Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
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A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
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PRecover 1.0: Process Rate Recovery with Machine Learning
Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
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Bounding Global and Local Compression Error of Signal Parameterizations
A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.