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Status: Unchecked ×Keyword: predictability limits ×Clear all

6 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis

   Vonich and Hakim · arXiv (Cornell University) · 2024

   The authors used deep-learning gradient methods to tune initial conditions and reduce forecast errors for the 2021 Pacific Northwest heatwave, finding improvement out to about 23 days.

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   1. UncheckedOptimising starting conditions in the GraphCast model cut 10-day forecast errors over the Pacific Northwest by over 90% for the June 2021 heatwave.“We apply this approach to forecasts of the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10-day forecast errors over the Pacific Northwest.”
   2. Unchecked“Similar improvements are found for Pangu-Weather model forecasts initialized with the GraphCast-derived optimal, suggesting that model error is not an important part of the initial perturbations.”
   3. UncheckedLengthening the optimisation window, the authors find the heatwave forecast can be improved out to about 23 days, at the upper end of recent predictability estimates.“Extending the length of the optimization window, we find forecast improvement to about 23 days, suggesting atmospheric predictability at the upper end of recent estimates.”

2. Physics and Astronomy › Model Reduction and Neural Networks

   Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study

   Soldatenko and Angudovich · Climate · 2024

   The paper tests unidirectional and bidirectional LSTM networks as surrogate models for a coupled fast–slow Lorenz system standing in for the atmosphere and ocean, and examines forecast accuracy and predictability limits.

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   1. UncheckedIn this study, bidirectional LSTM neural networks forecast a chaotic coupled fast–slow system more accurately than unidirectional LSTM networks.“The findings revealed the advantages of bidirectional neural networks over unidirectional ones in terms of forecasting accuracy.”
   2. UncheckedIn this study, with other settings held equal, giving a neural network more neurons per hidden layer tended to make its forecasts more accurate.“All other things being equal, increasing the number of neurons in hidden layers facilitates the improvement of forecast accuracy.”
   3. UncheckedIn a coupled fast–slow Lorenz model, the fast 'atmosphere' part stays predictable for somewhat longer than its Lyapunov time.“The predictability limit of the fast subsystem (“atmosphere”) is somewhat greater than the Lyapunov time.”