Retrieval of daytime mesospheric ozone using OSIRIS observations of O<sub>2</sub> (<i>a</i><sup>1</sup>Δ<sub>g</sub>) emission

<p>Improving knowledge of the ozone global distributions in the mesosphere–lower thermosphere (MLT) is a crucial step in understanding the behaviour of the middle atmosphere. However, the concentration of ozone under sunlit conditions in the MLT is often so low that its measurement requires in...

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Main Authors: A. Li, C. Z. Roth, K. Pérot, O. M. Christensen, A. Bourassa, D. A. Degenstein, D. P. Murtagh
Format: Article
Language:English
Published: Copernicus Publications 2020-11-01
Series:Atmospheric Measurement Techniques
Online Access:https://amt.copernicus.org/articles/13/6215/2020/amt-13-6215-2020.pdf
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spelling doaj-8245cd9269ff43d2a5608010c96034e12020-11-25T04:03:09ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482020-11-01136215623610.5194/amt-13-6215-2020Retrieval of daytime mesospheric ozone using OSIRIS observations of O<sub>2</sub> (<i>a</i><sup>1</sup>Δ<sub>g</sub>) emissionA. Li0C. Z. Roth1K. Pérot2O. M. Christensen3A. Bourassa4D. A. Degenstein5D. P. Murtagh6Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, SwedenInstitute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, CanadaDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, SwedenDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, SwedenInstitute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, CanadaInstitute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, CanadaDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden<p>Improving knowledge of the ozone global distributions in the mesosphere–lower thermosphere (MLT) is a crucial step in understanding the behaviour of the middle atmosphere. However, the concentration of ozone under sunlit conditions in the MLT is often so low that its measurement requires instruments with very high sensitivity. Fortunately, the bright oxygen airglow can serve as a proxy to retrieve the daytime ozone density indirectly, due to the strong connection to ozone photolysis in the Hartley band. The OSIRIS IR imager (hereafter, IRI), one of the instruments on the Odin satellite, routinely measures the oxygen infrared atmospheric band (IRA band) at 1.27&thinsp;<span class="inline-formula">µ</span>m. In this paper, we will primarily focus on the detailed description of the steps done for retrieving the calibrated IRA band limb radiance (with <span class="inline-formula">&lt;10</span>&thinsp;% random error), the volume emission rate of <span class="inline-formula">O<sub>2</sub></span> <span class="inline-formula">(<i>a</i><sup>1</sup>Δ<sub>g</sub>)</span> (with <span class="inline-formula">&lt;25</span>&thinsp;% random error) and finally the ozone number density (with <span class="inline-formula">&lt;20</span>&thinsp;% random error). This retrieval technique is applied to a 1-year sample from the IRI dataset. The resulting product is a new ozone dataset with very tight along-track sampling distance (<span class="inline-formula">&lt;20</span>&thinsp;<span class="inline-formula">km</span>). The feasibility of the retrieval technique is demonstrated by a comparison of coincident ozone measurements from other instruments aboard the same spacecraft, as well as zonal mean and monthly average comparisons between Odin-OSIRIS (both spectrograph and IRI), Odin-SMR and Envisat-MIPAS. We find that IRI appears to have a positive bias of up to 25&thinsp;% below 75&thinsp;<span class="inline-formula">km</span>, and up to 50&thinsp;% in some regions above. We attribute these differences to uncertainty in the IRI calibration as well as uncertainties in the photochemical constants. However, the IRI ozone dataset is consistent with the compared dataset in terms of the overall atmospheric distribution of ozone between 50 and 100&thinsp;<span class="inline-formula">km</span>. If the origin of the bias can be identified before processing the entire dataset, this will be corrected and noted in the dataset description. The retrieval technique described in this paper can be further applied to all the measurements made throughout the 19 year mission, leading to a new, long-term high-resolution ozone dataset in the middle atmosphere.</p>https://amt.copernicus.org/articles/13/6215/2020/amt-13-6215-2020.pdf
