16 Nov 2022
 | 16 Nov 2022
Status: a revised version of this preprint is currently under review for the journal ACP.

Convective Organization and 3D Structure of Tropical Cloud Systems deduced from Synergistic A-Train Observations and Machine Learning

Claudia J. Stubenrauch, Giulio Mandorli, and Elizabeth Lemaitre

Abstract. We are building a 3D description of upper tropospheric (UT) cloud systems in order to study the relation between convection and cirrus anvils. For this purpose we used cloud data from the Atmospheric InfraRed Sounder and the Infrared Atmospheric Sounding Inferometer and atmospheric and surface properties from the meteorological reanalyses ERA-Interim and machine learning techniques. The different artificial neural network models were trained on collocated radar – lidar data from the A-Train in order to add cloud top height, cloud vertical extent, cloud layering, as well as a rain intensity classification (no, light or heavy) to other variables describing the UT cloud systems. The rain intensity classification has an accuracy of about 65 to 70 % and allows to build objects of strong precipitation, used to identify convective organization. This classification is more efficient to detect large latent heating compared to cold cloud temperature. The cloud system concept allows a process-oriented evaluation of parameterizations in climate models. In agreement with earlier studies, we found that the rain intensity is maximum after the first development of anvils and that deeper convection leads to larger heavy rain areas and a larger detrainment. Finally we have shown the usefulness of our data to investigate tropical convective organization. A comparison of different tropical convective organization indices and proxies to define convective areas has revealed that all indices show a similar annual cycle in convective organization, in phase with the one of convective core height, anvil vertical extent, and horizontal detrainment of the mesoscale convective systems and in opposite phase with the one of the ratio of thin cirrus over total anvil size. Differences can be understood by seasonal cycles of size and number of areas in phase for intense precipitation and opposite phase for cold clouds as proxies. The geographical patterns and magnitudes in radiative heating rate inter-annual changes with respect to one specific convective organization index (Iorg) for the period 2008 to 2018 are similar for both proxies, but slightly larger for rain intensity, and they are similar to the ones related to the El Niño Southern Oscillation. However, the time series of the inter-annual anomalies of convective organization depend on the convective organization index.

Claudia J. Stubenrauch et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-753', Anonymous Referee #1, 08 Dec 2022
  • RC2: 'Comment on acp-2022-753', Anonymous Referee #2, 15 Jan 2023
  • AC1: 'Author response to Referees on acp-2022-753', Claudia Stubenrauch, 18 Feb 2023

Claudia J. Stubenrauch et al.

Claudia J. Stubenrauch et al.


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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.