Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models

Jonathan A Weyn, Dale Richard Durran, Rich Caruana, Nathaniel Cresswell‐Clay

Journal of Advances in Modeling Earth Systems 2021 article cited by 190

importance 7/10 partially solved Meteorological Phenomena and Simulations weather forecasting stage: research

Producing sub-seasonal (six-week) global weather forecasts with large ensembles is computationally prohibitive with traditional numerical models.

Longer-range forecasts improve preparedness for droughts, heatwaves, and storm planning.

A Deep Learning Weather Prediction (DLWP) model using CNNs on a cubed sphere grid recursively predicts six atmospheric variables, with ensemble spread from 32 randomly-trained model variants.

320-member six-week forecasts at 1.4° resolution in 3 minutes on a single GPU; reasonable 4.5-day deterministic forecast of Hurricane Irma; spontaneously generates tropical cyclones in one-year simulation.

Demonstrated computationally cheap sub-seasonal forecasting but does not forecast precipitation and abstract is truncated before final skill metric.

Venue Journal of Advances in Modeling Earth Systems
Published 2021
Work type article
Authors Jonathan A Weyn, Dale Richard Durran, Rich Caruana, Nathaniel Cresswell‐Clay
Citations 190 (OpenAlex, at import time)
References 31
Model or method DLWP (convolutional neural networks on cubed sphere grid)
Organization not stated in source
Primary source https://doi.org/10.1029/2021ms002502
Open access gold
Keywords deep learning, sub-seasonal forecasting, ensemble prediction, CNN, tropical cyclones
7 / 10

significant — thousands to millions affected

Sub-seasonal forecasting is a recognized global capability gap; abstract notes current systems have low skill at S2S timescales and offers 320-member six-week forecasts in minutes, a genuinely new cheap capability.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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