuchi, T.T Sekiyama, H. Murakami, T.Y. Tanaka, A. Ogi, H. Irie, P. Khatri, H. Okumura, K. Arai, I. Morino, O. Uchino, T. Maki
Format: Article
Language:English
Published: Wiley 2016-06-01
Series:Geophysical Research Letters
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Online Access:https://doi.org/10.1002/2016GL069298
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author K. Yumimoto
T.M. Nagao
M. Kikuchi
T.T Sekiyama
H. Murakami
T.Y. Tanaka
A. Ogi
H. Irie
P. Khatri
H. Okumura
K. Arai
I. Morino
O. Uchino
T. Maki
author_facet K. Yumimoto
T.M. Nagao
M. Kikuchi
T.T Sekiyama
H. Murakami
T.Y. Tanaka
A. Ogi
H. Irie
P. Khatri
H. Okumura
K. Arai
I. Morino
O. Uchino
T. Maki
author_sort K. Yumimoto
collection DOAJ
description Abstract Himawari‐8, a next‐generation geostationary meteorological satellite, was launched on 7 October 2014 and became operational on 7 July 2015. The advanced imager on board Himawari‐8 is equipped with 16 observational bands (including three visible and three near‐infrared bands) that enable retrieval of full‐disk aerosol optical properties at 10 min intervals from geostationary (GEO) orbit. Here we show the first application of aerosol optical properties (AOPs) derived from Himawari‐8 data to aerosol data assimilation. Validation of the assimilation experiment by comparison with independent observations demonstrated successful modeling of continental pollution that was not predicted by simulation without assimilation and reduced overestimates of dust front concentrations. These promising results suggest that AOPs derived from Himawari‐8/9 and other planned GEO satellites will considerably improve forecasts of air quality, inverse modeling of emissions, and aerosol reanalysis through assimilation techniques.
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spelling doaj-art-961638b360b944d5a2a825e0ba4bf2fb2025-08-20T02:31:41ZengWileyGeophysical Research Letters0094-82761944-80072016-06-0143115886589410.1002/2016GL069298Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satelliteK. Yumimoto0T.M. Nagao1M. Kikuchi2T.T Sekiyama3H. Murakami4T.Y. Tanaka5A. Ogi6H. Irie7P. Khatri8H. Okumura9K. Arai10I. Morino11O. Uchino12T. Maki13Meteorological Research Institute Japan Meteorological Agency Tsukuba JapanEarth Observation Research Center Japan Aerospace Exploration Agency Tsukuba JapanEarth Observation Research Center Japan Aerospace Exploration Agency Tsukuba JapanMeteorological Research Institute Japan Meteorological Agency Tsukuba JapanEarth Observation Research Center Japan Aerospace Exploration Agency Tsukuba JapanMeteorological Research Institute Japan Meteorological Agency Tsukuba JapanJapan Meteorological Agency Tokyo JapanCenter for Environmental Remote Sensing (CERes) Chiba University Chiba JapanCenter for Environmental Remote Sensing (CERes) Chiba University Chiba JapanGraduate School of Science and Engineering Saga University Saga JapanGraduate School of Science and Engineering Saga University Saga JapanNational Institute for Environmental Studies Tsukuba JapanMeteorological Research Institute Japan Meteorological Agency Tsukuba JapanMeteorological Research Institute Japan Meteorological Agency Tsukuba JapanAbstract Himawari‐8, a next‐generation geostationary meteorological satellite, was launched on 7 October 2014 and became operational on 7 July 2015. The advanced imager on board Himawari‐8 is equipped with 16 observational bands (including three visible and three near‐infrared bands) that enable retrieval of full‐disk aerosol optical properties at 10 min intervals from geostationary (GEO) orbit. Here we show the first application of aerosol optical properties (AOPs) derived from Himawari‐8 data to aerosol data assimilation. Validation of the assimilation experiment by comparison with independent observations demonstrated successful modeling of continental pollution that was not predicted by simulation without assimilation and reduced overestimates of dust front concentrations. These promising results suggest that AOPs derived from Himawari‐8/9 and other planned GEO satellites will considerably improve forecasts of air quality, inverse modeling of emissions, and aerosol reanalysis through assimilation techniques.https://doi.org/10.1002/2016GL069298aerosoldata assimilationgeostationary satelliteaerosol climate modelensemble Kalman filter
spellingShingle K. Yumimoto
T.M. Nagao
M. Kikuchi
T.T Sekiyama
H. Murakami
T.Y. Tanaka
A. Ogi
H. Irie
P. Khatri
H. Okumura
K. Arai
I. Morino
O. Uchino
T. Maki
Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite
Geophysical Research Letters
aerosol
data assimilation
geostationary satellite
aerosol climate model
ensemble Kalman filter
title Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite
title_full Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite
title_fullStr Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite
title_full_unstemmed Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite
title_short Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite
title_sort aerosol data assimilation using data from himawari 8 a next generation geostationary meteorological satellite
topic aerosol
data assimilation
geostationary satellite
aerosol climate model
ensemble Kalman filter
url https://doi.org/10.1002/2016GL069298
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