The promising potential of vision language models for the generation of textual weather forecasts
By: Edward C. C. Steele , Dinesh Mane , Emilio Monti and more
Potential Business Impact:
Creates weather reports from weather videos.
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.
Similar Papers
Forecasting and Visualizing Air Quality from Sky Images with Vision-Language Models
Machine Learning (CS)
Shows air pollution from sky pictures.
A Physics-guided Multimodal Transformer Path to Weather and Climate Sciences
Machine Learning (CS)
AI predicts weather better using different data.
WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization
CV and Pattern Recognition
Helps drones see where they are in bad weather.