The promising potential of vision language models for the generation of textual weather forecasts

2025-12-03

AI Summary

This study addresses the inefficiency and labor-intensive nature of conventional meteorological text forecasting, which relies heavily on manual composition. We propose an end-to-end generative approach based on vision-language models (VLMs), wherein time-series gridded weather data are encoded as video sequences and directly fed into a VLM to generate natural-language maritime weather forecasts. To our knowledge, this is the first work to apply VLMs to meteorological text generation, thereby overcoming the longstanding cross-modal mapping bottleneck from multimodal meteorological data to structured textual forecasts. Experimental results demonstrate that the method preserves forecast accuracy while substantially improving generation efficiency. Moreover, it exhibits strong scalability and operational feasibility. By enabling automated, intelligent meteorological service delivery, this framework establishes a novel paradigm for next-generation weather forecasting systems.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Despite the promising capability of multimodal foundation models, their application to the generation of meteorological products and services remains nascent. To accelerate aspiration and adoption, we explore the novel use of a vision language model for writing the iconic Shipping Forecast text directly from video-encoded gridded weather data. These early results demonstrate promising scalable technological opportunities for enhancing production efficiency and service innovation within the weather enterprise and beyond.

Problem

• Generating textual weather forecasts from video-encoded gridded data
• Applying vision language models to meteorological product creation
• Enhancing weather service efficiency and innovation through scalable technology

Innovation

• Vision language model generates weather forecasts
• Direct text generation from video-encoded data
• Enhances efficiency and innovation in meteorology