Title: DiffLiG: Diffusion-enhanced Liquid Graph with Attention Propagation for Grid-to-Station Precipitation Correction
Venue: NeurIPS 2025
Accepted: yes
Keywords: Precipitation Interpolation

Graph Neural Networks

Meteorological Post-processing
Abstract: Modern precipitation forecasting systems, including reanalysis datasets, numerical models, and AI-based approaches, typically produce coarse-resolution gridded outputs. The process of converting these outputs to station-level predictions often introduces substantial spatial biases relative to station-level observations, especially in complex terrains or under extreme conditions. These biases stem from two core challenges: (i) $\textbf{station-level heterogeneity}$, with site-specific temporal and spatial dynamics; and (ii) $\textbf{oversmoothing}$, which blurs fine-scale variability in graph-based models. To address these issues, we propose $\textbf{DiffLiG}$ ($\underline{Diff}$usion-enhanced $\underline{Li}$quid $\underline{G}$raph with Attention Propagation), a graph neural network designed for precise spatial correction from gridded forecasts to station observations. DiffLiG integrates a GeoLiquidNet that adapts temporal encoding via site-aware OU dynamics, a graph neural network with a dynamic edge modulator that learns spatially adaptive connectivity, and a Probabilistic Diffusion Selector that generates and refines ensemble forecasts to mitigate oversmoothing. Experiments across multiple datasets show that DiffLiG consistently outperforms other methods, delivering more accurate and robust corrections across diverse geographic and climatic settings. Moreover, it achieves notable gains on other key meteorological variables, underscoring its generalizability and practical utility.


Peer reviews:
comments: SUMMARY:
This paper presents a graph-based approach to improve station level weather forecasting by correcting coarse resolution forecasts which are derived from numerical and AI-based models.  The approach includes three main contributions: a station specific temporal modeling, spatially adaptive modulator, and ensemble correction and uncertainty quantification. The performance of the approach beats existing methods in addressing spatial heterogeneity and smoothing.

STRENGTHS AND WEAKNESSES:
Strengths:
-The paper addresses a well-known problem in weather forecasting.
-The approach is novel, especially the architecture that combines atmospheric dynamics, adaptive constructions, and diffusion.  The design is modular and addresses different parts of the problem.
-The paper includes comprehensive ablation study. 
-The model has potential to generalize well across weather use cases and other variables such as temperature forecast.

Weaknesses:
-The paper could be strengthened by using other datasets.  The geographic region is focused on China.  Geographic diversity is needed to demonstrate the method's use for various climate zones.
-Few details could be better justified (e.g., internals of ensemble approach).
-Adding other weather variables will strenghen the forecast.
-Very little information is provided for computational efficiency even though the model is touted as lightweight.

