the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Feb 2026
A Self-Supervised Precipitation Forecast Verification Based on Contrastive Learning
Abstract. Accurate precipitation forecast verification (PFV) is essential for improving forecasting models and supporting disaster management. However, current PFV methods remain limited, point-to-point methods are overly sensitive to minor errors, while spatial verification methods commonly require setting parameter and rule comprehensively, which constrains their availability. To tackle these issues, we are inspired by the success of deep learning in image verification through extracting high-level features, and thus propose a self-supervised contrastive learning-based PFV method (CLPFV). First, CLPFV uses precipitations augmentation (displacement, intensity, area size) to simulate actual forecast errors and construct positive and negative training sample pairs. Subsequently, with a novel loss function proportionally penalizing forecast errors, a backbone network is trained in CLPFV to extract high-level precipitation features. Finally, the cosine similarity of features is calculated as CLPFV’s verification score. Experiments demonstrate that CLPFV outperforms traditional (POD, FAR, TS) and spatial (FSS, SAL) verifications in different degrees of forecast errors and aligns better with expert assessments. In general, CLPFV offers an efficient deep learning solution for PFV tasks.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5746', Anonymous Referee #1, 15 Mar 2026
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RC2: 'Comment on egusphere-2025-5746', Anonymous Referee #2, 22 Mar 2026
This research proposes a self-supervised contrastive learning-based precipitation forecast verification method. It can effectively reflect the certain degree of forecasts errors through contrastive learning, thereby successfully addressing the “double penalty” issue of traditional point-to-point verification methods and the reliance on manually set parameters in spatial verification methods. Overall, the research holds considerable value and is recommended for publication after minor revisions.
Here are my suggestions and comments:
- In lines 65-66, this sentence “Therefore, a more comprehensive…still needed” is too long to read, please modify it for better understanding.
- In the paragraph of lines 70–75, the rationale for using deep learning to verify precipitation forecasts is not convincible enough. Please provide a more detailed explanation.
- In lines 180-183, the punishment function is not correct, first, it should be p(f-), second, it should provide a specific equation.
- In lines 209-210, the concept of “alternative sampling” is not clear, please specify the steps of sample splitting in detail.
- In figure 7, to improve its self-explanatory ability, it is recommended that the specific deviation values used be labeled directly below each subplot (e.g., “Displacement: -10, -5, 0, +5, +10” under subplot (a)), rather than only described in the caption.
- In the discussion of figure 9, is it possible to provide a benchmark to compare all verification methods? This will make the experimental results more intuitive.
- The content in lines 296–335 may be appropriately simplified to prevent redundancy with the information presented in figure 9.
Citation: https://doi.org/10.5194/egusphere-2025-5746-RC2
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- 1
This manuscript proposes a novel precipitation forecast verification (PFV) method, CLPFV, based on self-supervised contrastive learning. The study addresses a meaningful methodological issue, namely, how to develop a comprehensive verification method that is more tolerant of minor forecast errors, more sensitive to substantial errors, and better able to reflect different degrees of error. The basic idea of using data augmentations (displacement, intensity, and area size), together with an improved contrastive loss function, to train a neural network to learn the gradient of forecast errors is both scientifically sound and methodologically elegant. Overall, I find this manuscript valuable and potentially suitable for publication in GMD after minor revision.
Major Comments:
1. In the Introduction, there is a logical gap between the discussion of the limitations of spatial methods and the introduction of deep-learning-based image verification. Please explain more explicitly how the extraction of “high-level abstract features” directly helps address the spatial “double penalty” issue.
2. I suggest adding a short subsection, for example, “2.1 Basic Idea,” to explicitly present the core logic behind the proposed solution to the scientific gap. Part of the second-to-last paragraph of the Introduction already seems to contain this basic idea.
3. In Section 2, the conceptual framework is somewhat mixed with specific technical implementations (e.g., ResNet-18). In my view, the proposed verification framework does not strictly depend on ResNet-18 or InfoNCE. A brief discussion of the portability of this framework in the Discussion section, such as its applicability to other spatial modeling tasks, would further strengthen the methodological contribution of the paper.
4. The simplification of forecast errors into displacement, intensity, and area size is reasonable and useful. However, “area size” may not fully capture all structural errors in real precipitation forecasts. A brief acknowledgement of this limitation would improve the manuscript.
5. The rationale for the quadratic penalty in the improved loss function could be explained more clearly. The current explanation is understandable but somewhat brief. One or two additional sentences on why a quadratic penalty is appropriate here would make the design more transparent.
Minor Comments:
Several acronyms are used in the Abstract (POD, FAR, TS, FSS, SAL) without prior definition. Please ensure that all abbreviations are spelled out at their first occurrence.