Foundation models trained on satellite imagery can capture persistent surface properties (terrain, vegetation, water) that explain local weather variations better than hand-crafted features, enabling more accurate probabilistic weather predictions at arbitrary locations.
This paper shows that Earth observation embeddings from satellite data can improve weather downscaling—predicting local weather at specific locations from coarse global weather models.