Assimilation of GNSS RO and FY-4A AGRI data based on PBL-type-dependent background error covariances and its impact on short-term squall-line rainfall prediction.
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| Title: | Assimilation of GNSS RO and FY-4A AGRI data based on PBL-type-dependent background error covariances and its impact on short-term squall-line rainfall prediction. |
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| Authors: | Xu, X.1,2 (AUTHOR), Zou, X.3 (AUTHOR) xzou@nuist.edu.cn |
| Source: | Monthly Weather Review. Jun2026, Vol. 154 Issue 6, p1-22. 22p. |
| Subjects: | Atmospheric boundary layer, Data assimilation, Global Positioning System, Precipitation forecasting |
| Geographic Terms: | China |
| Abstract: | The atmospheric planetary boundary layer (PBL) plays a key role in controlling surface heating by solar radiation to influence atmospheric instability, convective initiation, and the location and intensity of precipitation. In this study, the PBL-type-dependent background error covariances (BECs) are first constructed separately for the convective, near-neutral, stable, and all-type boundary layers. The convective boundary layer exhibits the longest vertical length scales and the largest standard deviations of both temperature and relative humidity error among the four BECs. The PBL-type-dependent BECs are then employed in the assimilation of GNSS radio occultation (RO) refractivity observations from FY-3D/FY-3E, MetOp-B/MetOp-C, COSMIC-2, SPIRE, and GeoOptics, together with brightness temperature observations at channels 9 (6.25 μm) and 10 (7.1 μm) from the Advanced Geostationary Radiation Imager (AGRI) onboard FY-4A, aiming at improving quantitative precipitation forecasts (QPFs) of squall-line cases over eastern China. Compared with the data assimilation experiment using the traditional all-type BEC, the PBL-type-dependent data assimilation experiment produced a moister lower atmosphere and a higher PBL, and a slightly shallower 500-hPa trough and a farther northward cold front, resulting in a more northward precipitation forecast consistent with observations. Higher QPF threat scores, higher probabilities of detection, and lower false alarm rates confirm the benefit of using PBL-type-dependent BECs in the assimilation of GNSS RO and FY-4A AGRI data. Additional ensemble forecasts further demonstrate the robustness of these improvements, with consistent increases in threat scores across multiple cases and forecast lead times, indicating reduced model spin-up and more balanced initial conditions with PBL-type-dependent BECs. [ABSTRACT FROM AUTHOR] |
| Copyright of Monthly Weather Review 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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| Items | – Name: Title Label: Title Group: Ti Data: Assimilation of GNSS RO and FY-4A AGRI data based on PBL-type-dependent background error covariances and its impact on short-term squall-line rainfall prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xu%2C+X%2E%22">Xu, X.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zou%2C+X%2E%22">Zou, X.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> xzou@nuist.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Monthly+Weather+Review%22">Monthly Weather Review</searchLink>. Jun2026, Vol. 154 Issue 6, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Atmospheric+boundary+layer%22">Atmospheric boundary layer</searchLink><br /><searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Global+Positioning+System%22">Global Positioning System</searchLink><br /><searchLink fieldCode="DE" term="%22Precipitation+forecasting%22">Precipitation forecasting</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The atmospheric planetary boundary layer (PBL) plays a key role in controlling surface heating by solar radiation to influence atmospheric instability, convective initiation, and the location and intensity of precipitation. In this study, the PBL-type-dependent background error covariances (BECs) are first constructed separately for the convective, near-neutral, stable, and all-type boundary layers. The convective boundary layer exhibits the longest vertical length scales and the largest standard deviations of both temperature and relative humidity error among the four BECs. The PBL-type-dependent BECs are then employed in the assimilation of GNSS radio occultation (RO) refractivity observations from FY-3D/FY-3E, MetOp-B/MetOp-C, COSMIC-2, SPIRE, and GeoOptics, together with brightness temperature observations at channels 9 (6.25 μm) and 10 (7.1 μm) from the Advanced Geostationary Radiation Imager (AGRI) onboard FY-4A, aiming at improving quantitative precipitation forecasts (QPFs) of squall-line cases over eastern China. Compared with the data assimilation experiment using the traditional all-type BEC, the PBL-type-dependent data assimilation experiment produced a moister lower atmosphere and a higher PBL, and a slightly shallower 500-hPa trough and a farther northward cold front, resulting in a more northward precipitation forecast consistent with observations. Higher QPF threat scores, higher probabilities of detection, and lower false alarm rates confirm the benefit of using PBL-type-dependent BECs in the assimilation of GNSS RO and FY-4A AGRI data. Additional ensemble forecasts further demonstrate the robustness of these improvements, with consistent increases in threat scores across multiple cases and forecast lead times, indicating reduced model spin-up and more balanced initial conditions with PBL-type-dependent BECs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Monthly Weather Review 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/MWR-D-25-0149.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Atmospheric boundary layer Type: general – SubjectFull: Data assimilation Type: general – SubjectFull: Global Positioning System Type: general – SubjectFull: Precipitation forecasting Type: general – SubjectFull: China Type: general Titles: – TitleFull: Assimilation of GNSS RO and FY-4A AGRI data based on PBL-type-dependent background error covariances and its impact on short-term squall-line rainfall prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xu, X. – PersonEntity: Name: NameFull: Zou, X. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00270644 Numbering: – Type: volume Value: 154 – Type: issue Value: 6 Titles: – TitleFull: Monthly Weather Review Type: main |
| ResultId | 1 |
