Articles | Volume 23, issue 10
https://doi.org/10.5194/acp-23-5867-2023
https://doi.org/10.5194/acp-23-5867-2023
Research article
 | 
26 May 2023
Research article |  | 26 May 2023

Convective organization and 3D structure of tropical cloud systems deduced from synergistic A-Train observations and machine learning

Claudia J. Stubenrauch, Giulio Mandorli, and Elisabeth Lemaitre

Related authors

Relationship between latent and radiative heating fields of Tropical cloud systems using synergistic satellite observations
Xiaoting Chen, Claudia J. Stubenrauch, and Giulio Mandorli
EGUsphere, https://doi.org/10.5194/egusphere-2024-3434,https://doi.org/10.5194/egusphere-2024-3434, 2024
Short summary
Assessment of object-based indices to identify convective organization
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024,https://doi.org/10.5194/gmd-17-7795-2024, 2024
Short summary
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
Atmos. Chem. Phys., 21, 1015–1034, https://doi.org/10.5194/acp-21-1015-2021,https://doi.org/10.5194/acp-21-1015-2021, 2021
Short summary
Diurnal variation of high-level clouds from the synergy of AIRS and IASI space-borne infrared sounders
Artem G. Feofilov and Claudia J. Stubenrauch
Atmos. Chem. Phys., 19, 13957–13972, https://doi.org/10.5194/acp-19-13957-2019,https://doi.org/10.5194/acp-19-13957-2019, 2019
Short summary
Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications
Claudia J. Stubenrauch, Artem G. Feofilov, Sofia E. Protopapadaki, and Raymond Armante
Atmos. Chem. Phys., 17, 13625–13644, https://doi.org/10.5194/acp-17-13625-2017,https://doi.org/10.5194/acp-17-13625-2017, 2017
Short summary

Related subject area

Subject: Clouds and Precipitation | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height
Mengyuan Wang, Min Min, Jun Li, Han Lin, Yongen Liang, Binlong Chen, Zhigang Yao, Na Xu, and Miao Zhang
Atmos. Chem. Phys., 24, 14239–14256, https://doi.org/10.5194/acp-24-14239-2024,https://doi.org/10.5194/acp-24-14239-2024, 2024
Short summary
Observing convective activities in complex convective organizations and their contributions to precipitation and anvil cloud amounts
Zhenquan Wang and Jian Yuan
Atmos. Chem. Phys., 24, 13811–13831, https://doi.org/10.5194/acp-24-13811-2024,https://doi.org/10.5194/acp-24-13811-2024, 2024
Short summary
Weak liquid water path response in ship tracks
Anna Tippett, Edward Gryspeerdt, Peter Manshausen, Philip Stier, and Tristan W. P. Smith
Atmos. Chem. Phys., 24, 13269–13283, https://doi.org/10.5194/acp-24-13269-2024,https://doi.org/10.5194/acp-24-13269-2024, 2024
Short summary
Lightning declines over shipping lanes following regulation of fuel sulfur emissions
Chris J. Wright, Joel A. Thornton, Lyatt Jaeglé, Yang Cao, Yannian Zhu, Jihu Liu, Randall Jones II, Robert H. Holzworth, Daniel Rosenfeld, Robert Wood, Peter Blossey, and Daehyun Kim
EGUsphere, https://doi.org/10.48550/arXiv.2408.07207,https://doi.org/10.48550/arXiv.2408.07207, 2024
Short summary
Air mass history linked to the development of Arctic mixed-phase clouds
Rebecca J. Murray-Watson and Edward Gryspeerdt
Atmos. Chem. Phys., 24, 11115–11132, https://doi.org/10.5194/acp-24-11115-2024,https://doi.org/10.5194/acp-24-11115-2024, 2024
Short summary

Cited articles

Berry, G. and Reeder, M. J.: Objective Identification of the Intertropical Convergence Zone: Climatology and Trends from the ERA-Interim, J. Clim., 27, 1894–1909, https://doi.org/10.1175/JCLI-D-13-00339.1, 2014. 
Bläckberg, C. P. O. and Singh, M. S.: Increased Large-Scale Convective Aggregation in CMIP5 Projections: Implications for Tropical Precipitation Extremes, Geophys. Res. Lett., 49, e2021GL097295, https://doi.org/10.1029/2021GL097295, 2022. 
Bony, S., Semie, A., Kramer, R. J., Soden, B., Tompkins, A. M., and Emanuel, K. A.: Observed modulation of the tropical radiation budget by deep convective organization and lower-tropospheric stability, AGU Adv., 1, e2019AV000155, https://doi.org/10.1029/2019AV000155, 2020. 
Chen, Q., Fan, J., Hagos, S., Gustafson Jr., W. I., and Berg, L. K.: Roles of windshear at different vertical levels: Cloud system organization and properties, J. Geophys. Res.-Atmos., 120, 6551–6574, https://doi.org/10.1002/2015JD023253, 2015. 
Download
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.
Altmetrics
Final-revised paper
Preprint