2511.0020 AI-Powered Rainfall Forecasting: Progress, Challenges, Future Directions v1

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🎓 Meta Review & Human Decision

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Reject

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📋 Summary

This paper provides a review of the current state of AI-based rainfall forecasting, focusing on three main areas: physics-constrained machine learning, multi-modal data fusion, and extreme event prediction. I found the paper to be structured around these themes, discussing the advancements and challenges within each. The paper highlights the progress made in integrating physical constraints into machine learning models, such as the use of physics-informed neural networks (PINNs) and knowledge distillation techniques. It also explores the fusion of data from various sources, including radar, satellite, and ground-based observations, to improve forecasting accuracy. Furthermore, the paper delves into the complexities of predicting extreme rainfall events, which are critical for disaster management and planning. However, the paper primarily summarizes existing work and does not present any novel methodologies or findings. The writing quality is inconsistent, with numerous grammatical errors and a lack of precision in technical descriptions. The figures and tables, while generally referenced, lack detailed explanations, making it difficult to fully grasp their significance. Overall, the paper provides a useful overview of the field but falls short in terms of originality, clarity, and technical depth, which are essential for a research publication.

✅ Strengths

One of the strengths of this paper is its comprehensive coverage of the current state of AI-based rainfall forecasting. The paper effectively outlines the three main areas of focus: physics-constrained machine learning, multi-modal data fusion, and extreme event prediction. In the section on physics-constrained machine learning, the paper discusses the integration of physical laws into neural network architectures, which is a significant trend in the field. For instance, the paper mentions the use of physics-informed neural networks (PINNs) and knowledge distillation techniques, which are important methods for improving the physical consistency of AI models. The paper also highlights the fusion of data from multiple sources, such as radar, satellite, and ground-based observations, which is crucial for enhancing the accuracy and reliability of rainfall forecasts. The section on extreme event prediction addresses the challenges and methodologies for predicting rare but high-impact rainfall events, which is a critical area for disaster management and planning. The paper's discussion of these themes provides a solid foundation for understanding the current landscape of AI in rainfall forecasting. Additionally, the paper identifies key challenges in the field, such as data scarcity, model interpretability, and the generalization of models to unseen weather patterns, which are important considerations for future research. The paper's ability to summarize these challenges and the ongoing efforts to address them is a valuable contribution to the field, even if it is primarily a review.

❌ Weaknesses

Despite its comprehensive coverage, the paper has several significant weaknesses that detract from its overall quality and impact. Firstly, the paper lacks originality and does not provide any new insights or contributions. It primarily summarizes existing work and does not introduce novel methodologies, findings, or theoretical advancements. For example, the

💡 Suggestions

To improve this paper, several concrete and actionable changes are necessary. Firstly, the authors should clearly define the research question or objective they are trying to address. This will provide a focused direction for the paper and help readers understand the significance of the work. The introduction should be revised to explicitly state the problem, the approach taken, and the main contributions of the paper. Secondly, the paper needs a significant restructuring to enhance its logical flow. The authors should ensure smooth transitions between sections and subsections, explicitly linking different parts of the paper to create a coherent narrative. For instance, the connection between physics-constrained machine learning and multi-modal data fusion should be clearly articulated, explaining how these approaches complement each other in the context of rainfall forecasting. Thirdly, the writing quality must be substantially improved. The authors should carefully proofread the manuscript to correct grammatical errors, typos, and inconsistent use of technical terms. Tools for grammar and spell checking, as well as professional editing, could be beneficial. The technical descriptions should be more precise and detailed, providing sufficient background information for readers to understand the underlying mechanisms and limitations of the methods discussed. For example, when discussing physics-informed neural networks, the authors should explain how these models incorporate physical laws and constraints into the learning process, including the mathematical formulations and algorithmic descriptions. Similarly, the multi-modal data fusion section should detail the different techniques used to combine data from various sources, such as radar, satellite, and ground-based observations, and the challenges associated with each technique. The implementation details of the methods should also be expanded, including the datasets used, the evaluation metrics, and the computational resources required. This will make the paper more rigorous and accessible to both experts and non-experts in the field. Furthermore, the figures and tables need to be properly integrated into the text, with clear and detailed explanations of their purpose and significance. Each figure and table should be explicitly referenced in the text, and the authors should provide a brief description of what the figure or table is showing and how it relates to the main arguments of the paper. For example, Figure 1 should be accompanied by a detailed explanation of the different components of the AI-based rainfall forecasting system and how they interact with each other. The authors should ensure that the figures and tables are clear, concise, and easy to understand. Finally, the paper should include a thorough discussion of the ethical implications of using AI for rainfall forecasting. This could include a discussion of potential biases in AI models and the impact of these biases on different communities. The authors should also address the importance of transparency and accountability in the development and deployment of AI-based forecasting systems. By addressing these ethical considerations, the authors can demonstrate the responsible use of AI in this important domain. The paper should conclude with a clear summary of the main findings and contributions, and a discussion of future research directions, providing a roadmap for advancing the field of AI-based rainfall forecasting.

