| 11 May 2026
Research on deep learning-based missing echo restoration method for weather radar mosaic data
Abstract. Radar mosaic data represent a critical and widely utilized resource in weather forecasting. Nevertheless, the frequent occurrence of regional radar echo gaps, caused by factors including radar hardware malfunctions, data delivery delays, and software processing errors—each contributing to substantial spatial uncertainty in the missing areas—significantly constrains its quantitative application. To address this issue, we propose BiConvLSTM-UNet, a sequence reconstruction model designed to restore missing radar echoes. The model operates without relying on a missing-value mask during both training and inference, learning the inherent spatiotemporal variation patterns of radar echoes to reconstruct complete sequences. A post-processing procedure is implemented to minimize the impact of reconstruction on areas without missing data. Furthermore, multiple missing scenarios are synthetically generated to improve the model’s robustness and repair performance across diverse missing-data conditions. Comparative assessments against traditional and other deep learning approaches demonstrate the superior inpainting performance of the proposed BiConvLSTM-UNet across multiple missing-data scenarios. The method introduces minimal artifact to non-missing regions, and subsequent post-processing further diminishes reconstruction errors. Moreover, the model sustains consistent performance across varying missing data lengths and continuity patterns, indicating robust generalization capabilities. Consequently, the BiConvLSTM-UNet is more adept at addressing the intricate and varied scenarios of incomplete radar mosaic data encountered in practical applications.
Facing the problem of missing echo in radar mosaic data that often occurs in practical applications. A sequence reconstruction model for radar missing echo restoration is constructed in this study. Owing to the proposed reconstruction framework—which operates without reliance on missing masks—and the training strategy incorporating randomized missing patterns, the model demonstrates enhanced restoration performance and robust generalization capability. Furthermore, this approach also has the potential for operational applications. Nevertheless, there remain certain aspects that could be further refined in this study.
1. As described in Abstract, “Moreover, the model sustains consistent performance across varying missing data lengths and continuity patterns, indicating robust generalization capabilities.”, the statement of “consistent performance” is based on the experimental results in section 4.4. However, the numerical values are not the same. How should this be understood?
2.In Section 2.2, why is it necessary to separately establish an independent validation set to evaluate the model’s ability in reconstructing the missing echoes?
3. Why wasn't the optimization of various hyperparameters carried out in the validation set during the period of March to May 2022?
4. In the method of constructing the loss dataset, if the radar echoes within the selected area are set to zero using rectangles, will this cause the model to be more inclined to repair the rectangular missing areas?
5. Why was PixelShuffle used in the model design?
6. The weighted L1 loss in this paper adopts an artificial discrete weight design based on the reflectance threshold. Could you please explain the basis for setting these weights? Additionally, is this discontinuous segmented weight likely to cause unstable gradients during the optimization process and subsequently lead to a higher FAR in the experiments?
7. Why not adopt the model learning with missing mask instead of using mask-free learning?
8. Will post-processing result in discontinuity at the boundaries between the missing and non-missing regions?
9. In the non-missing area impact analysis experiment of section 4.3, the statement "Collectively, these results demonstrate that the post-processing strategy effectively preserves structural fidelity in non-missing regions while maintaining robust reconstruction capability in missing regions." is not very appropriate. From the results, it only relates to the non-missing areas and does not address the missing areas.
10. This paper mainly verified the model’s adaptability to different lengths of missing data and changes in temporal continuity. But do these missing patterns overlap with the missing distribution during the training stage? Could the model’s generalization ability be derived from the memory of the statistical characteristics of the missing patterns rather than truly learning the spatiotemporal evolution laws?