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Machine learning models show varied performance in emulating stratospheric warming events

By PulseAugur Editorial · [2 sources] ·

Researchers have investigated how different machine learning architectures impact the emulation of sudden stratospheric warming (SSW) events. Using idealised Isca simulations, they found that while convolutional, transformer, and graph-based models performed similarly during quiet stratospheric periods, their performance diverged significantly when SSW-like variability was present. The study highlights explicit three-dimensional vertical coupling as a crucial inductive bias for accurately emulating stratospheric dynamics, though it also notes that low forecast error does not always equate to physically accurate wave-mean-flow interactions. AI

IMPACT This research could lead to more accurate weather prediction models by improving the emulation of complex atmospheric phenomena.

RANK_REASON The cluster contains an academic paper detailing research findings.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Machine learning models show varied performance in emulating stratospheric warming events

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Oskar Bohn Lassen, Simon Driscoll, Stephen I. Thomson, Sebastian Schemm, Francisco C. Pereira ·

    Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations

    arXiv:2606.18857v1 Announce Type: new Abstract: Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically important sources of predictability. A key challenge is whe…

  2. arXiv cs.LG TIER_1 English(EN) · Francisco C. Pereira ·

    Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations

    Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically important sources of predictability. A key challenge is whether the models can exploit predictability ancho…

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