FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting
arxiv(2024)
Abstract
Ensemble forecasting is crucial for improving weather predictions, especially
for forecasts of extreme events. Constructing an ensemble prediction system
(EPS) based on conventional NWP models is highly computationally expensive. ML
models have emerged as valuable tools for deterministic weather forecasts,
providing forecasts with significantly reduced computational requirements and
even surpassing the forecast performance of traditional NWP models. However,
challenges arise when applying ML models to ensemble forecasting. Recent ML
models, such as GenCast and SEEDS model, rely on the ERA5 EDA or operational
NWP ensemble members for forecast generation. Their spatial resolution is also
considered too coarse for many applications. To overcome these limitations, we
introduce FuXi-ENS, an advanced ML model designed to deliver 6-hourly global
ensemble weather forecasts up to 15 days. This model runs at a significantly
increased spatial resolution of 0.25°, incorporating 5 atmospheric
variables at 13 pressure levels, along with 13 surface variables. By leveraging
the inherent probabilistic nature of Variational AutoEncoder (VAE), FuXi-ENS
optimizes a loss function that combines the CRPS and the KL divergence between
the predicted and target distribution, facilitating the incorporation of
flow-dependent perturbations in both initial conditions and forecast. This
innovative approach makes FuXi-ENS an advancement over the traditional ones
that use L1 loss combined with the KL loss in standard VAE models for ensemble
weather forecasting. Results demonstrate that FuXi-ENS outperforms ensemble
forecasts from the ECMWF, a world leading NWP model, in the CRPS of 98.1
360 variable and forecast lead time combinations. This achievement underscores
the potential of the FuXi-ENS model to enhance ensemble weather forecasts,
offering a promising direction for further development in this field.
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