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
Giulio Mandorli
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.
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Claudia J. Stubenrauch et al.
Status: final response (author comments only)
- RC1: 'Comment on acp-2022-753', Anonymous Referee #1, 08 Dec 2022
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RC2: 'Comment on acp-2022-753', Anonymous Referee #2, 15 Jan 2023
Review of “Convective Organization and 3D Structure of Tropical Cloud Systems deduced from Synergistic A-Train Observations and Machine Learning” by C. Stubenrauch et al. (Submitted to ACP)
Summary and Recommendation:
This study aims to study convective organization and dynamics of upper tropospheric clouds over the global tropics using satellite datasets and ERA reanalysis products. The authors apply novel machine learning method to merge these datasets in order to fill in the gap in these dataset to obtain a fully-connected understanding of tropical convective systems. Overall, I commend the authors for writing such a comprehensive manuscript and I really liked the scientific analyses presented here. I specifically liked the authors efforts to connect dots between convection, cloud systems, and organization using multiple metrics so as to provide a better idea of tropical convection. However, I think that manuscript needs some restructring as currently it looks like a lot of ingredients are mixed together but it didnt result in an edible dish. I got lost and a bit confused in section 3 as the authors jump between multiple thoughts and I couldnt connect the dots well. Therefore, I recommend major revision to the manuscript.
Comments:
1) First, some figures are highly pixelated (Figures 3, 7, 10, 11) and therefore I recommend providing high resloution version of all the figures.
2) I was wondering that whether the authors performed any tests regarding the variables of interest? For e.g., did the authors test other atmospheric state variables to predict the cloud properties? If not, can the authors comment on how they came up with these input/output variables. It would also be useful if the authors can comment on relative importance of each input variables, if possible. This will aid the readers to choose which variables are more important and significant for cloud properties and rain classification.
3) I suggest adding a schematic of the designed ANN as it would be easier to visualize the connected network and their hidden layers.
4) Previous studies (de Szoeke et al. 2017, Garg et al., 2020) have shown that cold pools have a strong relationship with tropical convection and specially with precipitation and convective areas. I highly recommend connecting these results with global tropical oceanic cold pool properties shown in these studies as it will help the authors connect dots between clouds, precipipitation and convection over the global tropics.
5) Line 390: Correct "MSCs" to "MCSs".
6) I suggest correcting TB to TB throughout the manuscript.
7) I recommend summarizing the conclusions section in some take-away points so that it better concludes and summarize your major points. Currently the conclusions section is also not linked at all.
8) In the Code/Data availability, the authors have provided links to the relevant datasets but I am just wondering if the authors intend to provide open source code of their ANN as well?
- 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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