the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Nov 2025
Improvement of the Computational Efficiency in SVD-3DEnVar Data Assimilation Scheme and Its Preliminary Application to the TRAMS 3.0 Model
Abstract. Although the Singular Value Decomposition-three Dimensional Ensemble Variational (SVD-3DEnVar) data assimilation scheme has achieved successful application in real case simulations with comprehensive numerical weather prediction models, its computational efficiency still cannot meet the demands of actual operational numerical forecasting. The main limitations lie in the generation of three-dimensional perturbations and the implementation of parallel calculations. This paper constructed a three-dimensional perturbation field generation scheme that supports multi-process parallelism and can directly generate any specified number of grid points in both horizontal and vertical directions. At the same time, an efficient parallel implementation scheme has been developed according to the characteristics of local patch assimilation in the SVD-3DEnVar scheme. The Observing System Simulation Experiment (OSSE) test results based on the Tropical Regional Atmospheric Model System (TRAMS) show that after computational efficiency optimization, the time required to generate a 3D perturbation field has been reduced from 22 minutes to 2.2 seconds, while the runtime of the assimilation process has decreased from 1,700 minutes under serial execution to less than 15 minutes (using 150 nodes in parallel). Finally, we conducted an assimilation experiment using actual observational data of sea surface wind fields to preliminarily validate the reasonableness of the assimilation results from the optimized SVD-3DEnVar scheme.
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RC1: 'Comment on egusphere-2025-4632', Anonymous Referee #1, 17 Nov 2025
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AC1: 'Reply on RC1', Kun Liu, 22 Nov 2025
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Response to Reviewer 1
We sincerely thank the reviewer for the insightful comments and constructive suggestions, which have significantly helped us improve the quality of our manuscript. We have carefully considered all points and have revised the manuscript accordingly. Below, we provide a point-by-point response to the comments.
Comment 1:
Regarding the choice of methodology, it is recommended that you explain why SVD-4DVar was not adopted in favor of SVD-3DVar, while briefly analyzing the core challenges of the latter.
Response:
We appreciate the reviewer’s suggestion. The decision to adopt SVD-3DEnVar instead of SVD-4DEnVar was primarily motivated by two factors:
Computational Efficiency and Operational Feasibility: The main objective of this study is to improve computational efficiency for operational typhoon forecasting, so the relatively simple 3DVar scheme is chosen for experimentation...
Observation Frequency: The sea surface wind observations used in our real-data experiments are available only every 6 hours. This low temporal resolution does not fully leverage the temporal continuity advantages of 4DEnVar.
We acknowledge that SVD-3DEnVar has its own challenges, which we now discuss in the revised manuscript (Section 5). The primary limitations include: The use of a single localization scale, which may not optimally handle multi-scale observations (e.g., surface, satellite, and radar); The limited ability of linear combinations of singular vectors to represent strongly nonlinear relationships, especially for unobserved variables. These challenges will guide our future work on multi-scale assimilation and machine-learning-enhanced methods.
Comment 2:
It should be noted that the singular value decomposition (SVD) of matrix A is extremely challenging in practice due to its large dimensions (Nx + Ny), posing significant difficulties in terms of both storage and computation. A discussion on this aspect is recommended.
Response:
We fully agree with the reviewer. To address the computational and storage challenges of performing SVD on the large matrix A, we implemented a local patch assimilation strategy. This approach significantly reduces the effective dimensions of A by horizontal and vertical localization. Only observations within a specified horizontal (lh) and vertical (lv) radius from the central grid point are included. As a result, both Nx (model variables in the local patch) and Ny (observations within the local patch) are drastically reduced. This makes the SVD computationally feasible without sacrificing the flow-dependent covariance information. We have added a clarification in Section 3.1 to explain this strategy.
Comment 3:
While equation (11) provides the Gaussian weight function, the localization scheme used in SVD-3DVar should be presented in more detail to enhance the completeness of the paper.
Response:
Thank you for this suggestion. We have expanded the description of the localization scheme in Section 3.1. The revised text now reads:
“The Gaussian weight function defined in Equation (11) is applied to each observation within the local patch. The horizontal and vertical localization scales (σh and σv) control the rate at which observation influence decays with distance. This localization ensures that only observations within a specified radius significantly impact the analysis increment at the center of the local patch, thereby mitigating spurious long-range correlations and improving the stability and accuracy of the assimilation.”
Comment 4:
In recent years, 4DEnVar methods have advanced rapidly. To reflect an up-to-date understanding of the field, it is advisable to include references to relevant studies published between 2022 and 2025.
