Reconstructing Aerosol Vertical Profiles with Aggregate Output Learning

2021 International Conference on Machine Learning

Aerosol-cloud interactions constitute the largest source of uncertainty in assessments of the anthropogenic climate change. This uncertainty arises in part from the inability to observe aerosol amounts at the cloud formation levels, and, more broadly, the vertical distribution of aerosols. Hence, we often have to settle for less informative two-dimensional proxies, i.e. vertically aggregated data. In this work, we formulate the problem of disaggregation of vertical profiles of aerosols. We propose some initial solutions for such aggregate output regression problem and demonstrate their potential on climate model data.

https://www.climatechange.ai/papers/icml2021/16

How to cite: Stefanovic, S., Bouabid, S., Stier, P., Nenes, A., & Sejdinovic, D. (2021). Reconstructing Aerosol Vertical Profiles with Aggregate Output Learning. ICML 2021 Workshop on Tackling Climate Change with Machine Learning. https://www.climatechange.ai/papers/icml2021/16