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Tropical Cyclogenesis in the Western North Pacific Improved by GNSS RO Data Assimilation Using Hybrid 3DEnVar with a Nonlocal Excess Phase Operator.

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Title: Tropical Cyclogenesis in the Western North Pacific Improved by GNSS RO Data Assimilation Using Hybrid 3DEnVar with a Nonlocal Excess Phase Operator.
Authors: Pham Xuan, Quan1 (AUTHOR), Chen, Shu-Ya1 (AUTHOR) shuyachen@ncu.edu.tw, Huang, Ching-Yuang1,2 (AUTHOR), Kuo, Ying-Hwa3 (AUTHOR)
Source: Weather & Forecasting. Nov2025, Vol. 40 Issue 11, p2255-2271. 17p.
Subjects: Cyclone forecasting, Data assimilation, Global Positioning System, Atmospheric boundary layer, Tropical storms, Water vapor, Forecasting methodology
Geographic Terms: North Pacific Ocean
Abstract: Accurate tropical cyclogenesis predictions are crucial for typhoon forecasting and disaster mitigation. Satellite observations are valuable for improving tropical cyclogenesis over the ocean, where there are few traditional radiosonde observations. Global Navigation Satellite System (GNSS) Radio Occultation (RO) data, mostly from Formosa Satellite mission-7/Constellation Observing System for the Meteorology, Ionosphere, and Climate mission-2 (FORMOSAT-7/COSMIC-2), provide extensive coverage of tropical regions with valuable atmospheric boundary layer information due to its deep penetration into the lower tropical troposphere. This study assessed the impact of GNSS RO data assimilation (DA) on the predictions of tropical cyclogenesis using 10 typhoon cases in the western North Pacific from 2020 to 2022. Conventional data and GNSS RO data were assimilated for each case using the WRF hybrid 3D ensemble-variational data assimilation (3DEnVar) system. The results show that including RO data with the nonlocal excess phase operator considerably improves the accuracy of cyclogenesis predictions in terms of both location and timing. Detailed case studies for Typhoons Chanthu (2021) and Hagupit (2020) reveal enhanced midtropospheric moisture by RO data assimilation, highlighting the important role of water vapor in tropical cyclogenesis. Although satellite radiances are also assimilated for both typhoons, the impacts of RO data are still evident in providing improved initial conditions that are more favorable for capturing genesis. For ensemble forecasts, more ensemble members successfully detect Chanthu's genesis and give a higher probability of detection for Typhoon Hagupit when RO data are included. The positive ensemble impacts highlight the fact that the RO data with the nonlocal operator may give steady improvement for operational forecasting of tropical cyclogenesis. [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.)
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  Data: Tropical Cyclogenesis in the Western North Pacific Improved by GNSS RO Data Assimilation Using Hybrid 3DEnVar with a Nonlocal Excess Phase Operator.
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  Data: Accurate tropical cyclogenesis predictions are crucial for typhoon forecasting and disaster mitigation. Satellite observations are valuable for improving tropical cyclogenesis over the ocean, where there are few traditional radiosonde observations. Global Navigation Satellite System (GNSS) Radio Occultation (RO) data, mostly from Formosa Satellite mission-7/Constellation Observing System for the Meteorology, Ionosphere, and Climate mission-2 (FORMOSAT-7/COSMIC-2), provide extensive coverage of tropical regions with valuable atmospheric boundary layer information due to its deep penetration into the lower tropical troposphere. This study assessed the impact of GNSS RO data assimilation (DA) on the predictions of tropical cyclogenesis using 10 typhoon cases in the western North Pacific from 2020 to 2022. Conventional data and GNSS RO data were assimilated for each case using the WRF hybrid 3D ensemble-variational data assimilation (3DEnVar) system. The results show that including RO data with the nonlocal excess phase operator considerably improves the accuracy of cyclogenesis predictions in terms of both location and timing. Detailed case studies for Typhoons Chanthu (2021) and Hagupit (2020) reveal enhanced midtropospheric moisture by RO data assimilation, highlighting the important role of water vapor in tropical cyclogenesis. Although satellite radiances are also assimilated for both typhoons, the impacts of RO data are still evident in providing improved initial conditions that are more favorable for capturing genesis. For ensemble forecasts, more ensemble members successfully detect Chanthu's genesis and give a higher probability of detection for Typhoon Hagupit when RO data are included. The positive ensemble impacts highlight the fact that the RO data with the nonlocal operator may give steady improvement for operational forecasting of tropical cyclogenesis. [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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      – Type: doi
        Value: 10.1175/WAF-D-25-0010.1
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 2255
    Subjects:
      – SubjectFull: Cyclone forecasting
        Type: general
      – SubjectFull: Data assimilation
        Type: general
      – SubjectFull: Global Positioning System
        Type: general
      – SubjectFull: Atmospheric boundary layer
        Type: general
      – SubjectFull: Tropical storms
        Type: general
      – SubjectFull: Water vapor
        Type: general
      – SubjectFull: Forecasting methodology
        Type: general
      – SubjectFull: North Pacific Ocean
        Type: general
    Titles:
      – TitleFull: Tropical Cyclogenesis in the Western North Pacific Improved by GNSS RO Data Assimilation Using Hybrid 3DEnVar with a Nonlocal Excess Phase Operator.
        Type: main
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          Name:
            NameFull: Pham Xuan, Quan
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            NameFull: Chen, Shu-Ya
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            NameFull: Huang, Ching-Yuang
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            NameFull: Kuo, Ying-Hwa
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          Dates:
            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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              Value: 40
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