❓ Questions

1. Can you provide more details on how physics-informed neural networks (PINNs) are used in AI-based rainfall forecasting and what are the benefits of using them? Specifically, how do PINNs incorporate physical laws and constraints into the learning process, and what are the mathematical formulations and algorithmic descriptions involved? 2. Can you elaborate on the specific challenges in AI-based rainfall forecasting, including inadequate physical understanding, data-related issues, and model-related issues? How can these challenges be addressed, and what are the current limitations and ongoing efforts in this area? 3. Can you provide more details on the different types of data that are used in multi-modal data fusion for AI-based rainfall forecasting and how they are fused together? What are the specific techniques used to combine data from radar, satellite, and ground-based observations, and what are the challenges associated with each technique? 4. Can you provide more details on the different types of extreme events that are modeled in AI-based rainfall forecasting and how they are predicted? What are the specific methodologies used for extreme event prediction, and what are the evaluation metrics and computational resources required for these models?

📊 Scores

Soundness:2.0
Presentation:1.75
Contribution:1.75
Rating: 2.5

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📋 Summary

This paper is a review of AI-powered rainfall forecasting, organized around three themes: physics-constrained ML (e.g., NowcastNet, GraphCast), multi-modal data fusion (e.g., MRMS, radar–satellite–gauge integration), and extreme event prediction (e.g., adversarial training, attention). It surveys recent advances (Section 2), outlines challenges across physical representation, data, and model-related issues (Section 3), and proposes solution directions including PINNs, knowledge distillation, data denoising and fusion, regularization, transfer learning, and interpretable ML (Section 4). The paper concludes with recommendations to better integrate physics, improve data quality and integration, enhance interpretability, and increase robustness to climate non-stationarity (Section 5).

✅ Strengths

  • Clear high-level taxonomy of the field into physics-constrained ML, multi-modal fusion, and extreme events, which is useful for readers entering the area (Abstract; Sections 2, 3, 4).
  • Challenge–solution mapping is concrete and actionable: PINNs and knowledge distillation for physics (Section 4.1), denoising and multi-modal fusion for data (Section 4.2), and regularization, transfer learning, and interpretability (LRP, attention) for model limitations (Section 4.3).
  • Appropriately references canonical architectures and lines of work (ConvLSTM [22], U-Net [20], MRMS; physics-constrained GANS [8]; transfer learning [24]).
  • Timely topic with practical importance and a broad, accessible narrative that can orient interdisciplinary audiences.

❌ Weaknesses

  • Lack of systematic review methodology: no description of search strategy, inclusion/exclusion criteria, coverage bounds, or completeness assurance; leads to potential selection bias.
  • Empirical claims are not supported by consistent metrics or standardized benchmarks across the surveyed advances. For example, while CSI is mentioned via prior work [5] (Section 1), the review does not consistently discuss standard metrics such as RMSE/MAE, POD, FAR, CSI, FSS, or benchmark datasets (e.g., MRMS/HKO/SEVIR/WeatherBench), limiting rigorous comparative assessment.
  • Several strong claims in Section 2.3 (e.g., AIFS ‘20% higher’, GenCast ‘97%’ better than ENS over ~1,300 indicators, Aardvark as a fully deep-learned replacement) lack precise citations, metrics, spatial/temporal scope, variables, or evaluation protocols. As written, these are difficult to verify.
  • Scope ambiguity: the paper sometimes mixes nowcasting and medium-range forecasting, global weather models and precipitation-specific modeling (e.g., GraphCast description in Section 2.2 focuses on atmospheric variables but implications for precipitation skill are not clarified), potentially confusing readers about what constitutes ‘rainfall forecasting’ in each example.
  • Some technical statements appear imprecise and need correction or sourcing (e.g., NowcastNet ‘embedding Navier–Stokes residuals’ and ‘curbed errors in complex terrains’, GraphCast ‘outperformed traditional NWP in extreme event detection’ in the Conclusion; these require specific references, metrics, and evaluation settings).
  • Writing and presentation issues: typos and inconsistencies (e.g., 'Al' vs 'AI'), uneven depth, and missing figure/table-based summaries reduce clarity. The paper would benefit from comparative tables summarizing model type, inputs, lead times, spatial resolution, training datasets, compute, and metrics.
  • Limited discussion of compute cost, energy/carbon footprint, data licensing/access constraints, and operationalization considerations, which are practically significant for deployment.

