Park et al. (2026) Development and testing of ensemble-variational data assimilation capabilities for radar data within JEDI coupled with FV3-LAM model

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-09-07
• Authors: Jun Park, Chengsi Liu, Ming Xue
• DOI: 10.5194/gmd-19-8213-2026

Research Groups

• Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma, 73072, USA
• Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma, 73072, USA
• School of Meteorology, University of Oklahoma, Norman, Oklahoma, 73072, USA

Short Summary

This study presents the development and testing of ensemble-variational data assimilation capabilities for radar data within the Joint Effort for Data assimilation Integration (JEDI) framework coupled with the FV3-LAM model. The refined reflectivity observation operator improves consistency with the Thompson microphysics scheme, leading to better analyses and short-range forecasts.

Objective

• To develop and test ensemble-variational data assimilation capabilities for radar data within JEDI coupled with the FV3-LAM model.
• To refine the reflectivity observation operator to improve its consistency with the Thompson microphysics scheme.

Study Configuration

• Spatial Scale: Regional scale, covering the midwestern United States.
• Temporal Scale: Hourly assimilation cycles between 19:00 UTC 12 May and 00:00 UTC 13 May 2022.

Methodology and Data

• Models used: FV3-LAM model with the RRFS_v1beta physics suite, JEDI En3DVar system.
• Data sources: Radar reflectivity and radial velocity observations from MRMS and WSR-88D radars.

Main Results

• The refined reflectivity observation operator improves consistency with the Thompson microphysics scheme, leading to better analyses and short-range forecasts.
• JEDI-New consistently outperforms JEDI-Org in terms of root-mean-square innovation (RMSI) for both reflectivity and radial velocity during DA cycles.
• The analysis fields from GSI-New and JEDI-New produce well-organized convective structures that closely resemble the MRMS observation.

Contributions

• This study provides a refined reflectivity observation operator that improves consistency with the Thompson microphysics scheme, leading to better analyses and short-range forecasts.
• The development of ensemble-variational data assimilation capabilities for radar data within JEDI coupled with the FV3-LAM model contributes to the improvement of numerical weather prediction.

Funding

• This research was supported by the National Science Foundation (NSF) under grant number [insert grant number].

Citation

@article{Park2026Development,
  author = {Park, Jun and Liu, Chengsi and Xue, Ming},
  title = {Development and testing of ensemble-variational data assimilation capabilities for radar data within JEDI coupled with FV3-LAM model},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-8213-2026},
  url = {https://doi.org/10.5194/gmd-19-8213-2026}
}