Title: Learning Urban Climate Dynamics via Physics-Guided Urban Surface–Atmosphere Interactions
Venue: NeurIPS 2025
Accepted: yes
Keywords: urban climate; climate emulation; emulator; hybrid model; physics-AI
Abstract: Urban warming differs markedly from regional background trends, highlighting the unique behavior of urban climates and the challenges they present. Accurately predicting local urban climate necessitates modeling the interactions between urban surfaces and atmospheric forcing. Although off-the-shelf machine learning (ML) algorithms offer considerable accuracy for climate prediction, they often function as black boxes, learning data mappings rather than capturing physical evolution. As a result, they struggle to capture key land-atmosphere interactions and may produce physically inconsistent predictions. To address these limitations, we propose UCformer, a novel multi-task, physics-guided Transformer architecture designed to emulate nonlinear urban climate processes. UCformer jointly estimates 2-m air temperature $\(T\)$, specific humidity $\(q\)$, and dew point temperature $\(t\)$ in urban areas, while embedding domain and physical priors into its learning structure. Experimental results demonstrate that incorporating domain and physical knowledge leads to significant improvements in emulation accuracy and generalizability under future urban climate scenarios. Further analysis reveals that learning shared correlations across cities enables the model to capture transferable urban surface–atmosphere interaction patterns, resulting in improved accuracy in urban climate emulation. Finally, UCformer shows strong potential to fit real-world data: when fine-tuned with limited observational data, it achieves competitive performance in estimating urban heat fluxes compared to a physics-based model.


Peer reviews:
comments: SUMMARY:
The authors propose an attention-based approach that integrates urban climate knowledge and physical models to capture interactions between atmospheric phenomena and urban surfaces. Their method utilizes an encoder to model atmospheric–surface interactions and a decoder to guide the physical models. The experiments cover dataset descriptions, baseline comparisons, and ablation studies aiming to assess the robustness of the approach.

STRENGTHS AND WEAKNESSES:
The authors present an approach that combines data with physical models to study climatological phenomena in urban environments. Their results are outstanding, and ablation studies confirm that the correct components were integrated. They also provide code to reproduce the findings.

However, there should be more emphasis on clarifying how the physical models connect to the machine-learning models, and the code should include additional details to make it easier to use.

