Novitasari, Maria Carolina;
(2025)
Cloudy with a Chance of Precision Series: Satellite's Autoconversion Rates Forecasting Powered by Machine Learning.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Precipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the largest uncertainties in determining the radiative forcing of climate change. High-resolution simulations, such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM), provide valuable insights into the complex interactions among aerosols, clouds, and precipitation. However, due to their exorbitant computational costs, they can only be employed for a limited period and geographical area. To address this, the thesis proposes more cost-effective novel methods powered by emerging machine learning approaches and abundance of satellite data -- which provides long-term global spatial coverage for over two decades -- to better understand the intricate dynamics of the climate system, via prediction of autoconversion rates — this is the process by which small cloud droplets collide and coalesce, becoming larger droplets (raindrops). The thesis makes various contributions: 1) First, we develop various machine learning models to unravel the key process of precipitation formation for liquid clouds, the autoconversion, directly from satellite observations. 2) Second, we also incorporate evidential regression into our machine learning models in order to capture uncertainty in the predictions, ensuring trustworthiness. 3) Finally, we utilize active learning to reduce the number of labeled data. We compare the performance of our machine learning models against simulation data under different conditions, showing from both visual and statistical inspections that our approaches are able to identify key features of groundtruth to a high degree. Additionally, the autoconversion rates obtained from the simulation output and satellite data demonstrate statistical concordance. By efficiently predicting this, we advance our comprehension of one of the key processes in precipitation formation, crucial for understanding cloud responses to anthropogenic aerosols and, ultimately, climate change. Overall, our various contributions lead to new approaches that are simple, meaningful, and easy to implement in practice while reducing the overall costs associated with the simulation of climate processes.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Cloudy with a Chance of Precision Series: Satellite's Autoconversion Rates Forecasting Powered by Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS 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/10203100 |
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