16–19 May 2022
Utrecht
Europe/Amsterdam timezone

Dependency of precipitation and cloud radiative feedback on subgrid scale clouds structure; a machine learning approach

18 May 2022, 11:30
15m
Public Library (Utrecht)

Public Library

Utrecht

Neude 11, 3512 AE Utrecht, the Netherlands

Speaker

Sara Shamekh (Columbia University)

Description

The organization of convective clouds has been argued to have a significant impact on the atmospheric humidity and circulation, as well as precipitation and cloud radiative feedback, yet global models do not include a parameterization of unresolved subgrid cloud structure. In this study, we investigate the necessity of including subgrid scale cloud structure in the model and whether it improves the prediction of precipitation and cloud radiative feedback. We use neural networks (NN), trained on coarse-grained data from the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison project, to learn about the dependency of precipitation, cloud cover and cloud radiative feedback on the cloud structure. Our preliminary results suggest that NNs accurately predict the mean precipitation and cloud cover by only having access to resolved state variables, yet this prediction can be improved by adding information about the subgrid structure or its previous state. Using explainable artificial intelligence, we seek to understand the underlying dynamics discovered by NNs so as to improve the conventional parameterizations, such that they include information about subgrid-scale cloud structure when necessary.

Primary author

Sara Shamekh (Columbia University)

Co-authors

Yu Huang (Columbia Univeristy) Kara Lamb (Columbia University) Pierre Gentine (Columbia University)

Presentation materials

There are no materials yet.