❓ Questions

  • Please clarify the scope: Which results and models are about short-term precipitation nowcasting (0–3h, radar-driven) versus medium-range forecasts (days, reanalysis-driven)? How do you define 'rainfall forecasting' in each context throughout Sections 2.2–2.3?
  • For Section 2.3, provide precise citations, metric definitions, target variables, domains, and evaluation protocols for the claims about AIFS (‘20% higher’), GenCast (‘97% better than ENS across ~1,300 indicators’), and Aardvark Weather. Which metrics (e.g., RMSE/MAE, CRPS, Brier, POD/FAR/CSI/FSS) and datasets/periods were used?
  • GraphCast (Section 2.2): Please specify whether precipitation was directly predicted or derived, and provide precipitation-specific comparative metrics and baselines, if any. If not, please limit claims about precipitation skill accordingly and cite the relevant evaluations.
  • NowcastNet: You state it 'unifies physical-evolution schemes and conditional-learning' and in the Conclusion imply 'embedding Navier–Stokes residuals.' Could you provide citations and clarify whether NowcastNet explicitly uses PDE residuals in its loss, or if it enforces physical consistency via other mechanisms?
  • Multi-modal fusion: Could you expand on practical treatments for unaligned heterogeneous data (temporal offsets, varying coverage, missing modalities), including architectures and training strategies? The current discussion cites [4] but lacks operational details.
  • Could you include a standardized comparison table covering: data inputs (radar/satellite/gauges/NWP), spatial/temporal resolution, lead time, modeling approach, training corpus scale, compute/training budget, and evaluation metrics for representative methods (e.g., ConvLSTM, U-Net variants, NowcastNet, MetNet/MetNet-2, GraphCast, ClimX, MRMS-based systems)?
  • Can you document your review methodology (databases searched, time window, keywords, inclusion/exclusion criteria) to strengthen reproducibility and reduce selection bias?
  • Your prior work [5]: please provide the explicit form of the multi-sigmoid 'differentiable CSI' loss and discuss its empirical trade-offs versus standard surrogates (e.g., focal/Tversky losses).
  • How do you recommend handling non-stationarity due to climate change beyond transfer learning and data augmentation (e.g., covariate shift correction, robust uncertainty quantification, distributionally robust optimization)?
  • Please discuss operational considerations: model uncertainty communication, forecaster-in-the-loop workflows, compute/carbon costs, and dataset licensing/access in national weather services.

⚠️ Limitations

  • The paper is a narrative review without a systematic methodology, which risks selection bias and incomplete coverage.
  • Empirical claims of 'remarkable progress' are not consistently grounded in standard, comparable metrics or benchmarks, limiting the reader’s ability to judge progress across methods.
  • Scope ambiguity between nowcasting and medium-range forecasting, and between general weather models and precipitation-specific models, may confuse the reader and overstate precipitation-centric conclusions.
  • Limited attention to compute/energy costs and environmental impact for large models (e.g., GraphCast, foundation models), and to data access/licensing constraints that affect real-world deployment.
  • Potential negative societal impacts if overconfident or poorly calibrated AI precipitation forecasts are used in disaster response, agriculture, or water management. The paper could discuss uncertainty quantification, calibration, and human-in-the-loop safeguards more explicitly.
  • The review briefly mentions interpretability (LRP, attention) but does not connect it to operational trust and accountability frameworks for high-stakes decision-making.

