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2 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON

   Helge, Schwabe, Gentine, Giorgetta and Eyring · arXiv (Cornell University) · 2023

   The authors separated convection from other small-scale processes in ICON simulations, compared machine learning models offline, then coupled them online, finding an ablated U-Net ran stably for 180 days.

   Unchecked2 claims
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   1. UncheckedAn unablated U-Net, the best offline performer, learned reverse causal links between convective precipitation and subgrid fluxes, per the paper.“We discover that an unablated U-Net, while showing the best offline performance, learns reverse causal relations between convective precipitation and subgrid fluxes.”
   2. UncheckedWhen coupled to the ICON climate model, a U-Net with precipitating tracers removed was more stable than the full U-Net and ran for the full 180 days.“Online, the ablated U-Net significantly improves stability compared to the non-ablated U-Net and runs stable for the full simulation period of 180 days.”