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

TL;DR

This paper explores using vision language models to generate textual weather forecasts directly from video-encoded weather data, offering a promising new approach to improve efficiency and innovation in meteorological services.

Contribution

It introduces a novel application of vision language models for translating weather data into forecast text, demonstrating potential for scalable technological advancements.

Findings

Early results show promising scalability.

Potential for enhancing forecast production efficiency.

Opens new avenues for service innovation.

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.