Improving estimation of a record breaking East Asian dust storm emission with lagged aerosol Ångström Exponent observations

A record-breaking East Asian dust storm over recent years occurred in March 2021. Ångström Exponent (AE) can resolve the particle size and is significantly sensitive to large aerosol such as dust. Due to lack of observation during dust storm and high uncertainty of satellite retri...

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Main Authors: Cheng, Yueming, Dai, Tie, Cao, Junji, Goto, Daisuke, Jin, Jianbing, Nakajima, Teruyuki, Shi, Guangyu
Format: Text
Language:English
Published: 2024
Subjects:
Online Access:https://doi.org/10.5194/egusphere-2024-840
https://egusphere.copernicus.org/preprints/2024/egusphere-2024-840/
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spelling ftcopernicus:oai:publications.copernicus.org:egusphere118930 2024-06-23T07:44:59+00:00 Improving estimation of a record breaking East Asian dust storm emission with lagged aerosol Ångström Exponent observations Cheng, Yueming Dai, Tie Cao, Junji Goto, Daisuke Jin, Jianbing Nakajima, Teruyuki Shi, Guangyu 2024-03-25 application/pdf https://doi.org/10.5194/egusphere-2024-840 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-840/ eng eng doi:10.5194/egusphere-2024-840 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-840/ eISSN: Text 2024 ftcopernicus https://doi.org/10.5194/egusphere-2024-840 2024-06-13T01:23:00Z A record-breaking East Asian dust storm over recent years occurred in March 2021. Ångström Exponent (AE) can resolve the particle size and is significantly sensitive to large aerosol such as dust. Due to lack of observation during dust storm and high uncertainty of satellite retrieved AE, it is crucial to estimate the dust storm emission using the lagged ground-based AE observations. In this study, the Aerosol Robotic Network (AERONET) observed hourly AEs are assimilated with the fixed-lag ensemble Kalman smoother and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to optimize the simulated dust emission from 14 to 16 March 2021. The emission inversion results reveal that the dust emissions from the Gobi desert in the official WRF-Chem are significantly underestimated. Not only the temporal variation of simulated AE but also that of simulated aerosol optical thickness (AOT) can be improved through assimilating AE information. Compared to the assimilation with only AOT, the additional inclusion of AE doubles the dust emission and induces the extra 46.8 % improvement of root mean square error (RMSE) between the simulated AOTs and the AERONET and independent Skynet Observation NETwork (SONET) observations. The optimized dust emission from Mongolia Gobi and China Gobi reach the peak value about 441.65 kt/hour and 346.87 kt/hour at 08:00 UTC on 14 March and at 19:00 UTC on 15 March, respectively. The additional inclusion of AE also best captures the magnitude and variations of aerosol vertical extinctions both in the westward and eastward pathways of dust transport. Text Aerosol Robotic Network Copernicus Publications: E-Journals
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description A record-breaking East Asian dust storm over recent years occurred in March 2021. Ångström Exponent (AE) can resolve the particle size and is significantly sensitive to large aerosol such as dust. Due to lack of observation during dust storm and high uncertainty of satellite retrieved AE, it is crucial to estimate the dust storm emission using the lagged ground-based AE observations. In this study, the Aerosol Robotic Network (AERONET) observed hourly AEs are assimilated with the fixed-lag ensemble Kalman smoother and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to optimize the simulated dust emission from 14 to 16 March 2021. The emission inversion results reveal that the dust emissions from the Gobi desert in the official WRF-Chem are significantly underestimated. Not only the temporal variation of simulated AE but also that of simulated aerosol optical thickness (AOT) can be improved through assimilating AE information. Compared to the assimilation with only AOT, the additional inclusion of AE doubles the dust emission and induces the extra 46.8 % improvement of root mean square error (RMSE) between the simulated AOTs and the AERONET and independent Skynet Observation NETwork (SONET) observations. The optimized dust emission from Mongolia Gobi and China Gobi reach the peak value about 441.65 kt/hour and 346.87 kt/hour at 08:00 UTC on 14 March and at 19:00 UTC on 15 March, respectively. The additional inclusion of AE also best captures the magnitude and variations of aerosol vertical extinctions both in the westward and eastward pathways of dust transport.
format Text
author Cheng, Yueming
Dai, Tie
Cao, Junji
Goto, Daisuke
Jin, Jianbing
Nakajima, Teruyuki
Shi, Guangyu
spellingShingle Cheng, Yueming
Dai, Tie
Cao, Junji
Goto, Daisuke
Jin, Jianbing
Nakajima, Teruyuki
Shi, Guangyu
Improving estimation of a record breaking East Asian dust storm emission with lagged aerosol Ångström Exponent observations
author_facet Cheng, Yueming
Dai, Tie
Cao, Junji
Goto, Daisuke
Jin, Jianbing
Nakajima, Teruyuki
Shi, Guangyu
author_sort Cheng, Yueming
title Improving estimation of a record breaking East Asian dust storm emission with lagged aerosol Ångström Exponent observations
title_short Improving estimation of a record breaking East Asian dust storm emission with lagged aerosol Ångström Exponent observations
title_full Improving estimation of a record breaking East Asian dust storm emission with lagged aerosol Ångström Exponent observations
title_fullStr Improving estimation of a record breaking East Asian dust storm emission with lagged aerosol Ångström Exponent observations
title_full_unstemmed Improving estimation of a record breaking East Asian dust storm emission with lagged aerosol Ångström Exponent observations
title_sort improving estimation of a record breaking east asian dust storm emission with lagged aerosol ångström exponent observations
publishDate 2024
url https://doi.org/10.5194/egusphere-2024-840
https://egusphere.copernicus.org/preprints/2024/egusphere-2024-840/
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source eISSN:
op_relation doi:10.5194/egusphere-2024-840
https://egusphere.copernicus.org/preprints/2024/egusphere-2024-840/
op_doi https://doi.org/10.5194/egusphere-2024-840
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