Assimilation of Spaceborne Radar Observations in the Goddard Earth Observing System (GEOS) Model

Moradi, Isaac; Todling, Ricardo; Zhu, Yanqiu; Ruston, Benjamin; Johnson, Benjamin; Tremolet, Yannick; Ardag, Dorukhan; Gu, Wei; Pawson, Steven

doi:10.5194/egusphere-2026-4086

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https://doi.org/10.5194/egusphere-2026-4086

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30 Jul 2026

| 30 Jul 2026

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Assimilation of Spaceborne Radar Observations in the Goddard Earth Observing System (GEOS) Model

Isaac Moradi, Ricardo Todling, Yanqiu Zhu, Benjamin Ruston, Benjamin Johnson, Yannick Tremolet, Dorukhan Ardag, Wei Gu, and Steven Pawson

Abstract. Spaceborne radar observations provide valuable information on the vertical structure of precipitation and hydrometeors, making them an important resource for numerical weather prediction and model evaluation. The Joint Effort for Data assimilation Integration (JEDI) is the unified and configurable data assimilation system developed and stewarded by the Joint Center for Satellite Data Assimilation (JCSDA) on behalf of its U.S. federal partners being adopted in NASA’s Goddard Earth Observing System (GEOS) suite of products. This study describes a new spaceborne radar data assimilation capability developed for use in JEDI, including both the radar forward operator and the associated data assimilation components. This new capability is then demonstrated using observations from the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR). The radar forward operator simulates reflectivities that agree well with observations, with mean differences typically below 2 dB during the August 2023–January 2024 test period. Consequently, a large number of DPR observations can be successfully assimilated into GEOS. Case studies of tropical cyclones show that DPR assimilation can substantially improve the analyzed distribution of precipitation and hydrometeors and, in some cases, improve atmospheric state variables such as wind, pressure, temperature, and water vapor. These results demonstrate the potential of spaceborne radar observations to improve the representation of precipitating systems in GEOS and, ultimately, the quality of future reanalyses and weather forecasts.

Received: 15 Jul 2026 – Discussion started: 30 Jul 2026

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Isaac Moradi, Ricardo Todling, Yanqiu Zhu, Benjamin Ruston, Benjamin Johnson, Yannick Tremolet, Dorukhan Ardag, Wei Gu, and Steven Pawson

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RC1:
'Comment on egusphere-2026-4086', Anonymous Referee #1, 24 Oct 2026 reply
This study implements a spaceborne radar data assimilation (DA) capability within the Joint Effort for Data assimilation Integration (JEDI). Using observations from the Dual-frequency Precipitation Radar (DPR) aboard Global Precipitation Measurement (GPM), the authors conduct numerical experiments to investigate the impact of assimilating spaceborne radar observations on analyses and forecasts in the Goddard Earth Observing System (GEOS) model.

The topic is important and the results are interesting. However, I have several concerns regarding the manuscript structure, experimental design, description of the DA methodology, and presentation of the results. In particular, the manuscript should better emphasize the modifications introduced to enable radar DA, provide a more detailed description of the assimilation methodology, and present a more systematic and quantitative assessment of the impacts on analyses and forecasts. Therefore, I recommend major revision.

Major comments:

1. Description of the JEDI system and GEOS-JEDI framework
The general descriptions of JEDI and GEOS-JEDI systems are unnecessarily lengthy. These sections should be streamlined to emphasize the modifications introduced to enable spaceborne radar DA, rather than providing extensive descriptions of existing system components that are not specific to this study. In particular,
Section 3.3 IODA and Section 3.4 SWELL: neither section currently provides details specific to the radar DA implementation presented in this study. I suggest briefly introducing the general functionality of each component and then focusing on the modifications required to support radar DA. The key contribution of this work is how these components are adapted to accommodate radar observations, rather than the general functionality of IODA and SWELL themselves.

Section 4 (Lines 190–232): The description of the GEOS-JEDI framework is too lengthy and general. The authors should streamline this section and cite existing publications for details of this establish framework. For example, the Incremental Analysis Update procedure is already implemented in GEOS-JEDI and does not appear to have been modified for this study. The manuscript should focus more on the DA methodology and the specific changes required for radar assimilation, rather than on general aspects of the DA workflow.