🖼️ Image Evaluation

Cross‑Modal Consistency: 30/50

Textual Logical Soundness: 18/30

Visual Aesthetics & Clarity: 9/20

Overall Score: 57/100

Detailed Evaluation (≤500 words):

1. Cross‑Modal Consistency

• Major 1: Fig. 4 shows four unlabeled snapshots with timestamps but no (a–d); caption mentions “1 minute” without mapping panels, blocking time-sequence verification. Evidence: Fig. 4 panels lack sublabels/axes/legend.

• Major 2: Ambiguity around “Figure 3”: three different conceptual diagrams appear near Sec. 4 but only one figure is cited, making dashed/solid arrow meaning unclear. Evidence: Sec. 4 caption “Figure 3 … dashed arrows indicate …; solid arrows …”.

• Minor 1: Figure 1–3 are conceptual infographics without variables/units, while text discusses concrete methods/results; mapping claims to visuals is weak. Evidence: Fig. 1 “Climate‑Proof/Trustworthy/Physics‑Guided/Data Renaissance” only.

• Minor 2: In Sec. 2.3, “satellite, radar (Figure 4), and ground-based” implies Fig. 4 supports multi‑modal fusion, but Fig. 4 is only radar volume. Evidence: Sec. 5: “satellite, radar (Figure 4), and ground‑based measurements”.

2. Text Logic

• Major 1: Unsupported flagship metrics: “AIFS … accuracy … 20% higher,” “GenCast … 97% … better than ENS,” no citations/experiments provided here. Evidence: Sec. 2.3: “accuracy … 20% higher …”; “about 97% … better than ENS”.

• Major 2: Likely factual inaccuracy: “NowcastNet, by embedding Navier‑Stokes residuals,” which the original work does not explicitly claim as a PINN‑style residual loss. Evidence: Sec. 5: “NowcastNet, by embedding Navier‑Stokes residuals”.

• Minor 1: Model naming inconsistency (“GenCast” vs earlier “GraphCast”); may confuse readers. Evidence: Sec. 2.3: “GenCast model … exceeds the ENS”.

• Minor 2: Recurrent “Al” vs “AI” typos and some grammar/spacing issues; readability impacted but not blocking. Evidence: Multiple instances, e.g., Sec. 2: “Al’s ability…”.

3. Figure Quality

• Major 1: Illegible at print size: small fonts and low‑resolution rasterization in Fig. 1–3; key labels cannot be read comfortably. Evidence: Fig. 1–3 text elements <≈8 pt and blurry.

• Major 2: Fig. 4 lacks axes, scale bars, colorbar, or geographic/altitude references; 3‑D volumes are uninterpretable quantitatively. Evidence: Fig. 4 shows colored volumes with only timestamps.

• Minor 1: No legends or call‑outs explaining dashed vs solid arrows (Fig. 3), failing the “figure‑alone” test. Evidence: Fig. 3 has dashed/solid arrows without legend panel.

• Minor 2: Heterogeneous figure styles (color palettes, typography) reduce coherence.

Key strengths:

  • Timely, comprehensive survey framing around physics constraints, multi‑modal fusion, and extremes.
  • Good breadth of references across DL architectures and operations.

Key weaknesses:

  • Central performance claims lack in‑text evidence and precise citations.
  • Inaccuracy/ambiguity on NowcastNet mechanics and “GenCast” naming.
  • Figures are low‑resolution, minimally annotated, and not self‑explanatory; Fig. 4 lacks essential metadata.

Recommendations:

  • Provide exact citations and quantitative summaries (datasets, metrics, baselines) for AIFS/GraphCast/“GenCast” claims.
  • Correct NowcastNet description; distinguish physics‑guided evolution from PINN residual losses.
  • Redraw figures as vector graphics, add legends/scales; label Fig. 4 subpanels (a–d) with times and add axes/colorbar.