Response:
We thank the reviewer for this suggestion. We have now incorporated several recent references on 4DEnVar advancements in the Introduction and Conclusion sections, including:
Inverarity et al. (2023) on hybrid En-4DEnVar in the Met Office system;
Berre and Arbogast (2024) on hybrid covariances at Météo-France;
Lu and Wang (2024) on scale-dependent localization in hurricane forecasting;
Thiruvengadam and Wang (2025) on convective-scale 4DEnVar;
Wang et al. (2025) on CubeSat radiance assimilation.
These additions help contextualize our work within the evolving landscape of ensemble-variational methods.
Comment 5:
Regarding the generation of initial samples, several classical works (e.g. those by Evensen) have achieved high memory efficiency. It would be beneficial to reference these works and discuss their relevance to the present method. From a practical perspective, the main computational burden in parallelization typically lies in the ensemble forecast component, which should also be addressed.
Response:
We agree with the reviewer. The initial perturbation generation in SVD-3DEnVar is based on the Gaussian random field method introduced by Evensen (1994), which is known for its memory efficiency and statistical robustness. However, the original implementation was limited to 2D square grids with odd numbers of points, which motivated our multi-dimensional and parallel optimizations. We have mentioned Evensen’s foundational work and clarify how our optimizations build upon it.
Regarding the computational burden of ensemble forecasts: yes, the ensemble integration is indeed the most computationally intensive part in parallel implementations. However, since ensemble members are independent, they can be run concurrently if sufficient computational resources are available. While this study focuses on optimizing the perturbation generation and assimilation steps, we acknowledge the resource demands of ensemble forecasting and will address this in future work.
Comment 6:
As this is an ensemble-based method, it is recommended that the ensemble sample update strategy in SVD-3DVar is explained briefly to improve the completeness of the methodological description.
Response:
We have added the following explanation to Section 1:
“Unlike traditional ensemble assimilation schemes (such as EnKF), which directly update each ensemble member during cyclic assimilation, SVD-3DEnVar only assimilates and updates the control forecast. Therefore, after each assimilation cycle, the updated analysis field must be perturbed again to generate the initial conditions for the next cycle's ensemble forecast. This approach has the advantage of avoiding filter divergence and, in the presence of model errors, performs better than EnKF (Qiu et al., 2007).”
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AC1: 'Reply on RC1', Kun Liu, 22 Nov 2025
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CC1: 'Comment on egusphere-2025-4632', Nima Zafarmomen, 03 Dec 2025
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The paper targets computational bottlenecks in the SVD-3DEnVar data assimilation scheme and proposes two main engineering improvements: (i) a redesigned perturbation-field generation algorithm that directly produces 3D, grid-conforming perturbations with multi-process parallelism and without intermediate I/O, and (ii) a parallel local-patch assimilation framework that partitions work, balances load, and reduces memory duplication via node-level pointer sharing. Using the TRAMS 3.0 model, the authors report large wall-clock reductions: 3D perturbation generation from ~22 minutes to 2.2 seconds and end-to-end assimilation from ~1700 minutes (serial) to <15 minutes with 150 nodes, alongside preliminary OSSE and real-data tests that show reasonable analysis increments and some improvement in typhoon forecasts.
Overall, the paper tackles a very practical problem: making SVD-3DEnVar fast and memory-efficient enough for realistic, large-domain applications. I. recommed it for publication after considering these comments:
- How do you prescribe and verify the target covariance statistics of the perturbation fields (variance, horizontal and vertical correlation lengths, cross-variable correlations)?
- Are perturbations generated multivariately (i.e., with controlled cross-variable balance), or independently per variable with post hoc smoothing?
- What are the precise observation operator and error model used for the 10 m sea-surface winds (e.g., stability-dependent surface-layer mapping, bias correction, representativeness error)? How are coastal/land points handled, and are rainy scenes screened?
- Please clarify the SVD/variational notation: define Λp explicitly, correct the SVD equation symbols, and detail the truncation criterion for K (energy, cross-validated error, or fixed)?
- I storngly recommend to expand your introduction and cite below paper
Assimilation of sentinel‐based leaf area index for modeling surface‐ground water interactions in irrigation districts
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Abstract:
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Consider explicitly stating that the main novelty is the computational optimization and parallelization that makes SVD-3DEnVar suitable for operational use, plus a first real-data test with satellite-derived sea surface winds.
