the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Feb 2025
Indirect assimilation of radar reflectivity data with an adaptive hydrometer retrieval scheme for the short-term severe weather forecasts
Abstract. Different hydrometeor retrieval schemes are explored based on the Weather Research and Forecasting (WRF) model in the indirect assimilation of radar reflectivity for two real cases occurred during June 2020 and August 2018. When retrieving hydrometeors from radar reflectivity, there are two commonly used hydrometeor classification methods: “temperature-based” and “background hydrometer-dependent” schemes. The hydrometeor proportions are usually empirically assigned in the “temperature-based” method within different background temperature intervals. Whereas, in the “background hydrometer-dependent” scheme, each type of the hydrometeor is derived based on the portions estimated from the background field for different radar reflectivity ranges. In this study, a blending scheme is designed to combine “temperature-based” and “background hydrometer-dependent” methods adaptively to avoid errors caused by fixed relationships and reduce uncertainties introduced by the background field itself. Three experiments, EXP_temp, EXP_bg, and EXP_temp-bg are conducted using the “temperature-based” method, “background hydrometer-dependent” scheme, and blending scheme, respectively. It is found that, the blending scheme facilitates the generation of accurate hydrometeor species which will enhance the effectiveness of radar data assimilation. EXP_temp-bg is capable of analyzing radar reflectivity structures more accurately compared to both EXP_temp and EXP_bg. Besides, due to the adaptive combination of “temperature-based” and “background hydrometer-dependent” schemes, the EXP_temp-bg experiment predict the radar reflectivity structures and precipitation intensity more accurately.
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RC1: 'Comment on nhess-2024-203', Anonymous Referee #1, 17 Mar 2025
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The paper proposed an adaptive hydrometeor retrieval scheme that blends two existing methods for radar reflectivity assimilation: the “temperature-based” method and the “background hydrometer-dependent” method. The innovation lies in the development of a blending scheme that combines both methods adaptively, which helps to mitigate the limitations of each individual method. The topic is really interesting. However, some critical details should be further described. Here are my comments and suggestions.
Major comments
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In case 2, the author did not conduct a quantitative evaluation of radar reflectivity or precipitation forecasts. A more thorough quantitative assessment could be provided to better validate the performances of the different retrieval schemes.
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The description of the background hydrometer-dependent method is not clear, particularly regarding key implementation details. For example, it is unclear how the radar reflectivity threshold intervals are defined, what sample size is used for the background statistics, and how the climatological data are calculated. Providing more details in these aspects can help to enhance the reproducibility and transparency of the method.
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The last paragraph of the introduction provides only a superficial listing of each section’s content. To improve the clarity and effectiveness of the paper, it is recommended to expand on the role of each section. Elaborate on how they contribute to the overall narrative and objectives of the research, which will help readers gain a clearer understanding of the study’s scope and significance.
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The writing needs further improvement. It is recommended that the authors engage a professional editor or a native English speaker to proofread it, which would significantly boost the clarity and coherence of the manuscript.
Minor comments
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Abstract: The abstract does not explicitly address the impact of thermodynamic and dynamic structures on the forecast results. Given that the evolution of convective systems is closely related to environmental thermodynamics and dynamics (e.g., vertical velocity and wind shear), including a brief statement on how the proposed method enhances key thermodynamic structures would make the abstract more comprehensive.
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Line 48: Please rephrase this sentence “the EXP_temp-bg experiment predict the radar reflectivity structures and precipitation intensity more accurately”
Line 254: spelling mistake: “producess” → “produces”
Line 344: “1-h, 3-h, and 5-h forecasts valid at 2100 UTC 06 August 2018 for EXP_temp”?
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Section 2: Please use the style requirements of American Meteorological Society uniformly in words, formulas and charts. For example, single-character variables should be italicized; Use non-italic bold for vectors or matrices.
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Section 2.3 L148~154: Does α and a represent the same variable?
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Section 2.3.3: Equations (9) and (10) defining the blending scheme lack a clear explanation of how the weighting factors are determined.
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Section 2.3.3: What is the weight meaning β for? It needs more description.
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Section 3: The paper mentions the use of radar observations but does not provide sufficient details on the quality control procedures.
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Section 3: The observation error statistics estimated and used in DA determine the increment field for given innovations. Quantitative details of these statistics are crucial for understanding DA results but are not provided. Also, a more detailed description of the experiment design is required.
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Section 4: In the Fig. 6 and Fig. 12, the wind speed scale needs to be given in the lower right corner of the figure, and the length of the wind vector indicates the wind speed, and the unit is how much.
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Section 4.2: Where is the cross section shown in Figure 12?
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Section 5: The conclusion mentions using dual-polarization radar in future studies, but does not elaborate on how this would be integrated into the current framework. Providing more details on potential improvements or challenges would strengthen the future outlook.
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Some pictures (e.g. Fig. 4, Fig. 5, Fig. 10, Fig. 11, Fig. 12) are too small to read clearly. Please enlarge the labels for better visibility.
Citation: https://doi.org/10.5194/nhess-2024-203-RC1 -
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CC1: 'Comment on nhess-2024-203', Yi Wang, 19 Mar 2025
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This manuscript presents a novel adaptive hydrometeor retrieval scheme that blends the “temperature-based” and “background hydrometer-dependent” methods for radar reflectivity assimilation in WRF-3DVar. The proposed scheme effectively addresses the limitations of fixed empirical relationships and background-dependent uncertainties. By testing the method on two severe weather cases, the study demonstrates its effectiveness in improving reflectivity structure analysis and precipitation forecasts. The study explores an interesting topic, but certain key details should be further elaborated for clarity and completeness.
The methods and experimental setup section would benefit from additional details. The specific implementation of the retrieval method is unclear and should be elaborated. Moreover, the method of statistical background error covariance needs further explanation. The experimental procedures for the two case studies also need to be described in greater detail.
Minor points
- It is a little bit confusing to use the "the positive impact is not promising" here.
- It is recommended to include topographic information in the simulation domain map shown in Figure 1 to provide additional geographic context.
- More information about the radar observations used in the data assimilation should be provided, including details such as the type of radar, spatial and temporal resolution, quality control procedures.
- The wind vector arrows in Figure 6 are difficult to discern clearly. Please consider adjusting the arrow color, thickness, or scale to enhance visibility against the background.
- The description of the Equitable Threat Score (ETS) is incomplete. Please provide more details on the calculation method.
- The discussion of θ behavior in convectively unstable environments aligns with theoretical expectations in the Figure 12. While this background is useful, the section could be condensed to focus more sharply on novel aspects of the study.
Citation: https://doi.org/10.5194/nhess-2024-203-CC1
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