Articles | Volume 23, issue 10
https://doi.org/10.5194/acp-23-5867-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-5867-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Convective organization and 3D structure of tropical cloud systems deduced from synergistic A-Train observations and machine learning
Claudia J. Stubenrauch
CORRESPONDING AUTHOR
Laboratoire de Météorologie Dynamique/Institut Pierre-Simon
Laplace, (LMD/IPSL), Sorbonne Université, Ecole Polytechnique, CNRS,
Paris, France
Giulio Mandorli
Laboratoire de Météorologie Dynamique/Institut Pierre-Simon
Laplace, (LMD/IPSL), Sorbonne Université, Ecole Polytechnique, CNRS,
Paris, France
Elisabeth Lemaitre
Laboratoire de Météorologie Dynamique/Institut Pierre-Simon
Laplace, (LMD/IPSL), Sorbonne Université, Ecole Polytechnique, CNRS,
Paris, France
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Atmospheric heating gradients caused by tropical upper tropospheric ice clouds (cirrus) influence the atmospheric circulation which then affects patterns of precipitation. While their overall radiative effect leads to a strengthening of the Hadley circulation, semi-transparent cirrus weaken the circulation. This means that in a warmer climate changes in their optical depth may then counteract or enhance the weakened Hadley circulation.
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Atmospheric heating gradients caused by tropical upper tropospheric ice clouds (cirrus) influence the atmospheric circulation which then affects patterns of precipitation. While their overall radiative effect leads to a strengthening of the Hadley circulation, semi-transparent cirrus weaken the circulation. This means that in a warmer climate changes in their optical depth may then counteract or enhance the weakened Hadley circulation.
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Strongly precipitating mesoscale convective systems produce a large amount of diabatic heating of the atmosphere, influencing atmospheric circulation. Their complete 3D description, attained by machine learning techniques in combination with satellite observations, has enabled a detailed study of the relationship between latent and radiative heating in these cloud systems. Convective organization increases both the average and the vertical gradient of radiative effects of the mesoscale convective systems.
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
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Short summary
Organized convection leads to large convective cloud systems and intense rain and may change with a warming climate. Their complete 3D description, attained by machine learning techniques in combination with various satellite observations, together with a cloud system concept, link convection to anvil properties, while convective organization can be identified by the horizontal structure of intense rain.
Organized convection leads to large convective cloud systems and intense rain and may change...
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