Flow matching for spatio-temporal downscaling with Anemoi

Summary

Machine-learning models can now produce highly skilful weather forecasts. For example, AIFS-CRPS (which now runs operational as AIFS ENS at ECMWF), GenCast, FourCastNet 3 and Aurora demonstrate that data-driven systems can generate skilful probabilistic predictions at medium-range timescales. At the same time, machine learning can increase the spatial and temporal resolution of existing forecasts. Spatial downscaling models such as Anemoi-D² learn to generate fine-scale atmospheric structure from coarse-resolution inputs. Temporal downscaling models such as HourGlass reconstruct realistic hourly evolution between forecast states that are available only every six to 12 hours. Forecasting, spatial downscaling and temporal downscaling can be treated as separate problems, allowing each component to be evaluated and improved independently. They can also be combined in different ways. Here, we explore one such combination: spatial and temporal downscaling are performed by a single machine-learning model, which can then be coupled to a separately trained machine-learning forecasting model. Adding detail in space and time The spatio-temporal downscaler is a flow-matching model, a generative machine learning technique, trained using the Anemoi framework, co-developed by ECMWF and several national meteorological services across Europe. The spatio-temporal downscaler transforms fields with low spatial and temporal resolution into higher-resolution, hourly sequences. It is trained on ERA5, ECMWF’s fifth-generation global reanalysis. Input data is provided to the model on an O96 grid, which corresponds to a horizontal resolution of approximately 100 km, at six-hourly intervals. The model then generates outputs on the N320 ERA5 native grid, which corresponds to a horizontal resolution of approximately 30 km, with one-hourly time steps, in chunks of five steps at a time. It therefore performs two tasks at once: - spatial downscaling from 100 km to 30 km - temporal downscaling from six-hourly to hourly fields Through flow matching, the model learns a conditional distribution of fine-scale spatial and temporal features that are consistent with the coarse input state. During training, it receives the coarse input together with a high-resolution ERA5 target that has been corrupted with Gaussian noise at a randomly selected noise level. It learns the direction in which this in…