Speaker
Description
Accurate weather forecasts are core ingredients for many applications.
Farmers rely on weather forecasts to plan planting, irrigation, and harvesting. Airlines use weather data to plan flight routes, ensure passenger safety, and mini-mize delays. Utility companies use forecasts to anticipate demand changes due to temperature variations and to manage resources efficiently.
Current state of the art weather models such as ICON from the German weather service have nested regional models such as ICON-EU or ICON-D2 with resolutions from roughly ~6.5 km up to ~2.2 km resolution. The first model provides forecasts for up to 5 days, whereas the second model offers predictions only up to 48 hours in advance. While the second model benefits from higher resolution, it is limited by a shorter forecasting horizon.
This study builds upon existing weather models by attempting to integrate their strengths, aiming to achieve higher resolution forecasts across both temporal and spatial dimensions. We employed a top-performing Vision Transformer model, train-ing it with one year of ICON data focused on variables such as 2-meter Temperature and 10-meter Wind Speeds using an H100 GPU. Preliminary results are encouraging, demonstrating an improvement over actual measurements.
With our approach we are aiming to provide a KISSKI service that runs the inference and gives users access to high-resolution forecasts of the two variables for up to 5 days in advance via an API interface.
