Unleashing the Autoconversion Rates Forecasting: Evidential Regression from Satellite Data

High-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM) can be used to study the interactions between aerosols, clouds, and precipitation processes that currently represent the largest source of uncertainty involved in climate change projections. However, due to significant computational costs, it can only be employed for a limited period and area. While machine learning mitigates this, model uncertainties may affect reliability. To address this, we developed a neural network (NN) model powered with evidential learning to assess the data and model uncertainties. Our study focuses on estimating the rate at which small droplets (cloud droplets) collide and coalesce to become larger droplets (raindrops) – autoconversion rates -- as the key process in the precipitation formation, crucial to better understanding cloud responses to anthropogenic aerosols. The results show that the model performs reasonably well, with the inclusion of both aleatoric and epistemic uncertainty estimation, which improves the credibility of the model and provides useful insights for future improvement.