the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Nov 2025
Development and Testing of Ensemble-Variational Data Assimilation Capabilities for Radar Data within JEDI coupled with FV3-LAM Model
Abstract. This study presents the first implementation and evaluation of radar reflectivity data assimilation capabilities within the ensemble three-dimensional variational (En3DVar) data assimilation (DA) system of the Joint Effort for Data assimilation Integration (JEDI) framework. Building on our earlier works that assimilated reflectivity in JEDI LETKF and in GSI En3DVar, this study focuses on the JEDI En3DVar algorithm when coupled with the FV3-LAM model using the Thompson microphysics scheme. The radar reflectivity observation operator is refined by modifying the snow and graupel reflectivity formulations to improve consistency with Thompson microphysics. The new operator notably improves reflectivity analyses at the upper levels and reduces root-mean-square innovations for both reflectivity and radial velocity during the DA cycles. A high-impact convective storm event is used to evaluate the new implementation. DA experiments are conducted using both the JEDI and GSI En3DVar systems, employing identical observation operators and similar configurations. The resulting analyses and short-range forecasts from the two systems are comparable, supporting the validity of the new implementation of JEDI En3DVar for reflectivity and radial velocity assimilation. Additional comparisons with real-time High-Resolution Rapid Refresh (HRRR) and experimental Rapid Refresh Forecast System (RRFS) forecasts are made. The JEDI-based experiment captures the storm structure and placement with accuracy similar to or better than the HRRR and RRFS forecasts. Improvements are especially evident in the depiction of convective cores and stratiform rainbands, where reflectivity intensity and coverage are better aligned with radar observations.
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Status: open (until 19 Jan 2026)
- RC1: 'Comment on egusphere-2025-5411', Anonymous Referee #1, 23 Dec 2025 reply
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CEC1: 'Comment on egusphere-2025-5411 - No compliance with the policy of the journal', Juan Antonio Añel, 23 Dec 2025
reply
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
You have archived your code and data in several sites that do not comply with our requirements, such as the Open Science Framework, NOAA servers, and GitHub. In the case of GitHub, it is not a suitable repository for scientific publication. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. The other servers do not fulfil GMD’s requirements for a persistent data archive because:
* It does not appear to have a published policy for data preservation over many years or decades (some flexibility exists over the precise length of preservation, but the policy must exist).
* It does not appear to have a published mechanism for preventing authors from unilaterally removing material. Archives must have a policy which makes removal of materials only possible in exceptional circumstances and subject to an independent curatorial decision,
* It does not appear to issue a persistent identifier such as a DOI or Handle for each precise dataset.If we have missed a published policy which does in fact address this matter satisfactorily, please post a response linking to it. If you have any questions about this issue, please post them in a reply.
Please, therefore, publish your code and data in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible. We cannot have manuscripts under discussion that do not comply with our policy.
The 'Code and Data Availability’ section must also be modified to cite the new repository locations, and corresponding references added to the bibliography.
I must note that if you do not fix this problem, we cannot continue with the peer-review process or accept your manuscript for publication in GMD.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2025-5411-CEC1 -
RC2: 'Comment on egusphere-2025-5411', Anonymous Referee #2, 26 Dec 2025
reply
Recommendation: Major revisions
--- Overview
This study develops a new radar observation operator within a JEDI ensemble-variational data assimilation framework. The new operator is consistent with the forecast microphysics scheme and demonstrates notable advantages in a high-impact squall line event.
The authors provide an excellent evaluation of the new radar operator, including detailed comparisons with the still-operational GSI system, as well as operational forecasts from HRRR and the experimental RRFS. I appreciated the discussion of how radar increments project onto different hydrometeor species, along with the combination of subjective and objective verification statistics. The results from the new radar operator in JEDI are promising and represent an important step toward broader adoption of JEDI workflows.
I only have two major comments, which are summarized below, and followed by minor suggestions.
--- Major comments
1. The introduction text up to line 75 should be considerably shortened, as it does not directly relate to the research topic of this paper. Instead, the authors should focus the narrative on the subsequent text which pertains to the type of radar DA issues addressed in this manuscript.
2. The results in Fig. 4 are promising. However, unlike radial velocity, the reflectivity-based innovation statistics in Fig. 4 do not show signs of convergence toward stable values, indicating that six cycles may not be sufficient. In the DA literature, fair comparisons traditionally discard these spin-up periods during which DA diagnostics exhibit mean trends. Since the paper focuses on a single case, I believe extending the experiment to additional cycles is worth pursuing and would provide a more convincing demonstration of the benefits of the new radar operator developments in JEDI.
--- Minor comments
Please check whether acronyms for ensemble-variational methods are used consistently throughout the paper. For example, lines 50–51 state that Wang and Wang (2017) developed an En3DVar system; the correct acronym should be 3DEnVar.
Line 114: This refers to the radar reflectivity factor, not simply reflectivity.
Fig. 1 and surrounding discussion: The revised text should clarify how the WRF reflectivity values are calculated.
Section 3.2.2: It would be helpful to write out the full DA equations to make this paper self-contained.
Line 235: The NODA experiment does not assimilate radar data. What about other conventional observations? Please clarify in the revised manuscript.
Lines 256–258: Can you briefly comment on what causes the positive biases?
When referring to states in spatial maps, it would be helpful to label them directly on the maps. It would also be helpful to include title labels for each plot; for example, experiment names in Fig. 12.
Fig. 9: In addition to underestimating surface temperatures, it should be noted that the DA experiments produce a stronger outflow. This behavior is consistent with cold pool dynamics (stronger cold pools correspond to stronger winds).
Line 463: “The NODA experiment … with weak, spatially inconsistent probabilities” — The NODA probabilities appear to be much higher than those of any other experiment; please reword this statement.
Citation: https://doi.org/10.5194/egusphere-2025-5411-RC2
Data sets
Processed radar observations and YAML configuration files for JEDI EnVar experiments Jun Park, Chengsi Liu, and Ming Xue https://osf.io/2w4su/overview
Model code and software
JEDI-FV3 bundle and UFS SRW App CSDA and UFS Community Developers https://github.com/JCSDA
JEDI-FV3 bundle and UFS SRW App CSDA and UFS Community Developers https://github.com/ufs-community/ufs-srweather-app
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All comments are found in the attached supplement.