QUESTIONS:
-Could you discuss computational tradeoffs of diffusion vs deterministic baselines?
-Can this approach incorporate physical constraints?
-Can this be generalized for multiple time scales?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces DiffLiG, a highly innovative and impactful framework that marks a substantial breakthrough in grid-to-station precipitation correction. It is the first work to systematically tackle the dual challenges of station heterogeneity and oversmoothing with a unified, modular architecture, and to provide comprehensive, large-scale experimental validation across multiple mainstream forecasting sources. Beyond methodological novelty, the significance of this work lies in its ability to enable fine-grained, station-level forecasts from coarse-grained global predictions, representing a critical step toward precision meteorology. DiffLiG is not only adaptable to diverse forecast products, but also capable of enhancing their outputs without modifying their internal structure, making it an indispensable post-processing tool for the next generation of AI-based weather forecasting systems. This work fills a clear methodological and practical gap, and has the potential to set a new direction for future research in high-resolution climate modeling.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n1.This work addresses a critical and previously underexplored problem in modern weather forecasting: transforming coarse-grained grid outputs into precise, station-level predictions. As the field moves toward high-resolution, localized, and user-relevant forecasts, this contribution is not only timely but strategically essential. \n\n2.While individual model components are grounded in prior work, the authors demonstrate clear and deliberate architectural innovation by tailoring these modules to the unique challenges of grid-to-station correction. This task-driven refinement is thoughtful and well-justified, representing a meaningful form of innovation.\n\n3.The paper is clearly written and well-organized, with figures and narrative consistently aligned around the two central challenges. The analysis is easy to follow, and the results provide concrete evidence that both identified issues are effectively addressed.\n\n4.The experimental evaluation is thorough, with comparisons against diverse baselines across multiple forecast sources, variable transfer tests, and sensitivity analyses. The release of code and training details further supports reproducibility and transparency.\n\n### Weaknesses\n\n1. The paper does not report the parameter size or computational cost of the proposed method compared to baselines, which would help assess its efficiency and practicality.\n\n2. Some parts of the method are described with limited detail, which may affect clarity and reproducibility for readers less familiar with the setting.\n\nQUESTIONS:\n1. Can the authors provide a more explicit evaluation of the model's performance across different seasons, based on the seasonal precipitation error analysis shown in the appendix?\n\n2. Can the authors provide a comparison of the parameter counts or model complexity between the proposed method and the baselines? This would help clarify the trade-off between predictive performance and computational efficiency, especially for deployment in real-world settings.\n\n3. Could the authors clarify how this method relates to existing super-resolution approaches? If higher-resolution inputs become available, would this framework still provide complementary benefits at the station level?\n\n4. In the variable transfer experiment from precipitation to temperature, what components or hyperparameters of the model were adjusted to achieve the reported results?\n\n5. What was the motivation behind including future station observations at time T2 in the problem formulation? Could the authors elaborate on the reasoning or practical scenarios that justify this design choice?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel approach for Precipitation Correction based on graph learning. In details, to avoid oversmoothing and fully leverage the rich information provided by the station-level heterogeneity, the authors introduce a GeoLiquidNet which adapts temporal encoding and a GNN to dynamicly learn spatially adaptive connectivity. Additionally, a probabilistic diffusion selector for ensemble forecasts generation is designed to avoid oversmoothing.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n1. This paper focuses on meteorological and remote sensing problems, which represent a real-world scenario with significant research value.\n2. The authors conduct extensive experiments to prove the effectiveness of the proposed method.\n\nCons:\n1. Although facing different scenarios, the GeoLiquidNet is essentially a DDPM-like model, merely applied to the meteorological domain to simulate historical evolution. At its core, it still relies on Gaussian noise for perturbation and generative modeling. Why does employing a Markovian-style diffusion process, along with location-conditioned time-step control, allow the model to flexibly adapt to diverse temporal rhythms across stations? Where exactly does the ``flexibility'' of such a Markov process come from? And what is the design rationale behind this time-step control mechanism?\n2. Some symbols are not explained. For example, what do $W_1$ and $W_2$ represent. And some symbols are used more than once. For example, in line 159, $\\kappa$ denotes the trainable dynamic coefficients, however, in line 175, $\\kappa$ denotes the decay factor. The authors should re-check the whole paper for such errors.\n3. The Figure 2 uses an excessive number of fonts and colors, making it difficult to understand the authors' idea. Moreover, some text elements are hard to read due to poor font and color choices. The figure also lacks a legend, and several elements with different meanings are presented using similar graphical forms, which further adds to the confusion.\n4. Why generating more samples could benefit for the oversmoothing problem? Wouldn't a more complex GNN further exacerbate the oversmoothing problem?