collection DOAJ
language English
format Article
sources DOAJ
author A. Li
C. Z. Roth
K. Pérot
O. M. Christensen
A. Bourassa
D. A. Degenstein
D. P. Murtagh
spellingShingle A. Li
C. Z. Roth
K. Pérot
O. M. Christensen
A. Bourassa
D. A. Degenstein
D. P. Murtagh
Retrieval of daytime mesospheric ozone using OSIRIS observations of O<sub>2</sub> (<i>a</i><sup>1</sup>Δ<sub>g</sub>) emission
Atmospheric Measurement Techniques
author_facet A. Li
C. Z. Roth
K. Pérot
O. M. Christensen
A. Bourassa
D. A. Degenstein
D. P. Murtagh
author_sort A. Li
title Retrieval of daytime mesospheric ozone using OSIRIS observations of O<sub>2</sub> (<i>a</i><sup>1</sup>Δ<sub>g</sub>) emission
title_short Retrieval of daytime mesospheric ozone using OSIRIS observations of O<sub>2</sub> (<i>a</i><sup>1</sup>Δ<sub>g</sub>) emission
title_full Retrieval of daytime mesospheric ozone using OSIRIS observations of O<sub>2</sub> (<i>a</i><sup>1</sup>Δ<sub>g</sub>) emission
title_fullStr Retrieval of daytime mesospheric ozone using OSIRIS observations of O<sub>2</sub> (<i>a</i><sup>1</sup>Δ<sub>g</sub>) emission
title_full_unstemmed Retrieval of daytime mesospheric ozone using OSIRIS observations of O<sub>2</sub> (<i>a</i><sup>1</sup>Δ<sub>g</sub>) emission
title_sort retrieval of daytime mesospheric ozone using osiris observations of o<sub>2</sub> (<i>a</i><sup>1</sup>δ<sub>g</sub>) emission
publisher Copernicus Publications
series Atmospheric Measurement Techniques
issn 1867-1381
1867-8548
publishDate 2020-11-01
description <p>Improving knowledge of the ozone global distributions in the mesosphere–lower thermosphere (MLT) is a crucial step in understanding the behaviour of the middle atmosphere. However, the concentration of ozone under sunlit conditions in the MLT is often so low that its measurement requires instruments with very high sensitivity. Fortunately, the bright oxygen airglow can serve as a proxy to retrieve the daytime ozone density indirectly, due to the strong connection to ozone photolysis in the Hartley band. The OSIRIS IR imager (hereafter, IRI), one of the instruments on the Odin satellite, routinely measures the oxygen infrared atmospheric band (IRA band) at 1.27&thinsp;<span class="inline-formula">µ</span>m. In this paper, we will primarily focus on the detailed description of the steps done for retrieving the calibrated IRA band limb radiance (with <span class="inline-formula">&lt;10</span>&thinsp;% random error), the volume emission rate of <span class="inline-formula">O<sub>2</sub></span> <span class="inline-formula">(<i>a</i><sup>1</sup>Δ<sub>g</sub>)</span> (with <span class="inline-formula">&lt;25</span>&thinsp;% random error) and finally the ozone number density (with <span class="inline-formula">&lt;20</span>&thinsp;% random error). This retrieval technique is applied to a 1-year sample from the IRI dataset. The resulting product is a new ozone dataset with very tight along-track sampling distance (<span class="inline-formula">&lt;20</span>&thinsp;<span class="inline-formula">km</span>). The feasibility of the retrieval technique is demonstrated by a comparison of coincident ozone measurements from other instruments aboard the same spacecraft, as well as zonal mean and monthly average comparisons between Odin-OSIRIS (both spectrograph and IRI), Odin-SMR and Envisat-MIPAS. We find that IRI appears to have a positive bias of up to 25&thinsp;% below 75&thinsp;<span class="inline-formula">km</span>, and up to 50&thinsp;% in some regions above. We attribute these differences to uncertainty in the IRI calibration as well as uncertainties in the photochemical constants. However, the IRI ozone dataset is consistent with the compared dataset in terms of the overall atmospheric distribution of ozone between 50 and 100&thinsp;<span class="inline-formula">km</span>. If the origin of the bias can be identified before processing the entire dataset, this will be corrected and noted in the dataset description. The retrieval technique described in this paper can be further applied to all the measurements made throughout the 19 year mission, leading to a new, long-term high-resolution ozone dataset in the middle atmosphere.</p>
url https://amt.copernicus.org/articles/13/6215/2020/amt-13-6215-2020.pdf
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