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Notation consistency:
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Ensure all symbols in the equations are defined once and consistently (e.g., Λ vs Λ_P in Equation (10); clarify if Λ_P is the same eigenvalue matrix as in Eq. (4) or modified).
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Make sure the dimension of vectors and matrices in Eqs. (1)–(10) is always clear (model vs observation subspaces).
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Equation (11) Gaussian localization:
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Check the condition in the piecewise definition:
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You currently use (r_h ≤ l_h and r_v ≤ l_v) vs (r_h > l_h or r_v ≥ l_v). It might be clearer and more symmetric to use strict/≤ consistently.
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Consider giving an example of actual physical localization scales (in km) corresponding to l_h, l_v, σ_h, σ_v.
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Citation: https://doi.org/10.5194/egusphere-2025-4632-CC1 -
AC2: 'Reply on CC1', Kun Liu, 14 Dec 2025
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Response to Reviewer 2 (Dr. Nima Zafarmomen)
We sincerely thank the reviewer for the thorough and constructive comments, which have significantly helped us improve the manuscript. Below, we provide point-by-point responses and describe the corresponding revisions made.
Comment 1:
How do you prescribe and verify the target covariance statistics of the perturbation fields (variance, horizontal and vertical correlation lengths, cross-variable correlations)?Response:
The perturbation fields are generated following Evensen (2003). First, a set of smooth two-dimensional random perturbation fields with zero mean and unit variance is generated. Then, a fixed weighted blending scheme is applied (40% from the perturbation information of adjacent filled layers, 60% from newly generated two-dimensional random fields) to produce three-dimensional perturbation fields with a certain vertical correlation scale. After superimposing different three-dimensional perturbations for each variable, the fields are integrated forward for a period of time to allow the perturbations between different variables to develop reasonable multivariate correlations (e.g., physically consistent relationships between temperature and pressure).The control of variance and horizontal correlation scale for the two-dimensional random perturbation fields is based on the following principles:
For a continuous two-dimensional field q = q(x,y), its Fourier transform can be written as:
q(x,y) = ∫∫_{-∞}^{∞} ̂q(k) e^{i k·x} dk (R1)where ̂q(k) are the Fourier coefficients, the wavenumber vector k is defined as k = (κ_l, γ_p), and κ_l and γ_p are the wavenumbers in the x and y directions, respectively.
Discretizing on an N × M horizontal grid:
q(x_n, y_m) = Σ_{l,p} ̂q(κ_l, γ_p) e^{i(κ_l x_n + γ_p y_m)} Δk (R2)where x_n = nΔx, y_m = mΔy, κ_l = (2π l) / (x_N) = (2π l) / (NΔx), γ_p = (2π p) / (y_M) = (2π p) / (MΔy), and Δk = Δκ Δγ = ( (2π)^2 ) / (N M Δx Δy).
Assuming the Fourier coefficients take the form:
̂q(κ_l, γ_p) = (c / √(Δk)) e^{- (κ_l^2 + γ_p^2) / σ^2} e^{2π i φ_{l,p}} (R3)where c is a normalization constant controlling the variance, σ is a bandwidth parameter determining the correlation length, and φ_{l,p} ∈ [0,1] is a uniformly distributed random number.
Substituting (R3) into (R2) yields the spatial field expression:
q(x_n, y_m) = Σ_{l,p} (c / √(Δk)) e^{- (κ_l^2 + γ_p^2) / σ^2} e^{2π i φ_{l,p}} e^{i(κ_l x_n + γ_p y_m)} Δk (R4)This can be interpreted as multiplying a Gaussian-shaped filter (spectral window) and random phase in wavenumber space, followed by an inverse Fourier transform to obtain the spatial random field. Therefore, the horizontal correlation length and variance of the two-dimensional random perturbation field can be controlled by adjusting the parameters σ and c.
Reference:
Evensen G. 2003. The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation, Ocean Dynamics 53, 343--367.Comment 2:
Are perturbations generated multivariately (i.e., with controlled cross-variable balance), or independently per variable with post hoc smoothing?Response:
Currently, perturbations for each variable are generated independently. By integrating the independently generated perturbations added to the model initial conditions forward for a period of time, cross-variable covariance relationships develop among the perturbations.Comment 3:
What are the precise observation operator and error model used for the 10 m sea-surface winds (e.g., stability-dependent surface-layer mapping, bias correction, representativeness error)? How are coastal/land points handled, and are rainy scenes screened?Response:
(a) Observation operator: The 10 m wind speed (u_10, v_10) is diagnosed from the wind speed (u_x, v_x) at the model's first layer height z_x, and then horizontally interpolated to observation locations. The diagnosis considers atmospheric stability through the dimensionless wind shear function Φ_x (the stability function in Monin--Obukhov similarity theory). The formulas are:
{ u_10 = u_x * (Φ_10 / Φ_x)
v_10 = v_x * (Φ_10 / Φ_x) } (R5)where Φ_10 and Φ_x are the dimensionless wind gradient functions at 10 m height and the model's first layer height z_x, respectively.
