Cloud detection for MIPAS using singular vector decomposition
Clouds are increasingly recognised for their influence on the radiative balance of the Earth and the implications that they have on possible climate change, as well as in air pollution and acid-rain production. However, clouds remain a major source of uncertainty in climate models. Satellite-borne h...
| Những tác giả chính: | , , |
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| Định dạng: | Journal article |
| Ngôn ngữ: | English |
| Được phát hành: |
Copernicus Publications
2009
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| Những chủ đề: |
| _version_ | 1826294504747958272 |
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| author | Hurley, J Dudhia, A Grainger, D |
| author2 | European Geosciences Union |
| author_facet | European Geosciences Union Hurley, J Dudhia, A Grainger, D |
| author_sort | Hurley, J |
| collection | OXFORD |
| description | Clouds are increasingly recognised for their influence on the radiative balance of the Earth and the implications that they have on possible climate change, as well as in air pollution and acid-rain production. However, clouds remain a major source of uncertainty in climate models. Satellite-borne high-resolution limb sounders, such as the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT, provide information on clouds, especially optically thin clouds, which have been difficult to observe in the past. The aim of this work is to develop, implement and test a reliable cloud detection method for infrared spectra measured by MIPAS. Current MIPAS cloud detection methods used operationally have been developed to detect thick cloud filling more than 30% of the measurement field-of-view (FOV). In order to resolve thin clouds, a new detection method using Singular Vector Decomposition (SVD) is formulated and tested. A rigorous comparison of the current operational and newly-developed detection methods for MIPAS is carried out - and the new SVD detection method has been proven to be much more reliable than the current operational method, and very sensitive even to thin clouds only marginally filling the MIPAS FOV. |
| first_indexed | 2024-03-07T03:46:40Z |
| format | Journal article |
| id | oxford-uuid:bfb6ecbd-d961-4e4a-a426-2a6c71a48cf7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:46:40Z |
| publishDate | 2009 |
| publisher | Copernicus Publications |
| record_format | dspace |
| spelling | oxford-uuid:bfb6ecbd-d961-4e4a-a426-2a6c71a48cf72022-03-27T05:49:29ZCloud detection for MIPAS using singular vector decompositionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:bfb6ecbd-d961-4e4a-a426-2a6c71a48cf7Atmospheric,Oceanic,and Planetary physicsPhysicsEnglishOxford University Research Archive - ValetCopernicus Publications2009Hurley, JDudhia, AGrainger, DEuropean Geosciences UnionClouds are increasingly recognised for their influence on the radiative balance of the Earth and the implications that they have on possible climate change, as well as in air pollution and acid-rain production. However, clouds remain a major source of uncertainty in climate models. Satellite-borne high-resolution limb sounders, such as the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT, provide information on clouds, especially optically thin clouds, which have been difficult to observe in the past. The aim of this work is to develop, implement and test a reliable cloud detection method for infrared spectra measured by MIPAS. Current MIPAS cloud detection methods used operationally have been developed to detect thick cloud filling more than 30% of the measurement field-of-view (FOV). In order to resolve thin clouds, a new detection method using Singular Vector Decomposition (SVD) is formulated and tested. A rigorous comparison of the current operational and newly-developed detection methods for MIPAS is carried out - and the new SVD detection method has been proven to be much more reliable than the current operational method, and very sensitive even to thin clouds only marginally filling the MIPAS FOV. |
| spellingShingle | Atmospheric,Oceanic,and Planetary physics Physics Hurley, J Dudhia, A Grainger, D Cloud detection for MIPAS using singular vector decomposition |
| title | Cloud detection for MIPAS using singular vector decomposition |
| title_full | Cloud detection for MIPAS using singular vector decomposition |
| title_fullStr | Cloud detection for MIPAS using singular vector decomposition |
| title_full_unstemmed | Cloud detection for MIPAS using singular vector decomposition |
| title_short | Cloud detection for MIPAS using singular vector decomposition |
| title_sort | cloud detection for mipas using singular vector decomposition |
| topic | Atmospheric,Oceanic,and Planetary physics Physics |
| work_keys_str_mv | AT hurleyj clouddetectionformipasusingsingularvectordecomposition AT dudhiaa clouddetectionformipasusingsingularvectordecomposition AT graingerd clouddetectionformipasusingsingularvectordecomposition |