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Status: Unchecked ×Keyword: radiation parameterization ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Navigating the Noise: Bringing Clarity to ML Parameterization Design With O(100) Ensembles

   Lin, Yu, Peng et al. · Journal of Advances in Modeling Earth Systems · 2025

   The authors built a pipeline for ensembles of around 100 hybrid climate simulations to test how machine-learning design choices affect online error and stability, finding offline gains do not always carry over.

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   1. Unchecked“For example, removing dropout and switching from a Mean Squared Error (MSE) to a Mean Absolute Error (MAE) loss both reduce offline error, but they have opposite effects on online error and online stability.”
   2. UncheckedEnsembles of around 100 hybrid simulations may be needed to reliably detect real online differences between machine-learning parameterization designs.“Finally, we show that ensemble sizes of $\mathcal{O}(100)$ may be necessary to reliably detect causally relevant differences online.”