📊 Scores

Originality:2
Quality:2
Clarity:2
Significance:2
Soundness:2
Presentation:2
Contribution:2
Rating: 3

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📋 Summary

This paper provides a review of the current state of AI-powered rainfall forecasting, highlighting recent advancements, challenges, and potential future directions. The core contribution of this work lies in its synthesis of various AI techniques applied to rainfall prediction, rather than proposing a novel methodology or presenting original experimental results. The paper begins by outlining the importance of accurate rainfall forecasting and the limitations of traditional numerical weather prediction (NWP) models. It then delves into the application of machine learning (ML) and deep learning (DL) techniques, emphasizing three key areas: physical-constrained ML, multi-modal data fusion, and extreme event prediction. The paper discusses how AI can address the limitations of NWP models, particularly in subgrid-scale parameterization, and highlights the potential of multi-modal data fusion to improve forecast accuracy. It also examines the challenges associated with AI-based rainfall forecasting, including the need for better physical understanding, data management, and model interpretability. The paper explores potential solutions, such as physics-informed neural networks (PINNs) and knowledge distillation techniques, to enhance the physical consistency of AI models. Furthermore, it emphasizes the importance of data quality, quantity, and integration for improving model performance. The paper concludes by reiterating the potential of AI to revolutionize rainfall forecasting and its significance for disaster management and other sectors. While the paper provides a useful overview of the field, its primary value is as a review and synthesis of existing work rather than a source of novel research findings. The paper's analysis of the current state of AI in rainfall forecasting is relevant to the broader ICLR community, given the increasing application of machine learning techniques in various domains. However, the lack of original research and the somewhat superficial treatment of certain topics limit its overall impact.

✅ Strengths

The paper's primary strength lies in its comprehensive review of the current state of AI-powered rainfall forecasting. It effectively synthesizes a wide range of recent advancements in the field, providing a valuable overview for researchers and practitioners. The paper successfully identifies three key areas where AI is making significant contributions: physical-constrained ML, multi-modal data fusion, and extreme event prediction. By highlighting these areas, the paper provides a useful framework for understanding the current landscape of AI in rainfall forecasting. The paper also effectively articulates the limitations of traditional NWP models and how AI can potentially address these shortcomings. The discussion of challenges, such as the need for better physical understanding, data management, and model interpretability, is also a strength. By identifying these challenges, the paper points to important areas for future research. The paper's exploration of potential solutions, such as PINNs and knowledge distillation, demonstrates a forward-looking perspective and suggests avenues for further development. The paper's emphasis on the importance of data quality, quantity, and integration is also a valuable contribution. By highlighting these issues, the paper underscores the critical role of data in the success of AI-based forecasting. The paper's focus on the practical applications of AI in rainfall forecasting, particularly in disaster management, further enhances its value. By connecting the technical aspects of AI to real-world needs, the paper demonstrates the potential impact of this research. Finally, the paper's clear and concise writing style makes it accessible to a broad audience, including those who may not be experts in the field.

