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Status: Unchecked ×Keyword: ICON model ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Interpretable Machine Learning‐Based Radiation Emulation for ICON

   Hafner, Iglesias‐Suarez, Shamekh et al. · Journal of Geophysical Research Machine Learning and Computation · 2025

   The study builds a machine-learning emulator of the radiation scheme in the ICON climate model, emulates heating rates accurately, and uses SHAP to interpret what the networks have learned.

   Unchecked3 claims
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   1. UncheckedAnalysis with Shapley Additive exPlanations indicates the trained radiation-emulating neural networks learned physically meaningful links between inputs and outputs.“Further, we analyze the trained neural networks using Shapley Additive exPlanations and confirm that the networks have learned physically meaningful relationships among the inputs and outputs.”
   2. UncheckedA multilayer perceptron, whose design is not based on radiation physics, tends to rely on spurious or less physically meaningful correlations when predicting.“In contrast, an architecture that is not inspired by the underlying physics, such as a multilayer perceptron, tends to rely on spurious or less physically meaningful correlations to make its predictions.”
   3. UncheckedIn the trained emulator, local temperature acts as a predictive input for longwave heating, in line with physical models of radiation.“It is worth noting that we observe that the local temperature is used as a predictive source for the longwave heating, consistent with physical models of radiation.”