Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models

Abstract

We introduce a probabilistic diffusion-based method for global atmospheric downscaling implemented within the Anemoi framework. The approach transforms low-resolution ensemble forecasts into high-resolution ensembles by learning the conditional distribution of finer-scale residuals, defined as the difference between the high-resolution fields and the interpolated low-resolution inputs. The system is trained on reforecast pairs from ECMWF IFS, using coarse fields at 100 km to reconstruct fine-scale variability at 30 km resolution. The bulk of the training focuses on recovering small-scale structures, while fine-tuning in high-noise regimes enables the generation of extremes. Evaluation against the medium-range IFS ensemble target shows that the model increases probabilistic skill (FCRPS) for surface variables, reproduces target power spectra at small scales, captures physically consistent multivariate relationships such as wind-pressure coupling, and generates extreme values consistent with those of the target ensemble in tropical cyclones.

Figures (10)

• Figure 2: Schematic of the diffusion-based downscaling model. At each denoising step, the model takes as input on the O320 grid: (i) the current noisy residual estimate, (ii) static and temporal forcing fields, and (iii) the coarse O96 atmospheric fields interpolated to O320 ($D_{96\to320}$). These features are processed by an encoder–processor–decoder graph neural network: the encoder projects information from O320 to the hidden grid, the processor applies 16 successive graph-transformer layers on the hidden graph, and the decoder projects the result back to O320. In the lower panel, the pink boxes represent these three consecutive components. The model output is a prediction of the residual on O320, which is added to the interpolated coarse field to reconstruct the final high-resolution field.
• Figure 3: Receptive field of the GNN processor for a randomly selected node (red). The figure shows how many nodes receive its information after 2, 10, and 16 message-passing layers—that is, across the corresponding 2-, 10-, and 16-hop neighborhoods. The graph structure is fixed; only the range of information propagation increases.
• Figure 4: Fair Continuous Ranked Probability Score (FCRPS) for 2-metre temperature (left) and 10-metre wind speed (right) over the Northern Hemisphere extratropics, from forecast day 1 to 10 (20240201–20240225), evaluated against SYNOP station observations. Comparison between the downscaled ensemble forecast, the IFS medium-range ensemble forecast, and the subseasonal ensemble forecast (input).
• Figure 5: Subseasonal ensemble forecast first member (first column), medium-range ensemble forecast first member (second column), and downscaling with the diffusion model of two subseasonal members (third and fourth columns, with the third corresponding to downscaling of the first column). Results are shown for the Amazon rainforest at lead time 24h. Rows display 10u, 10v, 2t, and T850. The diffusion model generates stochastic and physically consistent small-scale features.
• Figure 6: Subseasonal ensemble forecast first member (first column), medium-range ensemble forecast first member (second column), and downscaling with the diffusion model of two subseasonal members (third and fourth columns, with the third corresponding to downscaling of the first column). Results are shown for the Himalayas at lead time 24h. Rows display 10u, 10v, 2t, and T850. The diffusion model generates physically consistent small-scale features.