3D radiative heating of tropical upper tropospheric cloud systems derived from synergistic A-Train observations and machine learning

Stubenrauch, Claudia J.; Caria, Giacomo; Protopapadaki, Sofia E.; Hemmer, Friederike

doi:https://doi.org/10.5194/acp-21-1015-2021

Articles | Volume 21, issue 2

https://doi.org/10.5194/acp-21-1015-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-21-1015-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 2

Research article

|

26 Jan 2021

Research article |

| 26 Jan 2021

3D radiative heating of tropical upper tropospheric cloud systems derived from synergistic A-Train observations and machine learning

Claudia J. Stubenrauch, Giacomo Caria, Sofia E. Protopapadaki, and Friederike Hemmer

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Final revised paper (published on 26 Jan 2021)
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Preprint (discussion started on 04 Aug 2020)
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Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Referee Review of Stubenrauch et al 2020', Anonymous Referee #1, 08 Sep 2020
- AC1: 'Response to reviewer #1', Claudia Stubenrauch, 19 Nov 2020
RC2: '3D Radiative Heating of Tropical Upper Tropospheric Cloud Systems derived from Synergistic A-Train Observations and Machine Learning', Anonymous Referee #2, 03 Nov 2020
- AC2: 'Response to reviewer #2', Claudia Stubenrauch, 19 Nov 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Claudia Stubenrauch on behalf of the Authors (19 Nov 2020) Author's response Manuscript

ED: Publish as is (24 Nov 2020) by Manish Shrivastava

AR by Claudia Stubenrauch on behalf of the Authors (24 Nov 2020) Manuscript

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Short summary

Tropical anvils formed by convective outflow play a crucial role in modulating the Earth’s energy budget and heat transport. To explore the relation between these anvils and convection, we built 3D radiative heating fields, based on machine learning employed on cloud and atmospheric properties from IR sounder and meteorological reanalyses, trained on lidar–radar retrievals. The 15-year time series reveals colder convective systems during warm periods, affecting the atmospheric heating structure.