Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia
Validated dust and aerosol datasets provide valuable information for dust and other atmospheric research. A reanalysis product called ‘MERRA-2’ developed by NASA's Global Modeling and Assimilation Office (GMAO) in recent years provides long-term, high frequency, global coverage aerosol simulati...
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ftgriffithuniv:oai:research-repository.griffith.edu.au:10072/419139 2024-06-23T07:45:01+00:00 Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia Che, Yahui Yu, Bofu Parsons, Katherine Desha, Cheryl Ramezani, Mohammad 2022 http://hdl.handle.net/10072/419139 https://doi.org/10.1016/j.atmosenv.2022.119054 English eng Elsevier Atmospheric Environment Che, Y; Yu, B; Parsons, K; Desha, C; Ramezani, M, Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia, Atmospheric Environment, 2022, 277, pp. 119054 http://hdl.handle.net/10072/419139 1352-2310 doi:10.1016/j.atmosenv.2022.119054 open access Atmospheric sciences Climate change science Environmental engineering Science & Technology Life Sciences & Biomedicine Physical Sciences Environmental Sciences Meteorology & Atmospheric Sciences Journal article 2022 ftgriffithuniv https://doi.org/10.1016/j.atmosenv.2022.119054 2024-06-04T23:56:28Z Validated dust and aerosol datasets provide valuable information for dust and other atmospheric research. A reanalysis product called ‘MERRA-2’ developed by NASA's Global Modeling and Assimilation Office (GMAO) in recent years provides long-term, high frequency, global coverage aerosol simulations, in terms of AOD (aerosol optical depth) and dust AOD (DAOD). The AOD assimilation system including AERONET (Aerosol Robotic Network) as constraints has led to difficulties with independent validation of MERRA-2 AOD and DAOD. The existing validation and evaluation of MERRA-2 tend to focus on central inland Australia, and comparison of DAOD with MACC (Monitoring Atmospheric Composition and Climate) is insufficient to indicate which one is more appropriate for dust research in Australia. In this study, MERRA-2 output was compared with MODIS DeepBlue (MODIS-DB) (2002–2020) and AERONET (1998–2020) AOD and DAOD in Australia. The expected error (EE) for MERRA-2 AOD was estimated to be ±(0.03+0.15τA) containing 66% of data points. MERRA-2 is less well correlated with AERONET in terms of DAOD, but with a tighter distribution of data points (78%) within the EE. The EE for MODIS-DB AOD was found to be the same and the correlation between MODIS-DB DAOD and AERONET DAOD is stronger than that between MERRA-2 DAOD and AERONET DAOD. Comparison of MERRA-2 and MODIS-DB AOD showed that MERRA-2 AOD was 22.5% higher than MODIS-DB AOD at AERONET sites on average. MERRA-2 AOD was generally lower than MODIS-DB AOD away from AERONET sites when AOD >0.1, and the spatial distribution of the difference between the two is consistent with the spatial variation in AOD. Large differences between MODIS-DB AOD and MERRA-2 occurred in areas where AOD is greater than 0.3. AOD and DAOD based on MODIS-DB could be used for areas away from AERONET sites in Australia, as they are less dependent on AERONET data compared to MERRA-2. When compared to MODIS brightness temperature difference (BTD) for two known severe storm events in Australia, MODIS-DB ... Article in Journal/Newspaper Aerosol Robotic Network Griffith University: Griffith Research Online Merra ENVELOPE(12.615,12.615,65.816,65.816) Atmospheric Environment 277 119054 |
institution |
Open Polar |
collection |
Griffith University: Griffith Research Online |
op_collection_id |
ftgriffithuniv |
language |
English |
topic |
Atmospheric sciences Climate change science Environmental engineering Science & Technology Life Sciences & Biomedicine Physical Sciences Environmental Sciences Meteorology & Atmospheric Sciences |
spellingShingle |
