Enhancing Regional Ensemble‐Variational Data Assimilation for Convection‐Permitting Prediction With Combination of the Dual‐Localization and Scales Blending of Global and Regional Ensembles.
Saved in:
| Title: | Enhancing Regional Ensemble‐Variational Data Assimilation for Convection‐Permitting Prediction With Combination of the Dual‐Localization and Scales Blending of Global and Regional Ensembles. |
|---|---|
| Authors: | Wang, Yuanbing1 (AUTHOR), Qian, Xinyao1,2 (AUTHOR), Chen, Yaodeng1 (AUTHOR) keyu@nuist.edu.cn, Li, Xin3 (AUTHOR), Min, Jinzhong1 (AUTHOR), Deng, Yaxin1 (AUTHOR) |
| Source: | Journal of Advances in Modeling Earth Systems. May2026, Vol. 18 Issue 5, p1-30. 30p. |
| Subject Terms: | *Weather forecasting, Data assimilation, Precipitation forecasting, Statistical ensembles |
| Abstract: | This study proposes a new scheme combining ensemble blending and dual‐localization of ensemble error covariance in an ensemble‐variational (EnVar) framework to better account for both the multi‐scale characteristics of the background and background error covariance for high‐resolution data assimilation and forecasting. The ensemble blending introduces large‐scale analysis information from a 30‐member global model's ensemble into the regional model's background and ensemble‐based background error covariance using a low‐pass Raymond tangent implicit filter. The dual‐localization in EnVar also employs two different localization radii at meso‐α and meso‐β scales respectively to determine multiscale analysis increments through the scale‐blending background error covariance. We evaluate the impact of the new scheme on the assimilation of multi‐source observations including all‐sky Fengyun‐4A (FY‐4A) Advanced Geostationary Radiation Imager infrared radiances and on forecasting two convective events in the Yangtze‐Huaihe region influenced by different weather systems using a 4‐km model. Results show that while ensemble blending or dual‐localization in the EnVar framework alone provides improvements although the former contribution more than the latter, the proposed scheme combining them yields the best performance in the simulation of the ambient environment, moisture transport, and thermodynamic vertical structure. Notably, the proposed scheme enhances weak and low‐level updrafts and provides more accurate convection regions, improving precipitation forecasted patterns and fraction skill scores. In addition, the proposed scheme demonstrates greater efficacy in convections driven by multiple interacting weather systems across scales than a single local weather system. Plain Language Summary: Effectively incorporating observations across different scales is important to properly resolve the wide range of wavelengths in high‐resolution limited area models. This study proposes a new scheme that introduces multi‐scale information in an ensemble‐variational (EnVar) framework with combining scales' blending and multiscale localization of observation influence together to improve convection‐permitting prediction. The scales' blending incorporates large‐scale ensemble information from a global model into a regional model to reduce the large‐scale bias of the forecast and the ensemble‐based forecast error. The multiscale localization using two different influence radii to better account for mesoscale analysis corrections. To verify effectiveness of this method, we apply it to the assimilation of multi‐source observations including all‐sky Fengyun‐4A satellite infrared radiances for the prediction of two heavy rainfall events with different scale features. Results show that the proposed multiscale EnVar data assimilation scheme improves the simulation of large‐scale environmental conditions and meso‐scale thermodynamic vertical structure. Additionally, this new scheme enhances precipitation forecasting skills, especially for multiple interacting weather systems. Key Points: A dual‐localization of the ensemble error covariance was introduced into the convective‐scale ensemble‐variational data assimilation frameworkThe scales blending of global and regional ensembles was combined with the dual‐localization ensemble‐variational data assimilationThe proposed scheme improved convection‐permitting prediction of middle‐to‐low level conditions with assimilation of Advanced Geostationary Radiation Imager observations [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Advances in Modeling Earth Systems 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | This study proposes a new scheme combining ensemble blending and dual‐localization of ensemble error covariance in an ensemble‐variational (EnVar) framework to better account for both the multi‐scale characteristics of the background and background error covariance for high‐resolution data assimilation and forecasting. The ensemble blending introduces large‐scale analysis information from a 30‐member global model's ensemble into the regional model's background and ensemble‐based background error covariance using a low‐pass Raymond tangent implicit filter. The dual‐localization in EnVar also employs two different localization radii at meso‐α and meso‐β scales respectively to determine multiscale analysis increments through the scale‐blending background error covariance. We evaluate the impact of the new scheme on the assimilation of multi‐source observations including all‐sky Fengyun‐4A (FY‐4A) Advanced Geostationary Radiation Imager infrared radiances and on forecasting two convective events in the Yangtze‐Huaihe region influenced by different weather systems using a 4‐km model. Results show that while ensemble blending or dual‐localization in the EnVar framework alone provides improvements although the former contribution more than the latter, the proposed scheme combining them yields the best performance in the simulation of the ambient environment, moisture transport, and thermodynamic vertical structure. Notably, the proposed scheme enhances weak and low‐level updrafts and provides more accurate convection regions, improving precipitation forecasted patterns and fraction skill scores. In addition, the proposed scheme demonstrates greater efficacy in convections driven by multiple interacting weather systems across scales than a single local weather system. Plain Language Summary: Effectively incorporating observations across different scales is important to properly resolve the wide range of wavelengths in high‐resolution limited area models. This study proposes a new scheme that introduces multi‐scale information in an ensemble‐variational (EnVar) framework with combining scales' blending and multiscale localization of observation influence together to improve convection‐permitting prediction. The scales' blending incorporates large‐scale ensemble information from a global model into a regional model to reduce the large‐scale bias of the forecast and the ensemble‐based forecast error. The multiscale localization using two different influence radii to better account for mesoscale analysis corrections. To verify effectiveness of this method, we apply it to the assimilation of multi‐source observations including all‐sky Fengyun‐4A satellite infrared radiances for the prediction of two heavy rainfall events with different scale features. Results show that the proposed multiscale EnVar data assimilation scheme improves the simulation of large‐scale environmental conditions and meso‐scale thermodynamic vertical structure. Additionally, this new scheme enhances precipitation forecasting skills, especially for multiple interacting weather systems. Key Points: A dual‐localization of the ensemble error covariance was introduced into the convective‐scale ensemble‐variational data assimilation frameworkThe scales blending of global and regional ensembles was combined with the dual‐localization ensemble‐variational data assimilationThe proposed scheme improved convection‐permitting prediction of middle‐to‐low level conditions with assimilation of Advanced Geostationary Radiation Imager observations [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 19422466 |
| DOI: | 10.1029/2025MS004946 |