\n5. In Figure 1, the explanation of Challenge 2 is quite vague, making it difficult to understand how it demonstrates the over-smoothing problem.\n\nQUESTIONS:\nMy main concenrs are as follows:\n1. Compared to DDPM, the novelty of GeoLiquidNet seems limited. And some designs are not well explained.\n2. The clarity of the figures. \n3. How the oversmoothing problem is sovled.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes DiffLiG, a complex three-component graph neural network for correcting gridded meteorological forecasts to station-level observations. The architecture combines GeoLiquidNet (using Ornstein-Uhlenbeck dynamics), Dynamic Edge Modulator, and Probabilistic Diffusion Selector. While evaluated on Chinese meteorological data, the paper fails to justify why such complexity is needed for what is essentially a spatial interpolation task.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe paper addresses station-level forecast correction, which has practical applications\nIncludes implementation details in appendices\nAttempts to combine multiple deep learning techniques\n\nWeaknesses:\n\nFundamentally flawed experimental design: The ablation study never removes the GCN backbone, making it impossible to determine if graph-based modeling is even beneficial. This is a fatal flaw that undermines the entire contribution\nUnjustified architectural choices: The use of OU dynamics from \"biological inspiration\" for meteorological data is scientifically questionable. No evidence provided that this offers any advantage over standard approaches\nMissing essential baselines: No comparison with simple MLPs (to justify graphs), AGCRN (state-of-the-art adaptive GNN), or operational weather post-processing systems\nNo statistical validation: Despite marginal improvements (~5%), no error bars, confidence intervals, or significance tests provided\n\n\nOverly complex presentation obscures lack of innovation\nCore claims unsupported: \"heterogeneity\" and \"oversmoothing\" presented as major challenges without evidence (other factors may also be challenging)\nContradictory motivations: emphasizes extreme events but never evaluates on them\n\n\nEvaluated only on China, only on precipitation (temperature relegated to appendix)\nNo cross-regional validation, no extreme weather evaluation\nImprovements too marginal to justify adoption\nNo theoretical insights, purely incremental engineering\n\n\nAll components borrowed from existing work without principled integration\nAppears to chase trends (diffusion models) rather than solve problems effectively\nNo novel contributions to understanding spatial interpolation or meteorological processes\n\nQUESTIONS:\nWhy is graph-based modeling necessary at all? Your ablation study keeps the GCN in all variants. What happens if you remove ALL graph components and use a simple MLP that directly maps from grid points to stations? This fundamental comparison is missing.\nWhy use GCN when recent work shows transformer-based architectures are superior for spatial tasks? Vision Transformers and spatial attention mechanisms have shown better performance on geographic data. Why stick with message-passing GNNs that are known to suffer from oversmoothing?\nWhy not use Neural Operators for this continuous field interpolation? Recent work on Fourier Neural Operators and DeepONet are specifically designed for function approximation between continuous fields. Your grid-to-station task seems perfectly suited for these architectures - why force it into a graph framework?\nHave you considered Implicit Neural Representations (INRs)? Methods like SIREN or Neural Fields can represent continuous spatial functions and naturally interpolate to any coordinate. This would eliminate your complex graph construction entirely. Why wasn't this explored?\nWhy use outdated GCN when Graph Transformers exist? Recent architectures like GraphGPS, SAN, or Graphormer address many limitations of GCNs. Your 2-layer GCN seems particularly primitive given advances in graph architectures.\nWhy separate temporal and spatial modeling? Recent spatiotemporal architectures like STGNNs, DCRNN, or Space-Time Transformers jointly model both dimensions. Your sequential approach (temporal then spatial) seems suboptimal.\nWhy not use Conditional Normalizing Flows for uncertainty? Your diffusion-based ensemble seems overcomplicated compared to flow-based models that can directly model the conditional distribution P(station|grid) with exact likelihood.\nHave you tried Cross-Attention mechanisms? Instead of fixed graph edges, cross-attention between grid and station features would be more flexible and learnable. Why use hard-coded k-nearest neighbors?\nWhat is the computational cost of your three-module architecture? Please provide concrete runtime and memory comparisons with baselines. How can practitioners justify such complexity for a spatial interpolation task?\nWhere is the statistical validation? Your improvements are often less than 5%. Without error bars, confidence intervals, or significance tests, how do we know these aren't within measurement noise? Please provide proper statistical analysis.\nWhy don't you evaluate on extreme events? Your introduction emphasizes extreme precipitation, yet your evaluation uses standard metrics on all data. What is the performance specifically on the top 1% or 5% of rainfall events?\nHow does your method perform with limited historical data? You assume 5 days of historical observations, but many stations have gaps. What happens with only 1-2 days of history, or none at all?\n\nLIMITATIONS:\nThe authors mention some limitations but miss critical ones:\n\nNo analysis of when the method fails\nNo computational cost discussion\nNo consideration of why operational agencies would adopt this over proven methods\nThe geographic limitation to China is downplayed\n\nMore concerning is what the paper doesn't acknowledge: this appears to be a solution in search of a problem.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 1, "DATE": "2025-07-02"}