Under different stability conditions, Φ_x is expressed as:
Φ_x = {
-10 ln(z_x / z_0), (Ri_b > Ri_c = 0.2)
-5 * (Ri_b / (1.1 - 5 Ri_b)) * ln(z_x / z_0), (Ri_c ≥ Ri_b > 0)
0, (Ri_b = 0)
2 ln( (1+x) / x ) + 2 ln( (1+x^2) / x ) - 2 tan^{-1}(x) + π/2, (Ri_b < 0)
} (R6)where
x = (1 - 16z / L)^{1/4} (R7)Here, Ri_b is the bulk Richardson number, Ri_c is a threshold for strongly stable conditions, and z_0 is the surface roughness.
(b) Bias correction: The satellite-derived sea surface wind data have been bias-corrected prior to assimilation.
(c) Representativeness error: In the SVD-3DEnVar scheme, only the leading N singular vectors are retained when fitting observation increments, which effectively truncates short-wave information in observations. Therefore, the observation representativeness error has minimal impact on the assimilation results.
(d) Coastal/land treatment: The model uses static surface data to distinguish between ocean and land grid points. Over land, roughness length z_0 is specified based on land cover type. Over ocean, z_0 is diagnosed from sea surface wind speed using the Charnock (1955) relation:
z_0 = z_ch * (u_*^2 / g) + 0.00001 (R8)
where z_ch is the Charnock coefficient, u_* is the friction velocity, and g is gravitational acceleration. Differences in roughness calculation between land and sea surfaces further influence the diagnosis of 10 m winds through Eq. (R6).Reference:
Charnock, H. (1955), Wind stress on a water surface. Q.J.R. Meteorol. Soc., 81: 639-640. https://doi.org/10.1002/qj.49708135027(e) Rain screening: The satellite-retrieved sea surface wind product used in this study is a blended product that incorporates longer-wavelength microwave radiometer data with better cloud-penetration capability, thereby mitigating the impact of rainfall on wind retrievals.
Comment 4:
Please clarify the SVD/variational notation: define Λ_p explicitly, correct the SVD equation symbols, and detail the truncation criterion for K (energy, cross-validated error, or fixed)?Response:
(a) Λ_p has been corrected to Λ_K, where K is the truncation order. The definition of Λ_K has been added in the manuscript: "Λ_K is the diagonal matrix consisting of the first K largest singular values of the ensemble perturbation matrix A."(b) Selection of truncation order K:
K must be less than the rank of A (i.e., K < r = rank(A)) and cannot exceed the ensemble size M (since only the first r singular values are non-zero, and M limits the maximum effective dimension of ensemble perturbations). Additionally, truncation should retain the "dominant variance" of the ensemble perturbations, typically requiring that the sum of squares of the first K singular values accounts for ≥95% of the total variance from all non-zero singular values. In this study, with an ensemble size of 30, the truncation order is fixed at K = 27.Comment 5:
I strongly recommend to expand your introduction and cite below paper.
Assimilation of sentinel‐based leaf area index for modeling surface‐ground water interactions in irrigation districtsResponse:
The suggested reference has been added at the end of the first paragraph in the Introduction:
"Beyond meteorological forecasting, data assimilation has also been effectively applied in hydrological and environmental modeling to integrate multi-source observations, such as combining satellite-derived vegetation indices with in-situ measurements to improve the analysis of land surface and subsurface processes (e.g., Zafarmomen et al., 2024)."Comment 6:
Abstract: Consider explicitly stating that the main novelty is the computational optimization and parallelization that makes SVD-3DEnVar suitable for operational use, plus a first real-data test with satellite-derived sea surface winds.Response:
The abstract has been revised to emphasize the novelty:
"To bridge this gap towards operational readiness, this study introduces key computational optimizations: a new three-dimensional perturbation field generation scheme that supports multi-process parallelism and can directly generate any specified grid, and an efficient parallel implementation scheme tailored for the local patch assimilation in the SVD-3DEnVar scheme."Comment 7:
Notation consistency:
Ensure all symbols in the equations are defined once and consistently (e.g., Λ vs Λ_P in Equation (10); clarify if Λ_P is the same eigenvalue matrix as in Eq. (4) or modified).
