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Leveraging Machine Learning to Predict the Autoconversion Rates from Satellite Data

Novitasari, Maria; Quaas, Johannes; Rodrigues, Miguel; (2021) Leveraging Machine Learning to Predict the Autoconversion Rates from Satellite Data. In: Proceedings of the Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021. NeurIPS Green open access

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Abstract

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.

Type: Proceedings paper
Title: Leveraging Machine Learning to Predict the Autoconversion Rates from Satellite Data
Event: NeurIPS 2021 Workshop: Tackling Climate Change with Machine Learning
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.climatechange.ai/papers/neurips2021/59
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10184526
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