Novitasari, Maria Carolina;
Quaas, Johannes;
Rodrigues, Miguel RD;
(2024)
Cloudy with a chance of uncertainty: autoconversion rates
forecasting via evidential regression from satellite data.
[Corrigendum].
Environmental Data Science
, 3
, Article e45. 10.1017/eds.2024.37.
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Abstract
High-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM) can be used to understand the interactions among aerosols, clouds, and precipitation processes that currently represent the largest source of uncertainty involved in determining the radiative forcing of climate change. Nevertheless, due to the exceptionally high computing cost required, this simulation-based approach can only be employed for a short period within a limited area. Despite the potential of machine learning to alleviate this issue, the associated model and data uncertainties may impact its reliability. To address this, we developed a neural network (NN) model powered by evidential learning, which is easy to implement, to assess both data (aleatoric) and model (epistemic) uncertainties applied to satellite observation data. By differentiating whether uncertainties stem from data or the model, we can adapt our strategies accordingly. Our study focuses on estimating the autoconversion rates, a process in which small droplets (cloud droplets) collide and coalesce to become larger droplets (raindrops). This process is one of the key contributors to the precipitation formation of liquid clouds, crucial for a better understanding of cloud responses to anthropogenic aerosols and, subsequently, climate change. We demonstrate that incorporating evidential regression enhances the model’s credibility by accounting for uncertainties without compromising performance or requiring additional training or inference. Additionally, the uncertainty estimation shows good calibration and provides valuable insights for future enhancements, potentially encouraging more open discussions and exploration, especially in the field of atmospheric science.
| Type: | Article |
|---|---|
| Title: | Cloudy with a chance of uncertainty: autoconversion rates forecasting via evidential regression from satellite data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1017/eds.2024.37 |
| Publisher version: | https://doi.org/10.1017/eds.2024.37 |
| Language: | English |
| Additional information: | Copyright © The Author(s), 2024. Published by Cambridge University Press. 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; evidential regression; uncertainty quantification; aerosol-cloud interaction; precipitation formation |
| 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/10204535 |
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