A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources

A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources

<p>Sensitive and accurate detection of sulfur dioxide (SO<span class="inline-formula"><sub>2</sub></span>) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present...

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Autores principales: N. Theys, V. Fioletov, C. Li, I. De Smedt, C. Lerot, C. McLinden, N. Krotkov, D. Griffin, L. Clarisse, P. Hedelt, D. Loyola, T. Wagner, V. Kumar, A. Innes, R. Ribas, F. Hendrick, J. Vlietinck, H. Brenot, M. Van Roozendael
Formato: article
Lenguaje:EN
Publicado: Copernicus Publications 2021
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Physics
QC1-999
Chemistry
QD1-999
Acceso en línea:https://doaj.org/article/e95768eaa5b34689a1188007083db0dd
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spelling oai:doaj.org-article:e95768eaa5b34689a1188007083db0dd2021-11-17T12:38:17ZA sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources10.5194/acp-21-16727-20211680-73161680-7324https://doaj.org/article/e95768eaa5b34689a1188007083db0dd2021-11-01T00:00:00Zhttps://acp.copernicus.org/articles/21/16727/2021/acp-21-16727-2021.pdfhttps://doaj.org/toc/1680-7316https://doaj.org/toc/1680-7324<p>Sensitive and accurate detection of sulfur dioxide (SO<span class="inline-formula"><sub>2</sub></span>) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present here a new scheme to retrieve SO<span class="inline-formula"><sub>2</sub></span> columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO<span class="inline-formula"><sub>2</sub></span>-free radiance variability, so that the SO<span class="inline-formula"><sub>2</sub></span> slant column density is the only retrieved parameter of the algorithm. We demonstrate this approach, named COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method reduces significantly both the noise and biases present in the current TROPOMI operational DOAS SO<span class="inline-formula"><sub>2</sub></span> retrievals. The performance of this technique is also benchmarked against that of the principal component algorithm (PCA) approach. We find that the quality of the data is similar and even slightly better with the proposed COBRA approach. The ability of the algorithm to retrieve SO<span class="inline-formula"><sub>2</sub></span> accurately is further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO<span class="inline-formula"><sub>2</sub></span> map, considering 2.5 years of TROPOMI data. In addition to the known sources, we detect many new SO<span class="inline-formula"><sub>2</sub></span> emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO<span class="inline-formula"><sub>2</sub></span> emission rates. Results are comparable to other recently published TROPOMI-based SO<span class="inline-formula"><sub>2</sub></span> emissions estimates, but the associated uncertainties are significantly lower than with the operational data. Next, for a limited number of weak sources, we demonstrate the potential of our data for quantifying SO<span class="inline-formula"><sub>2</sub></span> emissions with a detection limit of about 8 kt yr<span class="inline-formula"><sup>−1</sup></span>, a factor of 4 better than the emissions derived from the Ozone Monitoring Instrument (OMI). We anticipate that the systematic use of our TROPOMI COBRA SO<span class="inline-formula"><sub>2</sub></span> column data set at a global scale will allow missing sources to be identified and quantified and help improve SO<span class="inline-formula"><sub>2</sub></span> emission inventories.</p>N. TheysV. FioletovC. LiC. LiI. De SmedtC. LerotC. McLindenN. KrotkovD. GriffinL. ClarisseP. HedeltD. LoyolaT. WagnerV. KumarA. InnesR. RibasF. HendrickJ. VlietinckH. BrenotM. Van RoozendaelCopernicus PublicationsarticlePhysicsQC1-999ChemistryQD1-999ENAtmospheric Chemistry and Physics, Vol 21, Pp 16727-16744 (2021)
institution DOAJ
collection DOAJ
language EN
topic Physics
QC1-999
Chemistry
QD1-999
spellingShingle Physics
QC1-999
Chemistry
QD1-999
N. Theys
V. Fioletov
C. Li
C. Li
I. De Smedt
C. Lerot
C. McLinden
N. Krotkov
D. Griffin
L. Clarisse
P. Hedelt
D. Loyola
T. Wagner
V. Kumar
A. Innes
R. Ribas
F. Hendrick
J. Vlietinck
H. Brenot
M. Van Roozendael
A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources
description <p>Sensitive and accurate detection of sulfur dioxide (SO<span class="inline-formula"><sub>2</sub></span>) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present here a new scheme to retrieve SO<span class="inline-formula"><sub>2</sub></span> columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO<span class="inline-formula"><sub>2</sub></span>-free radiance variability, so that the SO<span class="inline-formula"><sub>2</sub></span> slant column density is the only retrieved parameter of the algorithm. We demonstrate this approach, named COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method reduces significantly both the noise and biases present in the current TROPOMI operational DOAS SO<span class="inline-formula"><sub>2</sub></span> retrievals. The performance of this technique is also benchmarked against that of the principal component algorithm (PCA) approach. We find that the quality of the data is similar and even slightly better with the proposed COBRA approach. The ability of the algorithm to retrieve SO<span class="inline-formula"><sub>2</sub></span> accurately is further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO<span class="inline-formula"><sub>2</sub></span> map, considering 2.5 years of TROPOMI data. In addition to the known sources, we detect many new SO<span class="inline-formula"><sub>2</sub></span> emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO<span class="inline-formula"><sub>2</sub></span> emission rates. Results are comparable to other recently published TROPOMI-based SO<span class="inline-formula"><sub>2</sub></span> emissions estimates, but the associated uncertainties are significantly lower than with the operational data. Next, for a limited number of weak sources, we demonstrate the potential of our data for quantifying SO<span class="inline-formula"><sub>2</sub></span> emissions with a detection limit of about 8 kt yr<span class="inline-formula"><sup>−1</sup></span>, a factor of 4 better than the emissions derived from the Ozone Monitoring Instrument (OMI). We anticipate that the systematic use of our TROPOMI COBRA SO<span class="inline-formula"><sub>2</sub></span> column data set at a global scale will allow missing sources to be identified and quantified and help improve SO<span class="inline-formula"><sub>2</sub></span> emission inventories.</p>
format article
author N. Theys
V. Fioletov
C. Li
C. Li
I. De Smedt
C. Lerot
C. McLinden
N. Krotkov
D. Griffin
L. Clarisse
P. Hedelt
D. Loyola
T. Wagner
V. Kumar
A. Innes
R. Ribas
F. Hendrick
J. Vlietinck
H. Brenot
M. Van Roozendael
author_facet N. Theys
V. Fioletov
C. Li
C. Li
I. De Smedt
C. Lerot
C. McLinden
N. Krotkov
D. Griffin
L. Clarisse
P. Hedelt
D. Loyola
T. Wagner
V. Kumar
A. Innes
R. Ribas
F. Hendrick
J. Vlietinck
H. Brenot
M. Van Roozendael
author_sort N. Theys
title A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources
title_short A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources
title_full A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources
title_fullStr A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources
title_full_unstemmed A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources
title_sort sulfur dioxide covariance-based retrieval algorithm (cobra): application to tropomi reveals new emission sources
publisher Copernicus Publications
publishDate 2021
url https://doaj.org/article/e95768eaa5b34689a1188007083db0dd
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