Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis
In this paper, we introduce the usage of a newly developed spectral decomposition technique – combined maximum covariance analysis (CMCA) – in the spatiotemporal comparison of four satellite data sets and ground-based observations of aerosol optical depth (AOD). This technique is based on commonly u...
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ftdoajarticles:oai:doaj.org/article:26d7c97197784637b6d32d251f32814f 2023-05-15T13:06:59+02:00 Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis J. Li B. E. Carlson A. A. Lacis 2014-08-01T00:00:00Z https://doi.org/10.5194/amt-7-2531-2014 https://doaj.org/article/26d7c97197784637b6d32d251f32814f EN eng Copernicus Publications http://www.atmos-meas-tech.net/7/2531/2014/amt-7-2531-2014.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 1867-1381 1867-8548 doi:10.5194/amt-7-2531-2014 https://doaj.org/article/26d7c97197784637b6d32d251f32814f Atmospheric Measurement Techniques, Vol 7, Iss 8, Pp 2531-2549 (2014) Environmental engineering TA170-171 Earthwork. Foundations TA715-787 article 2014 ftdoajarticles https://doi.org/10.5194/amt-7-2531-2014 2022-12-31T10:57:41Z In this paper, we introduce the usage of a newly developed spectral decomposition technique – combined maximum covariance analysis (CMCA) – in the spatiotemporal comparison of four satellite data sets and ground-based observations of aerosol optical depth (AOD). This technique is based on commonly used principal component analysis (PCA) and maximum covariance analysis (MCA). By decomposing the cross-covariance matrix between the joint satellite data field and Aerosol Robotic Network (AERONET) station data, both parallel comparison across different satellite data sets and the evaluation of the satellite data against the AERONET measurements are simultaneously realized. We show that this new method not only confirms the seasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, but also identifies the strengths and weaknesses of each data set in capturing the variability associated with sources, events or aerosol types. Furthermore, by examining the spread of the spatial modes of different satellite fields, regions with the largest uncertainties in aerosol observation are identified. We also present two regional case studies that respectively demonstrate the capability of the CMCA technique in assessing the representation of an extreme event in different data sets, and in evaluating the performance of different data sets on seasonal and interannual timescales. Global results indicate that different data sets agree qualitatively for major aerosol-source regions. Discrepancies are mostly found over the Sahel, India, eastern and southeastern Asia. Results for eastern Europe suggest that the intense wildfire event in Russia during summer 2010 was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due to misclassification of smoke plumes as clouds. Analysis for the Indian subcontinent shows that here SeaWiFS agrees best with AERONET in terms of seasonality for ... Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Indian Atmospheric Measurement Techniques 7 8 2531 2549 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
spellingShingle |
Environmental engineering TA170-171 Earthwork. Foundations TA715-787 J. Li B. E. Carlson A. A. Lacis Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis |
topic_facet |
Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
description |
In this paper, we introduce the usage of a newly developed spectral decomposition technique – combined maximum covariance analysis (CMCA) – in the spatiotemporal comparison of four satellite data sets and ground-based observations of aerosol optical depth (AOD). This technique is based on commonly used principal component analysis (PCA) and maximum covariance analysis (MCA). By decomposing the cross-covariance matrix between the joint satellite data field and Aerosol Robotic Network (AERONET) station data, both parallel comparison across different satellite data sets and the evaluation of the satellite data against the AERONET measurements are simultaneously realized. We show that this new method not only confirms the seasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, but also identifies the strengths and weaknesses of each data set in capturing the variability associated with sources, events or aerosol types. Furthermore, by examining the spread of the spatial modes of different satellite fields, regions with the largest uncertainties in aerosol observation are identified. We also present two regional case studies that respectively demonstrate the capability of the CMCA technique in assessing the representation of an extreme event in different data sets, and in evaluating the performance of different data sets on seasonal and interannual timescales. Global results indicate that different data sets agree qualitatively for major aerosol-source regions. Discrepancies are mostly found over the Sahel, India, eastern and southeastern Asia. Results for eastern Europe suggest that the intense wildfire event in Russia during summer 2010 was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due to misclassification of smoke plumes as clouds. Analysis for the Indian subcontinent shows that here SeaWiFS agrees best with AERONET in terms of seasonality for ... |
format |
Article in Journal/Newspaper |
author |
J. Li B. E. Carlson A. A. Lacis |
author_facet |
J. Li B. E. Carlson A. A. Lacis |
author_sort |
J. Li |
title |
Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis |
title_short |
Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis |
title_full |
Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis |
title_fullStr |
Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis |
title_full_unstemmed |
Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis |
title_sort |
application of spectral analysis techniques to the intercomparison of aerosol data – part 4: synthesized analysis of multisensor satellite and ground-based aod measurements using combined maximum covariance analysis |
publisher |
Copernicus Publications |
publishDate |
2014 |
url |
https://doi.org/10.5194/amt-7-2531-2014 https://doaj.org/article/26d7c97197784637b6d32d251f32814f |
geographic |
Indian |
geographic_facet |
Indian |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Atmospheric Measurement Techniques, Vol 7, Iss 8, Pp 2531-2549 (2014) |
op_relation |
http://www.atmos-meas-tech.net/7/2531/2014/amt-7-2531-2014.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 1867-1381 1867-8548 doi:10.5194/amt-7-2531-2014 https://doaj.org/article/26d7c97197784637b6d32d251f32814f |
op_doi |
https://doi.org/10.5194/amt-7-2531-2014 |
container_title |
Atmospheric Measurement Techniques |
container_volume |
7 |
container_issue |
8 |
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2531 |
op_container_end_page |
2549 |
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