Reconstructing Aerosols Vertical Profiles with Aggregate Output Learning

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Authors

Sofija Stefanovic, Shahine Bouabid, Philip Stier, Athanasios Nenes, Dino Sejdinovic

Abstract

Aerosol-cloud interactions constitute the largest source of uncertainty in assessments of 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 an aggregate output regression problem and demonstrate their potential on climate model data.

DOI

https://doi.org/10.31223/X5QW5S

Subjects

Physical Sciences and Mathematics

Keywords

Aerosols, , ICML, Aerosols, Vertical Profiles, Optical Depth, Gaussian Processes, Aggregate Learning, Kernel Mean Embeddings, Variational Inference, ICML, Vertical Profiles, Optical Depth, Aggregate Learning

Dates

Published: 2021-07-02 10:58

Last Updated: 2021-07-02 13:58

License

CC BY Attribution 4.0 International

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