2. Experimental configuration and assimilation methodology
(1) What is the actual period covered by the continuously cycling experiments? The manuscript states that “The experiments are carried out continuously for the period spanning August 2023 through January 2024” (Lines 234–235), whereas the captions of Figures 3–5 refer to the period of “15 August to 15 September 2023”.
(2) What is the cycling frequency of this DA experiments? 6 hourly?
(3) Assimilation methodology: Given that this is a DA study, the assimilation methodology should be described more clearly and given greater prominence.
Please specify the control variables used in the DA system. Are hydrometeor variables included as control variables? If so, which hydrometeor variables are included, and does the “GSI background error covariance (GSIBEC)” used in this study account for covariances between hydrometeors and other model state variables? If these covariances are included, how are they estimated or specified?

Hydrometeors are required input for the radar observation operator, but they are diagnostic variables in GEOS. The authors should explain how these hydrometeor variables are diagnosed, with appropriate references when available. In the radar DA GEOS-JEDI framework, are the hydrometeor fields diagnosed within JEDI, or are they generated outside JEDI and supplied to the assimilation system as background fields? Clarifying this point is important for understanding how radar observations influence the model state.

Figure 5 shows that over the one-month period (15 August to 15 September), the number of the GPM DPR channel 1 observations is only 1 for most regions. To better contextualize the potential impact of DPR assimilation, could the authors report the mean, minimum, and maximum numbers of DPR observations assimilated per DA cycle? It would also be helpful to compare these numbers with the corresponding observation counts for all other routinely assimilated observation types. This comparison would help readers assess the relative contribution of DPR observations within the existing DA system.

Similarly, it would be helpful to mark the DPR observations on top of both cyclones in Figure 8.
DPR datasets are thinned both horizontally (Lines 102–103) and vertically (Lines 155–157). But based on the text, horizontal thinning is performed outside JEDI, whereas vertical thinning is performed through JEDI’s UFO. Why are these procedures implemented separately? Could both thinning procedures be performed within UFO? Please clarify the rationale for the current implementation.

3. Observation error specification
σ_bin is the representativeness error and assigned as the standard deviation of observations within a horizontal box. However, the DPR observations are thinned both horizontally (5 km to 25 km) and vertically (125 m to model layer). Why is the representativeness error estimated only from horizontal variability? The authors should explain this choice and discuss whether neglecting vertical variability could affect the estimated observation errors.

It is stated, “In this study, we ultimately adopt the follow error model for GPM DPR ……” (Lines 263–268). Please explain how the coefficients in this error model are determined. Are they based on previous studies, theoretical considerations, empirical estimates, or sensitivity experiments? If the coefficients were selected empirically, some justification or supporting analysis would help establish that the adopted coefficient values are appropriate for this application.

4. Impacts of radar observations on analyses and forecasts
The current evaluation relies heavily on two case studies and qualitative descriptions. A more systematic and quantitative evaluation is needed to demonstrate the benefits of assimilating DPR observations. The authors should present aggregated verification statistics comparing the CTRL and DPR experiments over the full evaluation period, “August 2023 through January 2024” (Lines 234–235), both for analyses and forecasts. Suitable metrics can be bias, RMSE, equitable threat score (ETS) for precipitation, and tropical cyclone verification measures, where applicable.

The abstract states, “Spaceborne radar observations provide valuable information on the vertical structure of precipitation and hydrometeors, making them an important resource for numerical weather prediction and model evaluation”. Given this motivation, could the authors evaluate forecast reflectivity against DPR observations? Such an evaluation could help determine whether assimilation DPR observations improves the model’s representation of precipitation structure, using true observations instead of reanalysis.

Figures 4 and 6: Please revise the color scales for the mean observation bias in Figure 4 and the mean Obs-HofX differences in Figure 6. A diverging color scale centered at zero would improve readability.

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Citation: https://doi.org/10.5194/egusphere-2026-4086-RC1

Isaac Moradi, Ricardo Todling, Yanqiu Zhu, Benjamin Ruston, Benjamin Johnson, Yannick Tremolet, Dorukhan Ardag, Wei Gu, and Steven Pawson

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Short summary

This study presents a new capability for assimilating spaceborne radar observations into NASA's Goddard Earth Observing System (GEOS) using the JEDI data assimilation framework. The implementation includes a radar forward operator and associated assimilation components, demonstrated with GPM Dual-frequency Precipitation Radar (DPR) observations. Results show improved analyses of precipitation, hydrometeors, and atmospheric state variables, supporting enhanced weather forecasts and reanalyses.


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