A flexible and robust neural network IASI-NH₃
n this paper, we describe a new flexible and robust NH₃ retrieval algorithm from measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The method is based on the calculation of a spectral hyperspectral range index (HRI) and subsequent conversion to NH₃ columns via a neural network...
| Главные авторы: | , , , , , , , , , |
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| Другие авторы: | |
| Формат: | Статья |
| Язык: | en_US |
| Опубликовано: |
American Geophysical Union (AGU)
2017
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| Online-ссылка: | http://hdl.handle.net/1721.1/110353 https://orcid.org/0000-0003-2894-5738 |
| _version_ | 1826190153668886528 |
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| author | Whitburn, S. Van Damme, M. Clarisse, L. Bauduin, S. Hadji-Lazaro, J. Hurtmans, D. Zondlo, M. A. Clerbaux, C. Coheur, P.-F Heald, Colette L. |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Whitburn, S. Van Damme, M. Clarisse, L. Bauduin, S. Hadji-Lazaro, J. Hurtmans, D. Zondlo, M. A. Clerbaux, C. Coheur, P.-F Heald, Colette L. |
| author_sort | Whitburn, S. |
| collection | MIT |
| description | n this paper, we describe a new flexible and robust NH₃ retrieval algorithm from measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The method is based on the calculation of a spectral hyperspectral range index (HRI) and subsequent conversion to NH₃ columns via a neural network. It is an extension of the method presented in Van Damme et al. (2014a) who used lookup tables (LUT) for the radiance-concentration conversion. The new method inherits the advantages of the LUT-based method while providing several significant improvements. These include the following: (1) Complete temperature and humidity vertical profiles can be accounted for. (2) Third-party NH₃ vertical profile information can be used. (3) Reported positive biases of LUT retrieval are reduced, and finally (4) a full measurement uncertainty characterization is provided. A running theme in this study, related to item (2), is the importance of the assumed vertical NH₃ profile. We demonstrate the advantages of allowing variable profile shapes in the retrieval. As an example, we analyze how the retrievals change when all NH₃ is assumed to be confined to the boundary layer. We analyze different averaging procedures in use for NH₃ in the literature, introduced to cope with the variable measurement sensitivity and derive global averaged distributions for the year 2013. A comparison with a GEOS-Chem modeled global distribution is also presented, showing a general good correspondence (within ±3 × 10¹⁵ molecules.cm⁻²) over most of the Northern Hemisphere. However, IASI finds mean columns about 1–1.5 × 10¹⁶ molecules.cm⁻² (∼50–60%) lower than GEOS-Chem for India and the North China plain. |
| first_indexed | 2024-09-23T08:35:54Z |
| format | Article |
| id | mit-1721.1/110353 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:35:54Z |
| publishDate | 2017 |
| publisher | American Geophysical Union (AGU) |
| record_format | dspace |
| spelling | mit-1721.1/1103532022-09-23T13:11:00Z A flexible and robust neural network IASI-NH₃ Whitburn, S. Van Damme, M. Clarisse, L. Bauduin, S. Hadji-Lazaro, J. Hurtmans, D. Zondlo, M. A. Clerbaux, C. Coheur, P.-F Heald, Colette L. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Heald, Colette L. n this paper, we describe a new flexible and robust NH₃ retrieval algorithm from measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The method is based on the calculation of a spectral hyperspectral range index (HRI) and subsequent conversion to NH₃ columns via a neural network. It is an extension of the method presented in Van Damme et al. (2014a) who used lookup tables (LUT) for the radiance-concentration conversion. The new method inherits the advantages of the LUT-based method while providing several significant improvements. These include the following: (1) Complete temperature and humidity vertical profiles can be accounted for. (2) Third-party NH₃ vertical profile information can be used. (3) Reported positive biases of LUT retrieval are reduced, and finally (4) a full measurement uncertainty characterization is provided. A running theme in this study, related to item (2), is the importance of the assumed vertical NH₃ profile. We demonstrate the advantages of allowing variable profile shapes in the retrieval. As an example, we analyze how the retrievals change when all NH₃ is assumed to be confined to the boundary layer. We analyze different averaging procedures in use for NH₃ in the literature, introduced to cope with the variable measurement sensitivity and derive global averaged distributions for the year 2013. A comparison with a GEOS-Chem modeled global distribution is also presented, showing a general good correspondence (within ±3 × 10¹⁵ molecules.cm⁻²) over most of the Northern Hemisphere. However, IASI finds mean columns about 1–1.5 × 10¹⁶ molecules.cm⁻² (∼50–60%) lower than GEOS-Chem for India and the North China plain. United States. National Oceanic and Atmospheric Administration (NA12OAR4310064) 2017-06-28T18:04:19Z 2017-06-28T18:04:19Z 2016-06 2016-01 Article http://purl.org/eprint/type/JournalArticle 2169-8996 2169-897X http://hdl.handle.net/1721.1/110353 Whitburn, S.; Van Damme, M.; Clarisse, L.; Bauduin, S.; Heald, C. L.; Hadji-Lazaro, J.; Hurtmans, D.; Zondlo, M. A.; Clerbaux, C. and Coheur, P.-F. “A Flexible and Robust Neural Network IASI-NH₃ retrieval Algorithm.” Journal of Geophysical Research: Atmospheres 121, no. 11 (June 2016): 6581–6599 ©2016 American Geophysical Union https://orcid.org/0000-0003-2894-5738 en_US http://dx.doi.org/10.1002/2016JD024828 Journal of Geophysical Research: Atmospheres Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Geophysical Union (AGU) MIT Web Domain |
| spellingShingle | Whitburn, S. Van Damme, M. Clarisse, L. Bauduin, S. Hadji-Lazaro, J. Hurtmans, D. Zondlo, M. A. Clerbaux, C. Coheur, P.-F Heald, Colette L. A flexible and robust neural network IASI-NH₃ |
| title | A flexible and robust neural network IASI-NH₃ |
| title_full | A flexible and robust neural network IASI-NH₃ |
| title_fullStr | A flexible and robust neural network IASI-NH₃ |
| title_full_unstemmed | A flexible and robust neural network IASI-NH₃ |
| title_short | A flexible and robust neural network IASI-NH₃ |
| title_sort | flexible and robust neural network iasi nh₃ |
| url | http://hdl.handle.net/1721.1/110353 https://orcid.org/0000-0003-2894-5738 |
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