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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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
CRPS-trained ensembles achieve better uncertainty reliability and speed than latent generative models for probabilistic emulation of 2D physical systems.
citing papers explorer
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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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Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
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Reliability of Probabilistic Emulation of Physical Systems
CRPS-trained ensembles achieve better uncertainty reliability and speed than latent generative models for probabilistic emulation of 2D physical systems.