From Discrete Full-Field Labels to Any-Time Weather Forecasting

Full-field labels for data-driven weather forecasting are typically available only at discrete lead times, which ties the learned prediction mappings to predefined forecast intervals, while many weather processes and downstream applications require weather states at arbitrary times. To enable any-time prediction under this discrete supervision, we propose Physics-Supervised Any-Time Forecasting (PSAF). PSAF recomposes internal representation changes formed during fixed-endpoint forecasting into latent states for requested times, and maps them to weather fields through local decoding aligned with the forecast semantics of neighboring supervised lead times. To supervise intermediate times without labels, we use diagnostic-space physical surrogates as training targets. On WeatherBench 2, PSAF reduces the mean RMSE across 1–6 h lead times by 39.7% relative to endpoint field interpolation and by 10.1% relative to the strongest learned endpoint-reconstruction baseline. At intermediate lead times, PSAF outperforms the compared continuous-time methods on independent RTMA-RU analyses and downstream wind-power forecasting. Physics Surrogate Supervision further reduces the geostrophic and kinetic-energy target errors by 25.6% and 7.2%, while also improving physical diagnostics not included in the training objective.