QUESTIONS:
1. I suggest adding a table that clarifies the mapping between the model and the elements of the inference scheme.
2- I suggest providing a minimal code example that loads the data and runs the test exercises.
3. Evaluate with a held-out city to demonstrate spatial robustness.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 4
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes UCformer, a Transformer-based architecture that integrates surface–atmosphere cross-attention and soft physics constraints for urban climate variables. It also introduces a new dataset built from multi-city, multi-decadal CLMU simulations. UCformer demonstrates improved RMSE and emulation skill over simple MLP, AutoML tree ensembles, and a vanilla Transformer.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nS1. Novel and important downstream tasks. Predicting urban climate variables at 3-hour resolution—2 m air temperature, specific humidity, and dew point—is both timely and practically valuable.\nS2. Physics-guided design. The cross-attention encoder and decoder inject domain knowledge softly, offering interpretability without hard constraints.\nS3. A new dataset. The CLMU simulation corpus (2020–2074) across six cities with chronological train/val/test/generalization splits enables robust evaluation.\n\nWeakness\nWhile the paper’s motivation and contributions are clear, it overlooks a vast body of multivariate time-series forecasting research, leading to two critical issues:\n\nW1. Problem definition ignored.\nThe authors’ own formulation (Sec. 3.1) makes it explicit:\n– Inputs: $X_f\\in\\mathbb{R}^{T\\times C\\times N}$, a sequence of $C$ forcing variables over $T$ timesteps across $N$ cities\n– Outputs: multi-step predictions of temperature, humidity, dew point over the same window\nThis is precisely a multivariate sequence-to-sequence forecasting task. Yet the manuscript fails to cite or discuss decades of work on RNN/LSTM forecasters, TCNs/CNNs for sequences, advanced Transformers for time series (Informer, Temporal Fusion Transformer, Autoformer, PatchTST), or even classical VAR/ARIMA methods.\n\nW2. Inadequate baselines.\nThe experimental section only benchmarks UCformer against:\n\n1. An MLP “ClimSim-style” regression\n2. An AutoML-selected tree ensemble\n3. A vanilla Transformer with no temporal forecasting enhancements\n   \nIt omits specialized forecasting models such as LSTNet and DeepAR, TCN-based architectures, Informer/PatchTST, and VAR/ARIMA. Without these comparisons, we cannot determine whether UCformer’s gains derive from its physics guidance or simply from its choice of baselines unsuitable for time series.\n\nW3. Implementation.\nBeyond the aforementioned problems, the core technical part of the paper is essentially not reproducible. Until the review time (6.30 AOE), the authors haven't fully released the code in their provided anonymous GitHub link. More importantly, 1) none of the novel physics components discussed in Section 3.4 appears in the provided code, 2) in the main implementation, no non-standard architecture appears other than naming things “climate_attention” and splitting the decoder into three branches. Everything is built on vanilla PyTorch Transformer primitives, significantly different from the paper's description, and 3) No data loading, loss functions, or training code is provided. Hence, it is impossible to verify the true technical designs of the proposed framework.\n\nQUESTIONS:\nSee weakness.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents UCFormer, a physics guided deep learning model based on transformer architectures designed to capture urban climate processes and predict air temperature, dew point temperature, and specific humidity. The model includes an encoder that aims to capture how the urban surface modulates incoming shortwave and longwave radiation and surface fluxes within the urban canyon. This encoder returns a latent representation of the outcome variables: air temperature, dew point temperature, and specific humidity. A decoder then converts the latent representations of the outcome variables to their target values. The structure of these transformer-based encoders and decoders is designed to encourage the model to learn and use physical rules and relationships when modelling atmospheric forcing-urban surface interactions and  decoding latent representations of urban climate properties to target variables. The model is trained on simulated data generated by the Community Land Model Urban from six cities and evaluated on a test set of data from 2050-2055, a generalisability experiment with simulated data from 2070-2074, and a comparison with Flux tower measurements. Compared to a range of deep learning architectures, which were not designed to emulate urban climate processes, UCFormer generated more accurate predictions of the target variables on the test set and future generalisation set. The UCFormer was also fine-tuned to predict latent and sensible heat fluxes that are measured at Flux towers and compared to CMLU simulated fluxes. The UCFormer model was better correlated with Flux tower measurements than CMLU.\n\nSTRENGTHS AND WEAKNESSES:\nThe authors present a well thought through and described approach to integrate physical rules and understanding of urban climate systems into a machine learning framework. The description of the model was comprehensive and there was a thorough set of evaluations and ablations studies. However, there was scope for more discussion of how the model could be applied to guide management and planning of urban environments. For example, if the primary aim of the model presented here was to emulate a physical model simulations, more detail could have been provided on computational efficiency gains (beyond a single line in the introduction) and why such gains are necessary (e.g. what kinds of analyses that have relevance for mitigating urban heat could be undertaken with more computationally efficient models?). However, if the primary aim was to more accurately simulate key urban climate variables, was there a reason why the authors did not evaluate the model against ground truth measurements (e.g. from weather stations) and compare physical models to the presented ML model? The comparison of UCFormer against other, more general, ML models was interesting. However, the improved performance of UCFormer is to be expected given the architecture is tailored for this task. It would have been interesting to see how UCFormer compared against other urban climate models.\n\nQUESTIONS:\nThere was also scope for more explanation of the results comparing model simulations to Flux tower measurements. As I understood what was presented, UCFormer was pre-trained using CMLU simulations and then calibrated (fine-tuned) with a subset of Flux tower measurements. Was the CMLU model also calibrated to Flux tower measurements? If not, are these results to be expected that UCFormer outperformed CMLU? Also, the model predictions of latent heat seem quite poor? Is there an explanation for this? And, how does this impact usability of the model or guide further research? \n\nA suggestion is to provide more details of the training dataset’s characteristics. Specifically, what was the spatial resolution / detail of the input data and where they were sourced from (e.g. parameters and variables characterising the urban surface). Also, it was not clear what the spatial resolution of the output predictions was? Providing such information would be useful to gauge what use cases the presented model is relevant for. \n\nI was interested in the distribution of predictions of target variables presented in the appendix. Have the author’s explored these in more detail and spatially and noted if there are certain geographic contexts / urban environments where model predictions are particularly erroneous? Relatedly, I would be interested in seeing results of the UCFormer predictions against observations from weather stations, and was there a reason the authors did not include such analysis as part of their evaluation?\n\nLIMITATIONS:\nYes (in the appendix)", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces UCformer, a multi-task, physics-guided Transformer architecture tailored for modeling urban climate dynamics. The model jointly predicts 2-m air temperature, specific humidity, and dew point temperature, embedding domain-specific urban surface-atmosphere interactions and physical priors into its architecture. The authors argue that conventional ML approaches inadequately capture complex urban processes due to a lack of physical grounding. UCformer addresses this gap via a domain-specific encoder and a physics-guided decoder, aiming to enhance generalizability and interpretability. Extensive experiments on both simulated and real-world datasets show that UCformer outperforms baseline models in predictive accuracy and generalization capacity, and can adapt to data-scarce real-world environments with minimal fine-tuning.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nIn terms of quality, the architecture is well-motivated, addressing an underexplored niche (urban climate modeling). Also, the experimental design is solid with diverse evaluation: emulation, generalization (future years), and transfer to real-world observations. There are clear performance gains over relevant baselines (Transformer, AutoML, MLP). Ablation studies reinforce the contribution of each module (encoder/decoder).\n\nIn terms of clarity, the paper is overall well-written.\n\nIn terms of significance, the paper addresses a critically understudied problem—urban climate prediction—with clear real-world implications (urban heat islands, planning). The paper could be highly impactful in climate-aware urban planning, if extended and validated further.\n\nIn terms of originality, embedding physics-informed priors directly into Transformer architectures in urban climate modeling is novel. The paper introduces a shared latent representation approach that exploits cross-city correlations, which is rare in ML climate literature.\n\n\nWeaknesses:\n\nI think the biggest weakness is that the paper lacks of detailed comparison to other physics-informed ML models, particularly in domains with overlapping temporal and spatial modeling needs. In Related work the authors have suggested several work on physics-guided ML, and hence the authors should have compared UCformer with previous work, or expain explain in the related work why previous work is not adequate for urban climate modeling.\n\nQUESTIONS:\nComparison to physics-guided ML: As the authors suggested, there are several physics-guided ML models, in particular in climate modeling. How does UCformer compares to these previous models? Are there some obvious reasons that these previous models are not applicable to urban climate modeling?\n\nReal-World Evaluation: How well does UCformer generalize to cities not included in training (e.g., cities with significantly different urban morphology or climate regimes)?\n\nModel Efficiency: Can the authors comment on inference time or model size relative to other ML-based climate models?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-23"}