Neural networks predict orographic gravity wave momentum fluxes from coarse state variables with offline R² of 0.56-0.72, learn physically meaningful relationships via SHAP, and are compared to the Lott-Miller parameterization.
rep., World Meteorological Organization, 56 pp
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WP-MIP creates a centralized forecast database and evaluation framework to compare physically based, machine-learning, and hybrid weather prediction models across global centers.
citing papers explorer
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Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves
Neural networks predict orographic gravity wave momentum fluxes from coarse state variables with offline R² of 0.56-0.72, learn physically meaningful relationships via SHAP, and are compared to the Lott-Miller parameterization.
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Hyperlocal urban NO2 hotspot modeling driven by microscopic traffic data
Dynamic traffic emissions from an online-calibrated SUMO model improve hyperlocal NO2 hotspot predictions and peak representation over static baselines when coupled to the CAIRDIO dispersion model.
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WP-MIP: An Artificial Intelligence, Hybrid and Physically Based Model Intercomparison Project for Weather Prediction
WP-MIP creates a centralized forecast database and evaluation framework to compare physically based, machine-learning, and hybrid weather prediction models across global centers.