Atmospheric sciences Climate change science Environmental engineering Science & Technology Life Sciences & Biomedicine Physical Sciences Environmental Sciences Meteorology & Atmospheric Sciences Che, Yahui Yu, Bofu Parsons, Katherine Desha, Cheryl Ramezani, Mohammad Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia |
topic_facet |
Atmospheric sciences Climate change science Environmental engineering Science & Technology Life Sciences & Biomedicine Physical Sciences Environmental Sciences Meteorology & Atmospheric Sciences |
description |
Validated dust and aerosol datasets provide valuable information for dust and other atmospheric research. A reanalysis product called ‘MERRA-2’ developed by NASA's Global Modeling and Assimilation Office (GMAO) in recent years provides long-term, high frequency, global coverage aerosol simulations, in terms of AOD (aerosol optical depth) and dust AOD (DAOD). The AOD assimilation system including AERONET (Aerosol Robotic Network) as constraints has led to difficulties with independent validation of MERRA-2 AOD and DAOD. The existing validation and evaluation of MERRA-2 tend to focus on central inland Australia, and comparison of DAOD with MACC (Monitoring Atmospheric Composition and Climate) is insufficient to indicate which one is more appropriate for dust research in Australia. In this study, MERRA-2 output was compared with MODIS DeepBlue (MODIS-DB) (2002–2020) and AERONET (1998–2020) AOD and DAOD in Australia. The expected error (EE) for MERRA-2 AOD was estimated to be ±(0.03+0.15τA) containing 66% of data points. MERRA-2 is less well correlated with AERONET in terms of DAOD, but with a tighter distribution of data points (78%) within the EE. The EE for MODIS-DB AOD was found to be the same and the correlation between MODIS-DB DAOD and AERONET DAOD is stronger than that between MERRA-2 DAOD and AERONET DAOD. Comparison of MERRA-2 and MODIS-DB AOD showed that MERRA-2 AOD was 22.5% higher than MODIS-DB AOD at AERONET sites on average. MERRA-2 AOD was generally lower than MODIS-DB AOD away from AERONET sites when AOD >0.1, and the spatial distribution of the difference between the two is consistent with the spatial variation in AOD. Large differences between MODIS-DB AOD and MERRA-2 occurred in areas where AOD is greater than 0.3. AOD and DAOD based on MODIS-DB could be used for areas away from AERONET sites in Australia, as they are less dependent on AERONET data compared to MERRA-2. When compared to MODIS brightness temperature difference (BTD) for two known severe storm events in Australia, MODIS-DB ... |
format |
Article in Journal/Newspaper |
author |
Che, Yahui Yu, Bofu Parsons, Katherine Desha, Cheryl Ramezani, Mohammad |
author_facet |
Che, Yahui Yu, Bofu Parsons, Katherine Desha, Cheryl Ramezani, Mohammad |
author_sort |
Che, Yahui |
title |
Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia |
title_short |
Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia |
title_full |
Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia |
title_fullStr |
Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia |
title_full_unstemmed |
Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia |
title_sort |
evaluation and comparison of merra-2 aod and daod with modis deepblue and aeronet data in australia |
publisher |
Elsevier |
publishDate |
2022 |
url |
http://hdl.handle.net/10072/419139 https://doi.org/10.1016/j.atmosenv.2022.119054 |
long_lat |
ENVELOPE(12.615,12.615,65.816,65.816) |
geographic |
Merra |
geographic_facet |
Merra |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
Atmospheric Environment Che, Y; Yu, B; Parsons, K; Desha, C; Ramezani, M, Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia, Atmospheric Environment, 2022, 277, pp. 119054 http://hdl.handle.net/10072/419139 1352-2310 doi:10.1016/j.atmosenv.2022.119054 |
op_rights |
open access |
op_doi |
https://doi.org/10.1016/j.atmosenv.2022.119054 |
container_title |
Atmospheric Environment |
container_volume |
277 |
container_start_page |
119054 |
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1802647722504749056 |