❌ Weaknesses

One of the most significant weaknesses of this paper is the lack of original research. As a review article, it does not present any new methodologies, experiments, or results. This limitation is inherent in the nature of a review, but it does impact the paper's overall contribution to the field. The paper primarily summarizes existing work rather than pushing the boundaries of knowledge. The paper also suffers from a somewhat superficial treatment of certain topics. While it covers a wide range of issues, the depth of analysis for each challenge and solution is limited. For example, the discussion of physics-constrained ML and multi-modal data fusion could benefit from a more detailed exploration of specific techniques and their limitations. The paper also lacks a clear articulation of the unique insights or perspectives that it brings to the topic. While it synthesizes existing knowledge, it does not offer a novel framework or interpretation of the material. The paper's structure could also be improved. While the flow is generally logical, some sections could be more clearly organized and focused. For instance, the section on 'Directions for Technical solutions' could be more tightly integrated with the preceding discussion of challenges. The paper also makes some claims that lack sufficient evidence. For example, the statement that 'Al has emerged as a powerful solution to overcome the long-standing limitations of traditional numerical weather prediction (NWP) and statistical downscaling models (SDMs)' is not fully supported by the evidence presented in the paper. While the paper discusses the potential of AI, it does not provide a comprehensive evaluation of its performance relative to traditional methods. The paper also uses some technical terms without sufficient explanation. For example, the term 'subgrid-scale parameterization' is used without a detailed definition. This could make the paper less accessible to readers who are not familiar with these concepts. The paper also lacks a clear discussion of the limitations of the proposed solutions. For example, the paper discusses PINNs as a solution for incorporating physical laws, but it does not address the challenges associated with implementing these models in practice. The paper also does not provide a comprehensive evaluation of the proposed solutions. For example, the paper suggests using advanced data cleaning algorithms, but it does not provide any evidence to support the effectiveness of these methods. The paper also lacks a clear discussion of the computational cost of the proposed solutions. For example, the paper discusses the use of complex neural network architectures, but it does not address the computational resources required to train and deploy these models. Finally, the paper's conclusion is somewhat weak. While it summarizes the main points of the paper, it does not offer a compelling vision for the future of AI in rainfall forecasting. The paper could benefit from a more forward-looking perspective that highlights the potential impact of this research.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete improvements. First, while acknowledging the paper's nature as a review, future iterations could benefit from a more explicit framing of the research gaps that remain after considering the current state of the field. This would shift the focus from simply summarizing existing work to highlighting the areas where further research is most needed. For example, the paper could include a section that specifically discusses the open questions and challenges that have emerged from the existing literature. Second, the paper should strive for a more in-depth analysis of the topics it covers. Instead of providing a broad overview of many topics, it could focus on a few key areas and explore them in greater detail. For example, the paper could delve deeper into the specific techniques used in physics-constrained ML, multi-modal data fusion, and extreme event prediction, and discuss their respective strengths and limitations. Third, the paper should provide more evidence to support its claims. For example, when stating that AI has emerged as a powerful solution, the paper should provide specific examples and citations to support this claim. The paper should also avoid making broad generalizations without sufficient evidence. Fourth, the paper should provide clear definitions for all technical terms. This would make the paper more accessible to a broader audience. For example, the paper should provide a clear definition of 'subgrid-scale parameterization' and other technical terms. Fifth, the paper should include a more detailed discussion of the limitations of the proposed solutions. For example, when discussing PINNs, the paper should address the challenges associated with implementing these models in practice. The paper should also provide a more comprehensive evaluation of the proposed solutions. For example, when suggesting the use of advanced data cleaning algorithms, the paper should provide evidence to support the effectiveness of these methods. Sixth, the paper should include a discussion of the computational cost of the proposed solutions. This is an important consideration for the practical application of AI in rainfall forecasting. The paper should also discuss the trade-offs between model complexity and computational cost. Seventh, the paper should conclude with a more compelling vision for the future of AI in rainfall forecasting. The conclusion should not only summarize the main points of the paper but also offer a forward-looking perspective that highlights the potential impact of this research. Finally, the paper could benefit from a more rigorous editing process to ensure that the language is clear, concise, and consistent throughout. This would improve the overall readability and impact of the paper.

❓ Questions

Several key questions arise from my analysis of this paper. First, given the paper's focus on the current state of AI in rainfall forecasting, what are the most pressing research questions that remain unanswered? The paper identifies several challenges, but it does not prioritize them or discuss which of these challenges are most critical for advancing the field. Second, how can the limitations of AI models, particularly their lack of physical understanding, be addressed more effectively? The paper discusses PINNs and knowledge distillation, but it does not evaluate the effectiveness of these methods or compare them to other approaches. Third, how can the quality, quantity, and integration of data be improved to enhance the performance of AI-based rainfall forecasting? The paper highlights the importance of data, but it does not provide specific recommendations for addressing the challenges associated with data collection, management, and integration. Fourth, how can the interpretability of AI models be improved to increase trust and adoption in operational forecasting? The paper acknowledges the 'black box' nature of many AI models, but it does not offer concrete solutions for addressing this issue. Fifth, what are the computational trade-offs associated with different AI models, and how can these trade-offs be managed in practice? The paper discusses complex neural network architectures, but it does not address the computational resources required to train and deploy these models. Sixth, how can the robustness of AI models be ensured in the face of changing climate conditions? The paper mentions the impact of climate change, but it does not discuss how AI models can be adapted to handle these changes. Seventh, what are the ethical considerations associated with the use of AI in rainfall forecasting, and how can these considerations be addressed? The paper does not discuss the potential ethical implications of using AI in this domain. Finally, how can the insights from this review be translated into practical applications that benefit society? The paper mentions the importance of rainfall forecasting for disaster management, but it does not discuss how the findings of this review can be used to improve real-world forecasting practices.

📊 Scores

Soundness:1.5
Presentation:1.5
Contribution:1.5
Confidence:4.0
Rating: 2.0

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