Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data
Thanks to its observational frequency of 15 min, the Meteosat Second Generation (MSG) geostationary satellite offers a great potential to monitor dust storms. To explore this potential, an algorithm for the detection and the retrieval of dust aerosol optical properties has been tested. This is a mul...
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ftdoajarticles:oai:doaj.org/article:9efb5d7925f849569eb86428db83728a 2023-05-15T13:06:07+02:00 Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data Francesco Di Paola Mariassunta Viggiano Domenico Cimini Elisabetta Ricciardelli Filomena Romano 2013-03-01T00:00:00Z https://doi.org/10.3390/atmos4010035 https://doaj.org/article/9efb5d7925f849569eb86428db83728a EN eng MDPI AG http://www.mdpi.com/2073-4433/4/1/35 https://doaj.org/toc/2073-4433 doi:10.3390/atmos4010035 2073-4433 https://doaj.org/article/9efb5d7925f849569eb86428db83728a Atmosphere, Vol 4, Iss 1, Pp 35-47 (2013) dust optical depth dust detection MSG SEVIRI Meteorology. Climatology QC851-999 article 2013 ftdoajarticles https://doi.org/10.3390/atmos4010035 2022-12-31T12:09:46Z Thanks to its observational frequency of 15 min, the Meteosat Second Generation (MSG) geostationary satellite offers a great potential to monitor dust storms. To explore this potential, an algorithm for the detection and the retrieval of dust aerosol optical properties has been tested. This is a multispectral algorithm based on visible and infrared data which has been applied to 15 case studies selected between 2007 and 2011. The algorithm has been validated in the latitude–longitude box between 30 and 50 degrees north, and −10 and 20 degrees east, respectively. Hereafter we present the obtained results that have been validated against Aerosol Robotic Network (AERONET) ground-based measurements and compared with the retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites. The dust aerosol optical depth variations observed at the AERONET sites are well reproduced, showing good correlation of about 0.77, and a root mean square difference within 0.08, and the spatial patterns retrieved by using the algorithm developed are in agreement with those observed by MODIS. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Atmosphere 4 1 35 47 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
dust optical depth dust detection MSG SEVIRI Meteorology. Climatology QC851-999 |
spellingShingle |
dust optical depth dust detection MSG SEVIRI Meteorology. Climatology QC851-999 Francesco Di Paola Mariassunta Viggiano Domenico Cimini Elisabetta Ricciardelli Filomena Romano Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data |
topic_facet |
dust optical depth dust detection MSG SEVIRI Meteorology. Climatology QC851-999 |
description |
Thanks to its observational frequency of 15 min, the Meteosat Second Generation (MSG) geostationary satellite offers a great potential to monitor dust storms. To explore this potential, an algorithm for the detection and the retrieval of dust aerosol optical properties has been tested. This is a multispectral algorithm based on visible and infrared data which has been applied to 15 case studies selected between 2007 and 2011. The algorithm has been validated in the latitude–longitude box between 30 and 50 degrees north, and −10 and 20 degrees east, respectively. Hereafter we present the obtained results that have been validated against Aerosol Robotic Network (AERONET) ground-based measurements and compared with the retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites. The dust aerosol optical depth variations observed at the AERONET sites are well reproduced, showing good correlation of about 0.77, and a root mean square difference within 0.08, and the spatial patterns retrieved by using the algorithm developed are in agreement with those observed by MODIS. |
format |
Article in Journal/Newspaper |
author |
Francesco Di Paola Mariassunta Viggiano Domenico Cimini Elisabetta Ricciardelli Filomena Romano |
author_facet |
Francesco Di Paola Mariassunta Viggiano Domenico Cimini Elisabetta Ricciardelli Filomena Romano |
author_sort |
Francesco Di Paola |
title |
Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data |
title_short |
Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data |
title_full |
Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data |
title_fullStr |
Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data |
title_full_unstemmed |
Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data |
title_sort |
dust detection and optical depth retrieval using msg‑seviri data |
publisher |
MDPI AG |
publishDate |
2013 |
url |
https://doi.org/10.3390/atmos4010035 https://doaj.org/article/9efb5d7925f849569eb86428db83728a |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Atmosphere, Vol 4, Iss 1, Pp 35-47 (2013) |
op_relation |
http://www.mdpi.com/2073-4433/4/1/35 https://doaj.org/toc/2073-4433 doi:10.3390/atmos4010035 2073-4433 https://doaj.org/article/9efb5d7925f849569eb86428db83728a |
op_doi |
https://doi.org/10.3390/atmos4010035 |
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Atmosphere |
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4 |
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1 |
container_start_page |
35 |
op_container_end_page |
47 |
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1766403905094156288 |