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A Large‐Scale Analysis of Pockets of Open Cells and Their Radiative Impact
Author(s) -
WatsonParris D.,
Sutherland S. A.,
Christensen M. W.,
Eastman R.,
Stier P.
Publication year - 2021
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl092213
Subject(s) - radiative transfer , cloud computing , cloud fraction , environmental science , convolutional neural network , scale (ratio) , remote sensing , meteorology , atmosphere (unit) , computer science , satellite , artificial neural network , data set , perturbation (astronomy) , atmospheric sciences , geology , physics , cloud cover , cartography , geography , machine learning , optics , artificial intelligence , astronomy , operating system
Pockets of open cells sometimes form within closed‐cell stratocumulus cloud decks but little is known about their statistical properties or prevalence. A convolutional neural network was used to detect occurrences of pockets of open cells (POCs). Trained on a small hand‐logged data set and applied to 13 years of satellite imagery the neural network is able to classify 8,491 POCs. This extensive database allows the first robust analysis of the spatial and temporal prevalence of these phenomena, as well as a detailed analysis of their micro‐physical properties. We find a large (30%) increase in cloud effective radius inside POCs as compared to their surroundings and similarly large (20%) decrease in cloud fraction. This also allows their global radiative effect to be determined. Using simple radiative approximations we find that the instantaneous global annual mean top‐of‐atmosphere perturbation by all POCs is only 0.01 W/m 2 .