5605067
doi
10.5281/zenodo.5605067
oai:zenodo.org:5605067
Bender, Frida
Stockholm University, Stockholm, Sweden
Using deep learning cloud classification in cloud feedback and climate sensitivity determination
Kuma, Peter
Stockholm University, Stockholm, Sweden
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>We develop a deep convolutional neural network for determination of cloud types in low-resolution daily mean top-of-atmosphere shortwave and longwave radiation images, corresponding to the classical cloud types recorded by human observers in the Global Telecommunication System. We train this network on the CERES top of atmosphere radiation dataset, and apply this network on the CMIP6 abrupt-4xCO2 model output to determine long-term change in cloud type occurrence in these models with increasing CO2 concentration. We contrast these results with corresponding cloud type change in historical satellite measurements. The proposed neural network approach is broadly applicable for model, reanalysis and satellite imagery evaluation because it does not require high resolution and corresponds to the cloud types commonly recorded at weather stations worldwide.</p>
Zenodo
2021-10-27
info:eu-repo/semantics/conferencePoster
5605066
1702633905.588607
5341430
md5:ecbcb6c7d5d454b0a4e7dd193d821cc6
https://zenodo.org/records/5605067/files/Kuma and Bender (2021), Using deep learning cloud classification in cloud feedback and climate sensitivity determination.pdf
public
10.5281/zenodo.5605066
isVersionOf
doi