A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements

Aerosol is an important component of the Earth's atmosphere, affecting weather, climate, and diverse elements of the biosphere. Satellite sounders are an essential tool for measuring the highly variable distributions of atmospheric aerosol. Here we present a new algorithm for estimating atmosph...

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Main Authors: Clarisse, Lieven, Clerbaux, Cathy, Franco, Bruno, Hadji-Lazaro, Juliette, Whitburn, Simon, Kopp, Andreas, Hurtmans, Daniel, Coheur, Pierre
Format: Article in Journal/Newspaper
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/290321
https://dipot.ulb.ac.be/dspace/bitstream/2013/290321/4/clarisse2019_dust.pdf
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author Clarisse, Lieven
Clerbaux, Cathy
Franco, Bruno
Hadji-Lazaro, Juliette
Whitburn, Simon
Kopp, Andreas
Hurtmans, Daniel
Coheur, Pierre
author_facet Clarisse, Lieven
Clerbaux, Cathy
Franco, Bruno
Hadji-Lazaro, Juliette
Whitburn, Simon
Kopp, Andreas
Hurtmans, Daniel
Coheur, Pierre
author_sort Clarisse, Lieven
collection DI-fusion : dépôt institutionnel de l'Université libre de Bruxelles (ULB)
description Aerosol is an important component of the Earth's atmosphere, affecting weather, climate, and diverse elements of the biosphere. Satellite sounders are an essential tool for measuring the highly variable distributions of atmospheric aerosol. Here we present a new algorithm for estimating atmospheric dust optical depths and associated retrieval uncertainties from spectral radiance measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The retrieval is based on the calculation of a dust index and on a neural network trained with synthetic IASI spectra. It has an inherent high sensitivity to dust and efficiently discriminates dust from other aerosols. In particular, over remote dust-free areas, the retrieved levels of optical depth have a low bias. Over sea, noise levels are markedly lower than over land. Performance over deserts is comparable to that of other land surfaces. We use ground-based coarse mode aerosol measurements from the AErosol RObotic NETwork to validate the new product. The overall assessment is favorable, with standard deviations in line with estimated uncertainties, low biases, and high correlation coefficients. However, a systematic relative bias occurs between sites dominated by African and Asian dust sources respectively, likely linked to differences in mineralogy. The retrieval has been performed on over a decade of IASI data, and the resulting data set is now publicly available. We present a global seasonal dust climatology based on this record and compare it with those obtained from independent satellite measurements (Moderate Resolution Imaging Spectroradiometer and a third-party IASI product) and dust optical depth from the ECMWF model. SCOPUS: ar.j info:eu-repo/semantics/published
format Article in Journal/Newspaper
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
id ftunivbruxelles:oai:dipot.ulb.ac.be:2013/290321
institution Open Polar
language English
op_collection_id ftunivbruxelles
op_relation uri/info:doi/10.1029/2018JD029701
uri/info:scp/85060947485
https://dipot.ulb.ac.be/dspace/bitstream/2013/290321/4/clarisse2019_dust.pdf
http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/290321
op_rights 1 full-text file(s): info:eu-repo/semantics/openAccess
op_source Journal of Geophysical Research: Atmospheres, 124 (3
publishDate 2019
record_format openpolar
spelling ftunivbruxelles:oai:dipot.ulb.ac.be:2013/290321 2025-01-16T18:38:42+00:00 A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements Clarisse, Lieven Clerbaux, Cathy Franco, Bruno Hadji-Lazaro, Juliette Whitburn, Simon Kopp, Andreas Hurtmans, Daniel Coheur, Pierre 2019-02-01 1 full-text file(s): application/pdf http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/290321 https://dipot.ulb.ac.be/dspace/bitstream/2013/290321/4/clarisse2019_dust.pdf en eng uri/info:doi/10.1029/2018JD029701 uri/info:scp/85060947485 https://dipot.ulb.ac.be/dspace/bitstream/2013/290321/4/clarisse2019_dust.pdf http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/290321 1 full-text file(s): info:eu-repo/semantics/openAccess Journal of Geophysical Research: Atmospheres, 124 (3 Sciences de la terre et du cosmos Géographie physique Phénomènes atmosphériques Sciences de l'espace info:eu-repo/semantics/article info:ulb-repo/semantics/articlePeerReview info:ulb-repo/semantics/openurl/article 2019 ftunivbruxelles 2022-06-12T22:05:38Z Aerosol is an important component of the Earth's atmosphere, affecting weather, climate, and diverse elements of the biosphere. Satellite sounders are an essential tool for measuring the highly variable distributions of atmospheric aerosol. Here we present a new algorithm for estimating atmospheric dust optical depths and associated retrieval uncertainties from spectral radiance measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The retrieval is based on the calculation of a dust index and on a neural network trained with synthetic IASI spectra. It has an inherent high sensitivity to dust and efficiently discriminates dust from other aerosols. In particular, over remote dust-free areas, the retrieved levels of optical depth have a low bias. Over sea, noise levels are markedly lower than over land. Performance over deserts is comparable to that of other land surfaces. We use ground-based coarse mode aerosol measurements from the AErosol RObotic NETwork to validate the new product. The overall assessment is favorable, with standard deviations in line with estimated uncertainties, low biases, and high correlation coefficients. However, a systematic relative bias occurs between sites dominated by African and Asian dust sources respectively, likely linked to differences in mineralogy. The retrieval has been performed on over a decade of IASI data, and the resulting data set is now publicly available. We present a global seasonal dust climatology based on this record and compare it with those obtained from independent satellite measurements (Moderate Resolution Imaging Spectroradiometer and a third-party IASI product) and dust optical depth from the ECMWF model. SCOPUS: ar.j info:eu-repo/semantics/published Article in Journal/Newspaper Aerosol Robotic Network DI-fusion : dépôt institutionnel de l'Université libre de Bruxelles (ULB)
spellingShingle Sciences de la terre et du cosmos
Géographie physique
Phénomènes atmosphériques
Sciences de l'espace
Clarisse, Lieven
Clerbaux, Cathy
Franco, Bruno
Hadji-Lazaro, Juliette
Whitburn, Simon
Kopp, Andreas
Hurtmans, Daniel
Coheur, Pierre
A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements
title A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements
title_full A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements
title_fullStr A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements
title_full_unstemmed A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements
title_short A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements
title_sort decadal data set of global atmospheric dust retrieved from iasi satellite measurements
topic Sciences de la terre et du cosmos
Géographie physique
Phénomènes atmosphériques
Sciences de l'espace
topic_facet Sciences de la terre et du cosmos
Géographie physique
Phénomènes atmosphériques
Sciences de l'espace
url http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/290321
https://dipot.ulb.ac.be/dspace/bitstream/2013/290321/4/clarisse2019_dust.pdf