Leverage Machine Learning to Predict the Autoconversion Rates from Satellite Data (Papers Track)
Maria C Novitasari (University College London); Johannes Quaas (University of Leipzig); Miguel Rodrigues (University College London)
One way of reducing the uncertainty involved in determining the radiative forcing of the Earth is by understanding the interaction between aerosols, clouds, and precipitation processes, which can be studied using high-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM). However, due to the extremely high computational cost required, this model can only be run for a limited amount of time within a limited area. To address this, we developed models using emerging machine learning approaches that leverage a plethora of satellite observations which provide long-term global spatial coverage. In particular, we developed machine learning models capable of capturing the key process of precipitation formation which greatly control cloud lifetime, namely autoconversion rates – the term used to describe the collision and coalescence of cloud droplets responsible for raindrop formation. We validate the performance of our models against the simulation data, and the results showed that our models are capable of predicting the autoconversion rate fairly well, with the best model (DNN) achieving an SSIM index of 96.80%.