Assimilation of Transformed Retrievals From Satellite High‐Resolution Infrared Data Over the Central Pacific Area.
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| Title: | Assimilation of Transformed Retrievals From Satellite High‐Resolution Infrared Data Over the Central Pacific Area. |
|---|---|
| Authors: | Cherubini, Tiziana1 (AUTHOR) tiziana@hawaii.edu, Antonelli, Paolo2 (AUTHOR), Businger, Steven1 (AUTHOR), Scaccia, Paolo2 (AUTHOR) |
| Source: | Journal of Geophysical Research. Atmospheres. 11/16/2023, Vol. 128 Issue 21, p1-23. 23p. |
| Subject Terms: | *Weather forecasting, *Water distribution, Numerical weather forecasting, Kalman filtering, Meteorological research, Water vapor, Brightness temperature |
| Company/Entity: | European Centre for Medium-Range Weather Forecasts (Organization) |
| Abstract: | A month‐long data assimilation experiment is carried out to assess the impact of CrIS and IASI Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3‐hr Weather Research and Forecasting cycling system implemented over the central North Pacific Ocean. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System and the European Centre for Medium‐Range Weather Forecasts analyses are used in the evaluation process. The results show that the assimilation of TRs alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800–300 hPa), and particularly in the lower portion (800–600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade‐wind inversion. Plain Language Summary: A month‐long data assimilation experiment is carried out to assess the impact of hyper‐spectral sensor Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3‐hr Weather Research and Forecasting cycling system implemented over the central North Pacific Ocean. TRs are the result of a mathematical inversion process that compresses the informational content of the hyper‐spectral radiances into a limited number of uncorrelated parameters, and provides an ad hoc observation operator, for their assimilation within Numerical Weather Prediction models. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System and the European Centre for Medium‐Range Weather Forecasts analyses are used in the evaluation process. The results show that the assimilation of TRs, both alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800–300 hPa), and particularly in the lower portion (800–600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade‐wind inversion. Key Points: Hyperspectral radiances are efficiently compressed into a limited number of uncorrelated parameters: the Transformed Retrievals (TRs)Assimilation of TRs is akin to assimilation of physical profilesThe assimilation of TRs results in a higher forecast accuracy in predicting the distribution of the water vapor [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 173516265 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assimilation of Transformed Retrievals From Satellite High‐Resolution Infrared Data Over the Central Pacific Area. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cherubini%2C+Tiziana%22">Cherubini, Tiziana</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tiziana@hawaii.edu</i><br /><searchLink fieldCode="AR" term="%22Antonelli%2C+Paolo%22">Antonelli, Paolo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Businger%2C+Steven%22">Businger, Steven</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Scaccia%2C+Paolo%22">Scaccia, Paolo</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Atmospheres%22">Journal of Geophysical Research. Atmospheres</searchLink>. 11/16/2023, Vol. 128 Issue 21, p1-23. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+distribution%22">Water distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+research%22">Meteorological research</searchLink><br /><searchLink fieldCode="DE" term="%22Water+vapor%22">Water vapor</searchLink><br /><searchLink fieldCode="DE" term="%22Brightness+temperature%22">Brightness temperature</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="DE" term="%22European+Centre+for+Medium-Range+Weather+Forecasts+%28Organization%29%22">European Centre for Medium-Range Weather Forecasts (Organization)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A month‐long data assimilation experiment is carried out to assess the impact of CrIS and IASI Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3‐hr Weather Research and Forecasting cycling system implemented over the central North Pacific Ocean. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System and the European Centre for Medium‐Range Weather Forecasts analyses are used in the evaluation process. The results show that the assimilation of TRs alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800–300 hPa), and particularly in the lower portion (800–600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade‐wind inversion. Plain Language Summary: A month‐long data assimilation experiment is carried out to assess the impact of hyper‐spectral sensor Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3‐hr Weather Research and Forecasting cycling system implemented over the central North Pacific Ocean. TRs are the result of a mathematical inversion process that compresses the informational content of the hyper‐spectral radiances into a limited number of uncorrelated parameters, and provides an ad hoc observation operator, for their assimilation within Numerical Weather Prediction models. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System and the European Centre for Medium‐Range Weather Forecasts analyses are used in the evaluation process. The results show that the assimilation of TRs, both alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800–300 hPa), and particularly in the lower portion (800–600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade‐wind inversion. Key Points: Hyperspectral radiances are efficiently compressed into a limited number of uncorrelated parameters: the Transformed Retrievals (TRs)Assimilation of TRs is akin to assimilation of physical profilesThe assimilation of TRs results in a higher forecast accuracy in predicting the distribution of the water vapor [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2022JD038153 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1 Subjects: – SubjectFull: Weather forecasting Type: general – SubjectFull: Water distribution Type: general – SubjectFull: Numerical weather forecasting Type: general – SubjectFull: Kalman filtering Type: general – SubjectFull: Meteorological research Type: general – SubjectFull: Water vapor Type: general – SubjectFull: Brightness temperature Type: general – SubjectFull: European Centre for Medium-Range Weather Forecasts (Organization) Type: general Titles: – TitleFull: Assimilation of Transformed Retrievals From Satellite High‐Resolution Infrared Data Over the Central Pacific Area. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cherubini, Tiziana – PersonEntity: Name: NameFull: Antonelli, Paolo – PersonEntity: Name: NameFull: Businger, Steven – PersonEntity: Name: NameFull: Scaccia, Paolo IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 11 Text: 11/16/2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 2169897X Numbering: – Type: volume Value: 128 – Type: issue Value: 21 Titles: – TitleFull: Journal of Geophysical Research. Atmospheres Type: main |
| ResultId | 1 |
