Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
arxiv(2024)
摘要
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.
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