The authors have answered all my questions properly.

This manuscript presents an adaptive observation weighting framework based on the TCKF1D-Var method for synergistic retrieval of atmospheric thermodynamic profiles from ground-based microwave radiometer and Mie–Raman lidar observations before nocturnal heavy precipitation events. The authors introduce dynamically optimized observation weights into the variational retrieval framework and demonstrate the performance using 107 nocturnal heavy precipitation cases over China. The study further investigates the contribution of individual GMWR channels and the vertical distribution of MRL observation weights.

The manuscript addresses an important scientific problem in atmospheric remote sensing and data fusion. Compared with traditional retrieval methods that assume fixed observation uncertainties, the proposed adaptive weighting approach provides a potentially useful strategy to dynamically adjust the relative contributions of multi-source observations under varying atmospheric conditions. The diagnostic analysis of observation weights also provides additional insights into the information content of different observations.

Overall, the manuscript is well organized, the methodology is clearly described, and the results demonstrate the potential advantages of the proposed framework. However, several aspects require further clarification before publication. In particular, the interpretation of diagnosed weights, and the limitations of the current validation strategy should be strengthened. Some conclusions regarding the physical meaning of adaptive weights should also be moderated because the current experiments mainly demonstrate statistical improvements rather than directly quantify observational information content. I therefore recommend minor revision.

Major Comments:

1. The theoretical interpretation of adaptive observation weights requires further clarification.The central innovation of this study is the introduction of adaptive observation weights into the TCKF1D-Var cost function. However, the current manuscript does not sufficiently explain the theoretical meaning of these optimized weights.According to Eqs. (3)–(5), the observation weights are introduced as adjustable parameters during the minimization process. However, it remains unclear whether these weights represent the relative information contributions of different observations or the mathematical scaling factors introduced to optimize retrieval performance. The manuscript frequently interprets large weights as indicating higher observation information content. However, the relationship between optimized weights and formal information content is not rigorously demonstrated. For example, the manuscript states that certain GMWR channels with large weights provide dominant observational constraints and that higher-altitude MRL observations receive larger confidence. However, because the weights are determined jointly by observation residuals, background uncertainties, and the retrieval framework itself, these interpretations may not necessarily indicate the intrinsic information content of the observations. The authors should clarify the physical interpretation of the adaptive weights, whether the diagnosed weights are comparable among different instruments and observation types, and what assumptions are required when interpreting weights as information contributions.
2. The validation strategy should better separate retrieval improvement from possible effects of the background field.The manuscript evaluates retrieval performance against radiosonde observations and compares adaptive weighting with static weighting. This approach is reasonable. However, because ERA5 profiles are used as prior information, the retrieved profiles may partly inherit characteristics from the background field. The manuscript should provide more discussion regarding whether adaptive weighting mainly adjusts observation influence or compensates for deficiencies in ERA5, and whether the improvements are consistent under different background uncertainties.
3. The physical interpretation of precipitation-dependent performance requires further discussion.The manuscript shows that the advantage of adaptive weighting becomes weaker under precipitation exceeding 30 mm/hr, especially for GMWR-only retrievals. This is an interesting result; however, the explanation that increased observational uncertainty reduces the effectiveness of adaptive weighting remains speculative. The authors should clarify whether precipitation contamination effects were explicitly considered in the observation processing, and whether the  signal attenuation and cloud effects influence the results.

Minor Comments:

1. Clarify the novelty compared with previous TCKF1D-Var studies.The manuscript extends previous studies by Zhang et al. (2026a,b). However, the distinction between the previous TCKF1D-Var framework and the current adaptive weighting version should be highlighted more clearly in the Introduction. A concise comparison table showing the differences between previous method and current improvement would help readers understand the methodological contribution.
2. Terminology of “dynamic weight” and “adaptive weight”.The manuscript uses both “adaptive observation weighting” and “dynamic weight” to describe the proposed method. Please consider using one consistent terminology throughout the manuscript. “Adaptive observation weighting” appears more appropriate because the method adjusts weights during optimization rather than necessarily evolving with time.
3. Definition of observation weights should be added explicitly.The manuscript should provide a clear mathematical definition of the weight coefficient. For example, Is a weight of 1 equivalent to the original observation contribution? Does a weight smaller than 1 indicate reduced confidence? Can weights larger than 1 occur? These details are important for reproducibility.
4. The manuscript uses MRL observations under heavy precipitation conditions. Since Raman lidar performance can be strongly affected by clouds and precipitation attenuation, the manuscript should provide more details about the cloud screening, the missing data treatment, and the signal quality criteria.
5. Clarify the retrieval vertical resolution.The manuscript discusses different vertical resolutions of GMWR and MRL observations, but the retrieval framework appears to use a common vertical grid. Please clarify the vertical resolution of the final retrieved profiles and how vertical resolution mismatch is handled.
6. Some statements in the conclusion imply that adaptive weights reveal physical observation information content. Since this interpretation is not directly validated through formal information-content analysis, the wording “information content ” should be moderated in Lines 74, 81, 287, 309, 422, 645, 679.
7. Line 19, “dynamically estimates observational contributions” can be replaced with “dynamically estimates the relative contribution of individual observations” for better understanding.
8. Lines 22–23, “with the largest benefits found for water vapor mass mixing ratio profiles” should be “with the most significant improvements found for water vapor mass mixing ratio profiles”.
9. Line 52, “a MRL” should be “an MRL”.
10. Line 114, “The MRLs emits” should be “The MRLs emit”.
11. Line 197, “sample amount” should be “sample size”.
12. Lines 203–204, “leads to a improved retrieval performance” should be “leads to an improved retrieval performance”
13. Lines 206 and 207, “framewrok” should be “framework”.
14. Lines 226–227, “prior thermodynamic profiles generated from ERA5 reanalysis” should be “prior thermodynamic profiles generated from  the ERA5 reanalysis”.
15. Table 1, “Case Amount” should be “Number of Cases”.
16. The title of Figure 11b should be “Hourly Precipitation ∈[10, 20) mm”, please make corrections.
17. Lines 462–463, “The fact that the adaptive observation weighting framework consistently achieves” can be changed to “The consistent lower retrieval errors achieved by the adaptive framework suggest that” for better understanding.