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Keyword: inertia-gravity waves ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves

   Haslauer, Schwabe, Dörnbrack et al. · arXiv (Cornell University) · 2026

   Neural networks trained on ERA5 reanalysis data predicted momentum fluxes of mountain-generated gravity waves at coarse climate-model resolution, and an explainable-AI analysis suggests they learned physically meaningful relationships.

   Unchecked2 claims
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   1. UncheckedNeural networks predicted orographic gravity-wave momentum fluxes with global R² of 0.56 to 0.72 when tested offline on data from another year.“Our NNs successfully predict momentum fluxes, with a global coefficient of determination ($R^2$) ranging from 0.72 to 0.56, depending on the case, when evaluated offline with data from another year.”
   2. UncheckedAnalysis with SHAP values suggests the neural networks learned physically meaningful relationships when predicting momentum fluxes of orographic gravity waves.“An analysis of our models using SHAP values, an explainable AI technique, suggests that the networks learned physically meaningful relationships.”