Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG
The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We emp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/1/116 |
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| author | Lucie Leonarski Laurent C.-Labonnote Mathieu Compiègne Jérôme Vidot Anthony J. Baran Philippe Dubuisson |
| author_facet | Lucie Leonarski Laurent C.-Labonnote Mathieu Compiègne Jérôme Vidot Anthony J. Baran Philippe Dubuisson |
| author_sort | Lucie Leonarski |
| collection | DOAJ |
| description | The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (<inline-formula><math display="inline"><semantics><mrow><mi>I</mi><mi>W</mi><mi>P</mi></mrow></semantics></math></inline-formula>) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose <inline-formula><math display="inline"><semantics><mrow><mi>I</mi><mi>W</mi><mi>P</mi></mrow></semantics></math></inline-formula> is between about 1 and 300 <inline-formula><math display="inline"><semantics><mrow><mi mathvariant="normal">g</mi><mo>/</mo><msup><mi mathvariant="normal">m</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>). |
| first_indexed | 2024-03-10T13:36:05Z |
| format | Article |
| id | doaj.art-b137ebfe9e73470a85525ce8e22fa61f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:36:05Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b137ebfe9e73470a85525ce8e22fa61f2023-11-21T07:33:40ZengMDPI AGRemote Sensing2072-42922020-12-0113111610.3390/rs13010116Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NGLucie Leonarski0Laurent C.-Labonnote1Mathieu Compiègne2Jérôme Vidot3Anthony J. Baran4Philippe Dubuisson5CNRS, UMR 8518—LOA—Laboratoire d’Optique Atmosphérique, University of Lille, F-59000 Lille, FranceCNRS, UMR 8518—LOA—Laboratoire d’Optique Atmosphérique, University of Lille, F-59000 Lille, FranceHygeos, F-59000 Lille, FranceCNRM, Université de Toulouse, Météo-France, CNRS, F-22300 Lannion, FranceMet Office, Exeter EX1 3PB, UKCNRS, UMR 8518—LOA—Laboratoire d’Optique Atmosphérique, University of Lille, F-59000 Lille, FranceThe present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (<inline-formula><math display="inline"><semantics><mrow><mi>I</mi><mi>W</mi><mi>P</mi></mrow></semantics></math></inline-formula>) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose <inline-formula><math display="inline"><semantics><mrow><mi>I</mi><mi>W</mi><mi>P</mi></mrow></semantics></math></inline-formula> is between about 1 and 300 <inline-formula><math display="inline"><semantics><mrow><mi mathvariant="normal">g</mi><mo>/</mo><msup><mi mathvariant="normal">m</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>).https://www.mdpi.com/2072-4292/13/1/116ice cloudsthermal infraredretrieval of geophysical parameters from spectral radiance measurements |
| spellingShingle | Lucie Leonarski Laurent C.-Labonnote Mathieu Compiègne Jérôme Vidot Anthony J. Baran Philippe Dubuisson Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG Remote Sensing ice clouds thermal infrared retrieval of geophysical parameters from spectral radiance measurements |
| title | Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG |
| title_full | Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG |
| title_fullStr | Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG |
| title_full_unstemmed | Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG |
| title_short | Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG |
| title_sort | potential of hyperspectral thermal infrared spaceborne measurements to retrieve ice cloud physical properties case study of iasi and iasi ng |
| topic | ice clouds thermal infrared retrieval of geophysical parameters from spectral radiance measurements |
| url | https://www.mdpi.com/2072-4292/13/1/116 |
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