Make sure the dimension of vectors and matrices in Eqs. (1)--(10) is always clear (model vs observation subspaces).Response:
The notation regarding Λ_p has been corrected as explained in the response to Comment 4. All equation symbols have been reviewed for consistency, and vector/matrix dimensions (model vs. observation subspaces) are explicitly stated in the revised manuscript.Comment 8:
Equation (11) Gaussian localization:
Check the condition in the piecewise definition:
You currently use (r_h ≤ l_h and r_v ≤ l_v) vs (r_h > l_h or r_v ≥ l_v). It might be clearer and more symmetric to use strict/≤ consistently.
Consider giving an example of actual physical localization scales (in km) corresponding to l_h, l_v, σ_h, σ_v.Response:
Equation (11) in manuscript has been revised to:
w(σ_h, σ_v) = {
exp( - (r_h^2 / σ_h^2) - (r_v^2 / σ_v^2) ), (r_h ≤ l_h and r_v ≤ l_v)
0, (r_h > l_h or r_v > l_v)
}In this study, l_h is set to 10 grid points (approximately 90 km), and l_v equals the number of model layers. Both σ_h and σ_v are set to 3 grid points, corresponding to about 27 km and 1.5 km, respectively. These parameters are detailed in Section 4.1. Note that grid counts are used as units for convenience in implementation. These are preliminary settings; future work will refine them and develop more effective optimization methods for data assimilation.
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RC2: 'Comment on egusphere-2025-4632', Anonymous Referee #2, 28 Dec 2025
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= General Comments =
This manuscript presents a technical advancement in the SVD-3DEnVar data assimilation scheme, focusing primarily on improving computational efficiency through a newly proposed three-dimensional ensemble perturbation generation method and a parallelization strategy. The authors evaluate this optimized framework using the TRAMS 3.0 model in both OSSE and real-data experiments.
The practical contribution of this study is evident, particularly the reported reduction in wall-clock time (from approximately 1,700 minutes to less than 15 minutes), which is highly relevant for operational numerical weather prediction. However, the manuscript requires substantial improvement in its scientific presentation. Several critical issues were identified regarding the mathematical rigor of the methodology, the theoretical consistency of the OSSE results, and the clarity of the experimental design. Specifically, the explanation of the load-balancing mechanism within the parallel strategy is vague, and multiple inconsistencies between the text and figures undermine the credibility of the findings.
Therefore, I recommend Major Revisions before this manuscript can be considered for publication.
= Specific Comments =
1. Clarification on Parallelization and Load Balancing (Line 255)
The authors state: "The approach adopted in this study allows for synchronous invocation of all processes... effectively avoiding the problem of idle waiting... thus significantly improving resource utilization."This explanation is scientifically insufficient. "Synchronous invocation" implies starting tasks simultaneously, but it does not inherently solve the computational load balancing problem. In a local patch-based domain decomposition, observations are rarely uniformly distributed. Consequently, using a static domain decomposition inevitably results in some processes handling significantly more observations than others. How does "synchronous invocation" prevent processes with fewer observations from finishing early and idling?
The authors seem to imply that a task redistribution mechanism is in place, but it is not clearly described. A detailed explanation of how the workload is balanced across processes is required (e.g., are grid points dynamically redistributed based on observation density or computational cost?).2. Theoretical Validity of OSSE Results (Figure 7)
In Figure 7, the analysis increment for the u-wind component (7b) appears almost identical to the "true error" (7a) in both spatial pattern and magnitude. According to data assimilation theory, the analysis increment is the product of the Kalman gain and the innovation (observation minus background). Unless the observation error (R) was set to zero or the background error covariance (B) was inflated to an unrealistic magnitude, the analysis increment should not match the true error so perfectly. This result raises serious questions about the experimental setup or the plotting (e.g., potential confusion between innovation and increment). The authors must verify this result and provide a physical or theoretical justification.3. Ambiguity in Observation Processing (Line 108)
The manuscript mentions: "valid data are selected according to program input requirements after quality control."
