Calibrated conformal prediction intervals for microphysical process rates

Abstract

Conformal prediction (CP) can yield statistically valid prediction intervals for any regression model, with no model modifications and small computational costs. To assess its practical value, we apply conformal methods to quantify uncertainty in machine learning emulators of six microphysical process rates (MPRs). MPRs describe small-scale processes in atmospheric clouds such as precipitation formation and aerosol–cloud interactions and help understand weather and climate. The emulators are trained on simulation output from the ICOsahedral Nonhydrostatic (ICON) model in a limited-area numerical weather prediction configuration. We compare split CP for deterministic emulators with conformalized quantile regression (CQR) for quantile regression (QR) emulators. Both CP methods yield well-calibrated and sharp prediction intervals on average, but CQR provides more consistent intervals across several orders of magnitude, making it preferable for the uncertainty quantification of climate variables.