Remove Barriers to Accessible Urban Climate Modelling with Large Language Models

Urban climate modelling tools like the Surface Urban Energy and Water balance Scheme (SUEWS) are indispensable for investigating complex surface–atmosphere interactions and guiding urban adaptation strategies. However, these models often present substantial barriers to use: they require extensive technical know-how, involve intricate input datasets, and can be time-consuming to set up and interpret. Recent advancements in Large Language Models (LLMs) hold promise for bridging this gap by transforming complex domain-specific tasks—such as data validation, simulation setup, and error diagnosis—into user-friendly interactive experiences.

In this study, we propose a novel workflow that leverages LLM capabilities—such as generative text, code suggestion, and context-driven troubleshooting—to streamline SUEWS usage and improve accessibility for researchers and practitioners:

• Automated Model Configuration
  We explore the use of LLM-guided prompts to generate properly formatted SUEWS input files, such as specifying hourly meteorological forcing data (e.g., temperature, wind speed, and humidity) or land cover fractions required for accurate simulations. By conversing with the model about location, time range, and data availability, users can rapidly produce consistent and error-checked setup files, reducing manual edits that often lead to inconsistencies.

• Interactive Error Diagnosis
  LLMs can parse error logs and suggest potential solutions in real time. For example, if SUEWS outputs an error related to missing albedo values for a specific land cover type, the LLM can pinpoint the source of the issue and suggest default values or a method for calculation based on site-specific conditions. For example, if a runtime error indicates a mismatch in the date format of meteorological input data, the LLM can identify the exact line causing the error, recommend the correct format, and provide a command or script snippet to rectify the issue. Through iterative dialogue, the model clarifies the root causes of typical setup or runtime issues, explaining how to fix them without requiring the user to trawl through detailed documentation.

• Model Output Interpretation
  Interpreting large volumes of SUEWS output, such as energy balance components (net radiation, latent heat flux, and sensible heat flux) or water budget terms (runoff and evapotranspiration), can be daunting, especially for newcomers. LLMs can summarise key metrics—like energy flux partitioning and surface runoff patterns—and highlight discrepancies in data, thereby assisting in rapid analysis and scenario comparison.

Our findings indicate that an LLM-enabled approach substantially lowers the learning curve and operational overhead associated with SUEWS, while still maintaining scientific rigour. We piloted trial deployments in teaching and professional contexts, reporting improvements in both setup speed and user confidence. Future work includes refining the LLM’s domain-specific training to ensure physically consistent responses—such as maintaining energy balance across flux computations or ensuring water budget closure—and incorporating advanced visualisation plugins for immediate data interpretation.

By harnessing the dialogic strengths of LLMs, we aim to remove barriers to the complexity of urban climate modelling, ultimately broadening participation and fostering more informed decision-making in cities worldwide.