The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation
The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm retrieves vertical dust concentration profiles from cloud-free Infrared Atmospheric Sounding Interferometer (IASI) thermal infrared (TIR) radiances using Rodgers' optimal estimation method (OEM). We describe the new version...
Published in: | Atmospheric Measurement Techniques |
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Copernicus Publications
2019
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Online Access: | https://doi.org/10.5194/amt-12-3673-2019 https://doaj.org/article/b0c95c27ab75450697bf95e0972191af |
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author | S. Callewaert S. Vandenbussche N. Kumps A. Kylling X. Shang M. Komppula P. Goloub M. De Mazière |
author_facet | S. Callewaert S. Vandenbussche N. Kumps A. Kylling X. Shang M. Komppula P. Goloub M. De Mazière |
author_sort | S. Callewaert |
collection | Directory of Open Access Journals: DOAJ Articles |
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container_title | Atmospheric Measurement Techniques |
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description | The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm retrieves vertical dust concentration profiles from cloud-free Infrared Atmospheric Sounding Interferometer (IASI) thermal infrared (TIR) radiances using Rodgers' optimal estimation method (OEM). We describe the new version 4.1 and evaluation results. Main differences with respect to previous versions are the Levenberg–Marquardt modification of the OEM, the use of the logarithm of the concentration in the retrieval and the use of Radiative Transfer for TOVS (RTTOV) for in-line radiative transfer calculations. The dust aerosol concentrations are retrieved in seven 1 km thick layers centered at 0.5 to 6.5 km . A global data set of the daily dust distribution was generated with MAPIR v 4.1 covering September 2007 to June 2018 , with further extensions planned every 6 months. The post-retrieval quality filters reject about 16 % of the retrievals, a huge improvement with respect to the previous versions in which up to 40 % of the retrievals were of bad quality. The median difference between the observed and fitted spectra of the good-quality retrievals is 0.32 K , with lower values over oceans. The information content of the retrieved profiles shows a dependence on the total aerosol load due to the assumption of a lognormal state vector. The median degrees of freedom in dusty scenes (min 10 µm AOD of 0.5 ) is 1.4 . An evaluation of the aerosol optical depth (AOD) obtained from the integrated MAPIR v 4.1 profiles was performed against 72 AErosol RObotic NETwork (AERONET) stations. The MAPIR AOD correlates well with the ground-based data, with a mean correlation coefficient of 0.66 and values as high as 0.88 . Overall, there is a mean AOD ( 550 nm ) positive bias of only 0.04 with respect to AERONET, which is an extremely good result. The previous versions of MAPIR were known to largely overestimate AOD (about 0.28 for v 3 ). A second evaluation exercise was performed comparing the mean aerosol layer altitude from MAPIR with the mean dust altitude ... |
format | Article in Journal/Newspaper |
genre | Aerosol Robotic Network |
genre_facet | Aerosol Robotic Network |
id | ftdoajarticles:oai:doaj.org/article:b0c95c27ab75450697bf95e0972191af |
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op_doi | https://doi.org/10.5194/amt-12-3673-2019 |
op_relation | https://www.atmos-meas-tech.net/12/3673/2019/amt-12-3673-2019.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 doi:10.5194/amt-12-3673-2019 1867-1381 1867-8548 https://doaj.org/article/b0c95c27ab75450697bf95e0972191af |
op_source | Atmospheric Measurement Techniques, Vol 12, Pp 3673-3698 (2019) |
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spelling | ftdoajarticles:oai:doaj.org/article:b0c95c27ab75450697bf95e0972191af 2025-01-16T18:39:10+00:00 The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation S. Callewaert S. Vandenbussche N. Kumps A. Kylling X. Shang M. Komppula P. Goloub M. De Mazière 2019-07-01T00:00:00Z https://doi.org/10.5194/amt-12-3673-2019 https://doaj.org/article/b0c95c27ab75450697bf95e0972191af EN eng Copernicus Publications https://www.atmos-meas-tech.net/12/3673/2019/amt-12-3673-2019.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 doi:10.5194/amt-12-3673-2019 1867-1381 1867-8548 https://doaj.org/article/b0c95c27ab75450697bf95e0972191af Atmospheric Measurement Techniques, Vol 12, Pp 3673-3698 (2019) Environmental engineering TA170-171 Earthwork. Foundations TA715-787 article 2019 ftdoajarticles https://doi.org/10.5194/amt-12-3673-2019 2022-12-31T09:37:20Z The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm retrieves vertical dust concentration profiles from cloud-free Infrared Atmospheric Sounding Interferometer (IASI) thermal infrared (TIR) radiances using Rodgers' optimal estimation method (OEM). We describe the new version 4.1 and evaluation results. Main differences with respect to previous versions are the Levenberg–Marquardt modification of the OEM, the use of the logarithm of the concentration in the retrieval and the use of Radiative Transfer for TOVS (RTTOV) for in-line radiative transfer calculations. The dust aerosol concentrations are retrieved in seven 1 km thick layers centered at 0.5 to 6.5 km . A global data set of the daily dust distribution was generated with MAPIR v 4.1 covering September 2007 to June 2018 , with further extensions planned every 6 months. The post-retrieval quality filters reject about 16 % of the retrievals, a huge improvement with respect to the previous versions in which up to 40 % of the retrievals were of bad quality. The median difference between the observed and fitted spectra of the good-quality retrievals is 0.32 K , with lower values over oceans. The information content of the retrieved profiles shows a dependence on the total aerosol load due to the assumption of a lognormal state vector. The median degrees of freedom in dusty scenes (min 10 µm AOD of 0.5 ) is 1.4 . An evaluation of the aerosol optical depth (AOD) obtained from the integrated MAPIR v 4.1 profiles was performed against 72 AErosol RObotic NETwork (AERONET) stations. The MAPIR AOD correlates well with the ground-based data, with a mean correlation coefficient of 0.66 and values as high as 0.88 . Overall, there is a mean AOD ( 550 nm ) positive bias of only 0.04 with respect to AERONET, which is an extremely good result. The previous versions of MAPIR were known to largely overestimate AOD (about 0.28 for v 3 ). A second evaluation exercise was performed comparing the mean aerosol layer altitude from MAPIR with the mean dust altitude ... Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Atmospheric Measurement Techniques 12 7 3673 3698 |
spellingShingle | Environmental engineering TA170-171 Earthwork. Foundations TA715-787 S. Callewaert S. Vandenbussche N. Kumps A. Kylling X. Shang M. Komppula P. Goloub M. De Mazière The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation |
title | The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation |
title_full | The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation |
title_fullStr | The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation |
title_full_unstemmed | The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation |
title_short | The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation |
title_sort | mineral aerosol profiling from infrared radiances (mapir) algorithm: version 4.1 description and evaluation |
topic | Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
topic_facet | Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
url | https://doi.org/10.5194/amt-12-3673-2019 https://doaj.org/article/b0c95c27ab75450697bf95e0972191af |