the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Feb 2026
Scale-selective nudging with a diffusion-based filter in the variable-resolution Model for Prediction Across Scales version 8.2.2
Abstract. Nudged “specified-dynamics” configurations are widely used to align atmospheric models with reanalysis, but their behaviour in unstructured variable-resolution (VR) global models remains poorly understood. Here we implement a diffusion-based spectral nudging scheme in the Model for Prediction Across Scales–Atmosphere (MPAS-A) on a global VR mesh refined over East Asia and evaluate its performance under two convection schemes (Grell–Fritsch and Tiedtke) and a range of filter scales and nudged variables. Full analysis nudging imposes the strongest large-scale constraint and largely erases the scheme-dependent differences, whereas weaker, scale-selective spectral nudging still controls the large scales but allows GF and TK to exhibit distinct behaviours in precipitation frequency and rainband evolution. Kinetic-energy spectra, transient-eddy coherence, and temporal amplitude spectra jointly confirm that the diffusion-based filters act in a clearly scale-selective manner. Overall, our findings suggest that carefully tuned spectral nudging offers an effective trade-off: it keeps the large-scale flow phase-locked to the analysis while preserving enough variability to diagnose how different physics schemes shape the solution.
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Status: open (until 14 May 2026)
- RC1: 'Comment on egusphere-2026-176', Anonymous Referee #1, 10 Mar 2026 reply
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RC2: 'Comment on egusphere-2026-176', Anonymous Referee #2, 03 May 2026
reply
Review of “Scale-selective nudging with a diffusion-based filter in the variable-resolution Model for Prediction Across Scales version 8.2.2” by Chen and Tang.
This study implements a diffusion-based spectral nudging method within the MPAS model using a variable-resolution mesh. However, the primary objective of the manuscript is not clearly articulated, and the overall structure requires improvement. In addition, several findings are presented without sufficient explanation or physical interpretation. I recommend a major revision before the manuscript can be considered for publication.
Specific comments
- L21: This sentence appears incomplete. Do the authors intend to state that nudging is a data assimilation technique that constrains model simulations using observations while keeping them close to a predefined reference state? Please clarify.
- Section 1: The goals and research questions of the manuscript are not clearly stated. What does this work contribute beyond previous studies? This should be explicitly stated.
- I suggest dividing Section 2 into 2 sections: 2. methodology and 3. model configuration and experimental design
- This section needs to be reorganized to clearly highlight the authors’ contributions to nudging method development. At present, it is unclear whether the manuscript aims to develop/improve a nudging method or simply apply an existing one. Additionally, the equations require careful verification. For example, what does n represent in Eq. (2)? Please also double-check Eq. (4) for correctness.
- Table 1: The two experiment suites differ not only in convective parameterizations, but also in microphysics and boundary layer/surface layer schemes. This should be clearly stated and discussed.
- L200-202: These technical details would be more appropriate in the methodology section rather than here.
- L206: What is the purpose of using two filters and the selected cutoff scales? How were these cutoff scales determined? The experimental design lacks sufficient justification, making it difficult for the reader to follow.
- L219: It seems that GPCP rather than GPCP1DD is used in later text.
- Figure 1: Does this figure show wind bias between the nudged simulation and ERA-Interim? If so, ERA-Interim is not an independent dataset, as it is used to constrain the MPAS forecasts. The authors need to clarify this.
- The purpose of Figure 2 and several subsequent figures is not clearly explained. Currently, figures appear to be presented without sufficient context or interpretation. The authors should clearly state the objective of each figure and how it supports the study’s conclusions.
- L271: What is the rationale for selecting these variables? Please provide justification
- L275-287: Please refer explicitly to figure panel indices when describing results. For example, which panels support the statement that “GF and TK converge to nearly identical patterns and statistical scores”?
- Differences shown in several figures (e.g., between GF and TK suites in Figure 5 and others) are not adequately explained. A complete manuscript should provide physical interpretations and, where possible, hypotheses to explain these differences.
Citation: https://doi.org/10.5194/egusphere-2026-176-RC2
Model code and software
MPAS-A spectralnug_v1.0 Yiyuan Cheng https://doi.org/10.5281/zenodo.18229522
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- 1
Review of “Scale-selective nudging with a diffusion-based filter in the variable-resolution Model for Prediction Across Scales version 8.2.2” by Cheng and Tang.
This study implements the diffusion-based filter proposed by Grooms et al. (2021) to conduct scale-selective nudging in the MPAS-A model. The results show that the diffusion-based filter has achieved the scale-selective capability, allowing the model simulation to keep the large-scale flow phase-locked to the analysis while preserving enough variability to develop. Overall, the results are clear. However, some of the contents need to be revised. Therefore, I would recommend the manuscript to be accepted for publication after major revision.
Comments:
In the introduction, I would suggest that the author use a few words explicitly introducing the goal of this study. The author should state it more clearly so the reader can follow more easily.
I think most of the content in section 2.2 is from Grooms et al. (2021), however, it is not well-organized. Please revise it carefully.
L105: I cannot find the Laplacian operator △ you have mentioned.
L126-127:” the diffusion operator is designed in the form of △”
I cannot find the diffusion operator you have mentioned.
L162: In the experiment design, in addition to the difference in convective parameterization, the PBL scheme and microphysic schemes are also different in your experiment. Is there any reason not to use the same PBL and microphysic scheme?
Can the author show some results to demonstrate that the convective parameterization plays a major role in contributing to the difference between the experiments?
L168: “altering the partitioning between parameterized and large-scale precipitation.”
I’m curious about what the large-scale precipitation here refers to. The model is run with 92–25 km grid-spacing, so I assume all the convection is “parameterized”, is that correct?
In Table 2, the nstep values for gaussian1000km-uv_GF and gaussian1000km-uv_TK differ; is this the correct number?
L225: I did not see any verification with IMERG product.
L255: What is the observed standard deviation mean?
Results show that nudging with the analysis largely erases the scheme-dependent differences. I wonder if the nudging coefficient used here is appropriate or not? Have you tried different settings?
L263-264: It is interesting that even with analysis nudging, the nRMSE in VORT500 remains large. Have you check the nRMSE in U500 and V500? Are they also not corrected by nudging?
L265-268: In GF, introducing potential temperature produces the worst precipitation nRMSE, while adding moisture nudging yields the best performance in precipitation.
Is it the same in TK?
L323: How to define “Nudging Efficiency”?
How do you compute kinetic energy spectra in this study? If you use 2D-FFT, do you need to interpolate simulation results to a uniform-distance grid? In such a case, what is your grid-spacing? Will it affect the result at a higher wave number?
In Fig.6, the taper1000-UV_GF and taper500-UV_GF have a filter scale of 1000 and 500km, respectively. But they separate at a wavelength of 400 km. What’s the relationship between filter scale and wavelength defined here?
L345: Can you explain how the spectral coherence is computed?
In Fig.7c and 7d, why do all experiments have a minimum at a wavelength of around 120 km?