Development and tests of machine-learned cloud droplet number concentration in a next-generation bulk microphysics scheme for operational applications

Abstract. A machine-learning (ML) model was developed to predict cloud droplet number concentrations. This ML model was trained on data from simulations using the Model for Prediction Across Scales–Atmosphere (MPAS-A) coupled to a next-generation version of the Thompson-Eidhammer bulk microphysics scheme. The ML model was tested for two applications. First, the ML model was used to predict the number concentration, and hence the effective radii of subgrid-scale clouds from the MYNN boundary-layer scheme. These subgrid-scale clouds, necessary for accurate predictions of radiation, have historically been parameterized using constant values for their effective radii. The ML model predicted cloud droplet number concentrations for subgrid-scale clouds so that their effective radii could be calculated using a method physically consistent with those of fully resolved clouds predicted by the bulk microphysics scheme. These results were compared with observations using a novel approach to show that the ML model provided reasonable estimates of effective radii. Second, cloud droplet number concentrations were replaced in the bulk microphysics parameterization with values predicted by the ML model. Results from this test were compared with aircraft and surface observations from two IOPs during the WINTRE-MIX field campaign. Overall, the simulation with the ML prediction of cloud droplet number concentrations reproduced the 24-h total precipitation for the case and reasonably represented the microphysical details. The ML model performed better for relatively short forecasts, which was attributed to the lack of model spin-up required by physical parameterizations. The biases in the ML model for this case study resulted in reduced drizzle production, which slightly degraded the forecast of precipitation phase. Benefits of the ML method include lower computational cost, better coupling between fully resolved and unresolved (subgrid-scale) clouds, and more rapid development of realistic forecasts when initial conditions are not fully spun up.