Tag
This paper introduces a probabilistic approach to end-to-end AI weather forecasting by enhancing the Aardvark Weather model with learned observation noise and Monte Carlo dropout, improving mean forecasts by 4.2% on average and separating aleatoric and epistemic uncertainty.
This paper introduces an iterative refinement framework for data assimilation that combines neural operators and diffusion models to super-resolve multiscale physical systems, outperforming baselines on benchmarks like Kraichnan turbulence.
Windborne Systems launched WeatherMesh 6, an AI weather forecasting model that claims to outperform the European Centre for Medium-Range Weather Forecasting (ECMWF) in accuracy and frequency, thanks to direct ingestion of sensor data from its balloons.
This paper proposes using the Ensemble Score Filter (EnSF), a score-based diffusion data assimilation method, to correct forecasts from a pretrained spatio-temporal energy consumption model using noisy partial observations. Numerical experiments show EnSF significantly improves state estimation over open-loop propagation and outperforms the Ensemble Kalman Filter under nonlinear observations.