Leveraging 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)

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One way of reducing the uncertainty involved in determining the radiative forcing of climate change is by understanding the interaction between aerosols, clouds, and precipitation processes. This 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 simulation-based approach can only be run for a limited amount of time within a limited area. To address this, we developed new models using emerging machine learning approaches that leverage a plethora of satellite observations providing long-term global spatial coverage. In particular, our machine learning models are 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 simulation data, showing that our models are capable of predicting the autoconversion rates fairly well.

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