Direct Assimilation of GOES-16 ABI All-Sky Radiances in HAFS Self-Cycled Dual-Resolution 3DEnVar System: System Description and a Case Study of Hurricane Laura (2020).
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| Title: | Direct Assimilation of GOES-16 ABI All-Sky Radiances in HAFS Self-Cycled Dual-Resolution 3DEnVar System: System Description and a Case Study of Hurricane Laura (2020). |
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| Authors: | Yang, Yue1 (AUTHOR) yue.yang@ou.edu, Wang, Xuguang1 (AUTHOR) |
| Source: | Weather & Forecasting. Jan2026, Vol. 41 Issue 1, p187-203. 17p. |
| Subjects: | Brightness temperature, Cyclone forecasting, Data assimilation, Multisensor data fusion, Hurricanes, Remote sensing devices, Meteorological satellites |
| Abstract: | Compared to utilizing temperature and mixing ratios of water vapor and hydrometeors as state variables (Standard_state), the direct assimilation of GOES-16 Advanced Baseline Imager (ABI) channel-10 all-sky radiances is advanced by additionally including brightness temperature (BT) as a state variable (BT_state) within the Hurricane Analysis and Forecast System (HAFS) self-cycled dual-resolution three-dimensional ensemble–variational (3DEnVar) data assimilation (DA) system. With a focus on the rapid intensification (RI) predictions for Hurricane Laura (2020), the ABI all-sky radiances and operational observations are simultaneously assimilated during the pre-RI to RI period. Statistical comparisons across multiple DA cycles demonstrate the superiority of BT_state over Standard_state in terms of objective alignment with observations for background, analyses, and forecasts. Compared to Standard_state, the closer fit of BT_state to observations benefits from better convergence and faster variational minimization. The performances of the background, analyses, and forecasts from Standard_state and BT_state are verified subjectively against various observations. During the DA period, the background and analysis from BT_state outperform Standard_state in capturing the storm size, surrounding clear-sky areas, and the wind field structure within the storm region. Additionally, BT_state yields higher forecast skill than Standard_state for the intensity, track, and main structural features of Laura. Detailed diagnostics suggest that the enhanced thermodynamic structure analysis in BT_state facilitates the RI prediction, and the improved large-scale environment analysis in BT_state contributes to the accurate track prediction. Significance Statement: Given the high spatiotemporal resolution, all-sky infrared satellite radiances have become potential observations for data assimilation (DA) to improve tropical cyclone forecasts. However, only clear-sky radiances are involved in current operational prediction systems for hurricanes due to challenges in assimilating cloudy radiances. Including brightness temperature as a state variable (BT_state) can address the convergence issue related to the linearization of the nonlinear observation operator in variational algorithms. This study extends the BT_state into a self-cycled three-dimensional variational DA system that employs flow-dependent error relationships estimated from ensemble forecasts to assimilate all-sky radiances and routine operational observations simultaneously. The approach is tested for a hurricane case characterized by rapid intensification. Results support the superiority of BT_state and physically explain the improvements. [ABSTRACT FROM AUTHOR] |
| Copyright of Weather & Forecasting is the property of American Meteorological Society 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: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 191287159 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Direct Assimilation of GOES-16 ABI All-Sky Radiances in HAFS Self-Cycled Dual-Resolution 3DEnVar System: System Description and a Case Study of Hurricane Laura (2020). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Yue%22">Yang, Yue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yue.yang@ou.edu</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Xuguang%22">Wang, Xuguang</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Weather+%26+Forecasting%22">Weather & Forecasting</searchLink>. Jan2026, Vol. 41 Issue 1, p187-203. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Brightness+temperature%22">Brightness temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Cyclone+forecasting%22">Cyclone forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Hurricanes%22">Hurricanes</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing+devices%22">Remote sensing devices</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+satellites%22">Meteorological satellites</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Compared to utilizing temperature and mixing ratios of water vapor and hydrometeors as state variables (Standard_state), the direct assimilation of GOES-16 Advanced Baseline Imager (ABI) channel-10 all-sky radiances is advanced by additionally including brightness temperature (BT) as a state variable (BT_state) within the Hurricane Analysis and Forecast System (HAFS) self-cycled dual-resolution three-dimensional ensemble–variational (3DEnVar) data assimilation (DA) system. With a focus on the rapid intensification (RI) predictions for Hurricane Laura (2020), the ABI all-sky radiances and operational observations are simultaneously assimilated during the pre-RI to RI period. Statistical comparisons across multiple DA cycles demonstrate the superiority of BT_state over Standard_state in terms of objective alignment with observations for background, analyses, and forecasts. Compared to Standard_state, the closer fit of BT_state to observations benefits from better convergence and faster variational minimization. The performances of the background, analyses, and forecasts from Standard_state and BT_state are verified subjectively against various observations. During the DA period, the background and analysis from BT_state outperform Standard_state in capturing the storm size, surrounding clear-sky areas, and the wind field structure within the storm region. Additionally, BT_state yields higher forecast skill than Standard_state for the intensity, track, and main structural features of Laura. Detailed diagnostics suggest that the enhanced thermodynamic structure analysis in BT_state facilitates the RI prediction, and the improved large-scale environment analysis in BT_state contributes to the accurate track prediction. Significance Statement: Given the high spatiotemporal resolution, all-sky infrared satellite radiances have become potential observations for data assimilation (DA) to improve tropical cyclone forecasts. However, only clear-sky radiances are involved in current operational prediction systems for hurricanes due to challenges in assimilating cloudy radiances. Including brightness temperature as a state variable (BT_state) can address the convergence issue related to the linearization of the nonlinear observation operator in variational algorithms. This study extends the BT_state into a self-cycled three-dimensional variational DA system that employs flow-dependent error relationships estimated from ensemble forecasts to assimilate all-sky radiances and routine operational observations simultaneously. The approach is tested for a hurricane case characterized by rapid intensification. Results support the superiority of BT_state and physically explain the improvements. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Weather & Forecasting is the property of American Meteorological Society 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: 10.1175/WAF-D-25-0055.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 187 Subjects: – SubjectFull: Brightness temperature Type: general – SubjectFull: Cyclone forecasting Type: general – SubjectFull: Data assimilation Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Hurricanes Type: general – SubjectFull: Remote sensing devices Type: general – SubjectFull: Meteorological satellites Type: general Titles: – TitleFull: Direct Assimilation of GOES-16 ABI All-Sky Radiances in HAFS Self-Cycled Dual-Resolution 3DEnVar System: System Description and a Case Study of Hurricane Laura (2020). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Yue – PersonEntity: Name: NameFull: Wang, Xuguang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08828156 Numbering: – Type: volume Value: 41 – Type: issue Value: 1 Titles: – TitleFull: Weather & Forecasting Type: main |
| ResultId | 1 |
