Novitasari, Maria Carolina;
Quaas, Johannes;
Rodrigues, Miguel RD;
(2024)
Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning.
Environmental Data Science
, 3
, Article e23. 10.1017/eds.2024.24.
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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 biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach involves two phases: (1) we develop machine learning models which are capable of predicting autoconversion rates by leveraging high-resolution climate model data, (2) we repurpose our best machine learning model to predict autoconversion rates directly from satellite observations. We compare the performance of our machine learning models against simulation data under several different conditions, showing from both visual and statistical inspections that our approaches are able to identify key features of the reference simulation data to a high degree. Additionally, the autoconversion rates obtained from the simulation output and satellite data (predicted) 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.
| Type: | Article |
|---|---|
| Title: | Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1017/eds.2024.24 |
| Publisher version: | https://doi.org/10.1017/eds.2024.24 |
| Language: | English |
| Additional information: | This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
| Keywords: | Autoconversion rates; aerosol-cloud interactions; machine learning; precipitation formation; remote sensing |
| 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-pp.ucl.ac.uk/id/eprint/10200116 |
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