SEETO achieves 6% better hypervolume in NWP parameter calibration with only 20 evaluations by using meteorological state representations for bi-level knowledge transfer from similar past tasks.
Fixing the double penalty in data-driven weather forecasting through a modified spherical harmonic loss function,
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A typology of blended ML and physics-based modeling approaches for weather and climate is presented to support informed decision-making in prediction systems.
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Efficient Parameter Calibration of Numerical Weather Prediction Models via Evolutionary Sequential Transfer Optimization
SEETO achieves 6% better hypervolume in NWP parameter calibration with only 20 evaluations by using meteorological state representations for bi-level knowledge transfer from similar past tasks.