This description is too vague for a research article. How exactly are "valid data" selected? Does this process involve spatial thinning, super-obbing, or specific domain checks? A concrete description of the selection criteria is necessary to ensure reproducibility.4. Mathematical Rigor and Definitions (Section 3.1)
The mathematical description of the SVD-3DEnVar scheme lacks precision:- Line 135: The matrix AA is rectangular; therefore, it has singular values, not "eigenvalues."
- Undefined Symbols: In Eq. (4) (A=BΛV^T), the term V^T is not defined. Similarly, in Eq. (6) (x=bα), the variable αα appears without a proper definition. The authors should explicitly define these variables to aid reader understanding.
5. Overgeneralization of Results (Figure 8)
Based on a single OSSE case shown in Figure 8, the authors claim that "the SVD-3DEnVar scheme can significantly improve typhoon track and intensity forecasts." This statement is too strong for a single idealized experiment. It would be more appropriate to state that the scheme demonstrates potential for improvement in this specific case.6. Interpretation of “Operational Feasibility”
The manuscript frequently emphasizes the operational applicability of the proposed scheme based on reduced wall-clock time. However, operational feasibility depends not only on speed but also on resource availability and system stability. Given that some results rely on a large number of computing nodes (up to 300 nodes), it would be beneficial for the authors to discuss whether similar performance gains can be achieved under more constrained computational resources, which is a common reality for many operational centers.= Technical Corrections and Visualization Issues =
1. Figure 4: The red boxes representing parallel domains are difficult to distinguish because the model grid points are also represented by lines. I suggest representing the model grid points as dots and using lines only for the red boxes to improve visual clarity.
2. Figure 10 Issues:
- Visibility: The black line indicating the cross-section in Fig. 10a is obscured by the wind vectors. Please use a contrasting color (e.g., gray or magenta) or increase the line thickness.
- Physical Interpretation: The cross-section passes through the typhoon center. It is physically puzzling why the u-wind analysis increments (Fig. 10b) appear overwhelmingly positive across the center.
- Mismatches: The caption for Fig. 10d states it shows v-wind, but the unit label in the image is ππ (dimensionless pressure). Additionally, the caption describes panels up to (j), but the layout and labels need to be checked for consistency.
3. Section Titles: The titles for Section 4.2 and Section 4.3 are identical ("Results of OSSE Experiment"). Section 4.3 discusses real-data assimilation and should be titled accordingly (e.g., "Results of Real-Data Assimilation Experiment").
4. Figure 11 Caption: The caption is too brief. It needs to be expanded to clearly describe what panels (a) and (b) represent (e.g., "Comparison of typhoon tracks (a) and maximum wind speed (b) between CTL and DA experiments...").
5. Typos and Grammar:
- Line 16: "This paper constructed..." → "constructs" (or presents/proposes).
- Line 83: "microphysical schem" → "scheme".
- Line 261: "Figure 5a showed..." → "shows".
- Line 286: "The results shows that..." → "The results show that...".
- Line 445: "assimilate atmospheric variable" → "atmospheric variables".
Citation: https://doi.org/10.5194/egusphere-2025-4632-RC2
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The SVD-3/4DVar method, proposed by Qiu et al. (2007), is considered a pioneering achievement in the field of four-dimensional ensemble variational data assimilation (4DEnVar). This manuscript provides a valuable exploration of the practical application of the SVD-3DVar method and demonstrates certain innovative merits, meeting the publication criteria of this journal. The following suggestions are provided for further improvement:
1. Regarding the choice of methodology, it is recommended that you explain why SVD-4DVar was not adopted in favour of SVD-3DVar, while briefly analysing the core challenges of the latter.
2. It should be noted that the singular value decomposition (SVD) of matrix A is extremely challenging in practice due to its large dimensions (Nx+Ny), posing significant difficulties in terms of both storage and computation. A discussion on this aspect is recommended.
3. While equation (11) provides the Gaussian weight function, the localization scheme used in SVD-3DVar should be presented in more detail to enhance the completeness of the paper.
4. In recent years, 4DEnVar methods have advanced rapidly. To reflect an up-to-date understanding of the field, it is advisable to include references to relevant studies published between 2022 and 2025.
5. Regarding the generation of initial samples, several classical works (e.g. those by Evensen) have achieved high memory efficiency. It would be beneficial to reference these works and discuss their relevance to the present method. From a practical perspective, the main computational burden in parallelisation typically lies in the ensemble forecast component, which should also be addressed.
6. As this is an ensemble-based method, it is recommended that the ensemble sample update strategy in SVD-3DVar is explained briefly to improve the completeness of the methodological description.