WeatherJEPA: Joint Embedding of Observational and Atmospheric State Representations for Weather Forecasting

Accurate weather forecasting depends not only on learning atmospheric dynamics, but also on determining how atmospheric information should be represented. Many leading data-driven weather models predict future gridded atmospheric fields from reanalysis-based training data, whereas observation-driven systems infer atmospheric representations directly from heterogeneous measurements. These approaches reveal a fundamental representation trade-off: atmospheric state representations provide coherent and physically structured information but inherit assumptions from assimilation systems, while observational representations preserve direct measurements but must discover atmospheric structure from incomplete evidence. We introduce WeatherJEPA, a joint embedding framework that aligns complementary observational and atmospheric-state representations into a shared latent space. The atmospheric-state representation provides a structured view during training, while the observational representation defines the deployment pathway. Rather than reconstructing one representation from the other, WeatherJEPA learns their relationship through joint embedding, grounding, and temporal prediction objectives. Frozen-state readouts and ten-day forecasts show that initial accessibility does not consistently predict forecast ranking. Under matched observation-driven comparisons, WeatherJEPA achieves the lowest aggregate probe errors for both physical fields and in-situ observations. Across six representative station, radiosonde, and satellite targets, it reduces Day-10 forecast error by 4.4–12.7% relative to the strongest competing learned method for each target. On ICOADS marine zonal wind observations, its Day 1–10 mean MAE is 4.4% lower than IFS-HRES.