Calibration of HRRRCast ensemble spread

Develop and validate a method to calibrate the ensemble spread of HRRRCast’s diffusion-based probabilistic forecasts so that the spread realistically represents forecast uncertainty (e.g., achieving a spread-error ratio near unity) across variables and lead times.

Background

HRRRCast generates ensembles via DDIM-based diffusion, currently perturbed only by initial Gaussian noise. In Appendix A, the authors report an average spread-error ratio of about 0.6, indicating under-dispersion.

The conclusions explicitly note ensemble spread calibration as an open challenge and suggest potential improvements, such as incorporating GEFS-based initial condition perturbations and adding stochastic noise during sampling.