Machine Learning Calibration of Cloud Parameterization in a Numerical Weather Prediction Model

Cloud parameterization introduces uncertainty in numerical weather prediction (NWP), partly arising from the “tunable parameters”. However, the selection of the parameter values, namely calibration, has long been criticized for its arbitrariness, its tendency to induce error compensation, and its high computational cost. The development of machine learning (ML) methods in geoscientific research offers new tools to improve traditional calibration approach. Here, we propose a new framework for the objective calibration of cloud parameterization in the state-of-art ICON-NWP model. A trained machine learning model based on Gaussian Process Regression (GPR) will serve as a surrogate model for the numerical model, which allows sufficiently large ensembles under limited computation resources. History matching is adopted to quantify a plausible range for parameter values. We expect this framework to reveal the spatiotemporal distribution and cloud-regime dependency of paramters, which will provide us with a new insight into cloud parameterization and the underlying physics. In the future, we will further analyse the calibration results, especially regarding its impacts on aerosol-cloud radiative forcing and cloud–climate feedback.