Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams

2025-08-16

AI Summary

Manual structured visual reasoning on Skew-T diagrams for meteorological forecasting is labor-intensive and lacks scalability. Method: We propose a lightweight multimodal AI approach that employs a curriculum-learning-based two-stage training framework, integrating visual grounding, attention-guided feature localization, and multimodal chain-of-thought reasoning. A small-scale vision-language model (VLM) and language model (LLM) are jointly fine-tuned using Skew-T diagrams and auxiliary textual metadata to parse atmospheric vertical structure and predict precipitation probability. Contribution/Results: To our knowledge, this is the first work enabling small models to achieve forecast skill—measured by critical success index (CSI) and Brier skill score (BSS)—comparable to operational numerical weather prediction systems, while ensuring high interpretability and low computational cost. Ablation studies confirm the essential roles of visual grounding and reasoning supervision; attention visualization demonstrates consistent focus on physically meaningful features, including the lifted condensation level (LCL) and level of free convection (LFC).

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Forecasting from atmospheric soundings is a fundamental task in operational meteorology, often requiring structured visual reasoning over Skew-T log-P diagrams by human forecasters. While recent advances in Vision-Language Models (VLMs) have shown promise in other scientific domains, their application to meteorological diagram interpretation remains largely unexplored. In this study, we present a lightweight AI assistant that interprets Skew-T diagrams using a small language model (LM) and a small VLM fine-tuned to emulate human forecasters. Using a curriculum learning framework, we first train the models to identify key atmospheric features from diagrams through visual question answering, followed by chain-of-thought reasoning tasks that estimate precipitation probability based on the derived visual groundings. Model inputs include either textual summaries or generated Skew-T diagrams derived from operational Numerical Weather Prediction (NWP) forecasts, paired with three-hour precipitation observations from South Korea's Auto Weather Stations network. Evaluation results demonstrate that the fine-tuned VLM achieves skill comparable to an operational NWP model, despite relying solely on static atmospheric profiles. Ablation studies reveal that visual grounding and reasoning supervision are critical for performance, while attention map analysis confirms that the model learns to focus on relevant meteorological features. These findings highlight the potential of compact, interpretable multimodal models to support weather forecasting tasks. The approach offers a computationally efficient alternative to large-scale systems, and future work could extend it to more complex applications.

Problem

• Interpret Skew-T diagrams for meteorological forecasting using AI
• Develop lightweight multimodal models to emulate human forecaster reasoning
• Enhance precipitation prediction accuracy from static atmospheric profiles

Innovation

• Lightweight AI assistant for Skew-T interpretation
• Curriculum learning with visual question answering
• Fine-tuned small VLM emulates human forecasters