Huge ensembles part I design of ensemble weather forecasts with spherical Fourier neural operators; Huge ensembles part II properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators

Studying low-likelihood high-impact extreme weather events in a warming
world is a significant and challenging task for current ensemble
forecasting systems. While these systems presently use up to 100 members,
larger ensembles could enrich the sampling of internal variability. They
may capture the long tails associated with climate hazards better than
traditional ensemble sizes. Due to computational constraints, it is
infeasible to generate huge ensembles (comprised of 1,000-10,000 members)
with traditional, physics-based numerical models. In this two-part paper,
we replace traditional numerical simulations with machine learning (ML) to
generate hindcasts of huge ensembles. In Part I, we construct an ensemble
weather forecasting system based on Spherical Fourier Neural Operators
(SFNO), and we discuss important design decisions for constructing such an
ensemble. The ensemble represents model uncertainty through
perturbed-parameter techniques, and it represents initial condition
uncertainty through bred vectors, which sample the fastest-growing modes
of the forecast. Using the European Centre for Medium-Range Weather
Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an
evaluation pipeline composed of mean, spectral, and extreme diagnostics.
Using large-scale, distributed SFNOs with 1.1 billion learned parameters,
we achieve calibrated probabilistic forecasts. As the trajectories of the
individual members diverge, the ML ensemble mean spectra degrade with lead
time, consistent with physical expectations. However, the individual
ensemble members' spectra stay constant with lead time. Therefore,
these members simulate realistic weather states, and the ML ensemble thus
passes a crucial spectral test in the literature. The IFS and ML ensembles
have similar Extreme Forecast Indices, and we show that the ML extreme
weather forecasts are reliable and discriminating. In Part I, we created
an ensemble based on Spherical Fourier Neural Operators. As initial
condition perturbations, we used bred vectors, and as model perturbations,
we used multiple checkpoints trained independently from scratch. Based on
diagnostics that assess the ensemble’s physical fidelity, our ensemble has
comparable performance to operational weather forecasting systems.
However, it requires several orders of magnitude fewer computational
resources. Here in Part II, we generate a huge ensemble (HENS), with 7,424
members initialized each day of summer 2023. We enumerate the technical
requirements for running huge ensembles at this scale. HENS precisely
samples the tails of the forecast distribution and presents a detailed
sampling of internal variability. For extreme climate statistics, HENS
samples events 4σ away from the ensemble mean. At each grid cell, HENS
improves the skill of the most accurate ensemble member and enhances
coverage of possible future trajectories. As a weather forecasting model,
HENS issues extreme weather forecasts with better uncertainty
quantification. It also reduces the probability of outlier events, in
which the verification value lies outside the ensemble forecast
distribution.