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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Bibliographic Details
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.
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]
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Database:	Engineering Source

Description
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]
ISSN:	00270644
DOI:	10.1175/MWR-D-25-0149.1