Preprints
https://doi.org/10.5194/egusphere-2024-3434
https://doi.org/10.5194/egusphere-2024-3434
14 Nov 2024
 | 14 Nov 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Relationship between latent and radiative heating fields of Tropical cloud systems using synergistic satellite observations

Xiaoting Chen, Claudia J. Stubenrauch, and Giulio Mandorli

Abstract. In order to investigate the relationship between latent and radiative heating (LH, RH), particularly within mesoscale convective systems (MCSs), we used synergistic satellite-derived data from active instruments. Given the sparse sampling of these observations, we expanded the Spectral LH profiles derived from the Tropical Rain Measurement Mission (TRMM-SLH) by applying artificial neural network regressions on Clouds from InfraRed Sounder data and meteorological reanalyses, following a similar approach as for the expansion of the RH profiles. The zonal averages of vertically integrated LH (LP) at 1:30 AM and PM LT align well with those from the full diurnal sampling of TRMM–SLH over ocean. For Upper Tropospheric (UT) clouds releasing large latent heat, the surface temperature has a larger impact on the atmospheric cloud radiative effect (ACRE) in dry than in humid environments, while for lower clouds, producing relatively small latent heat, humidity plays a large role in enhanced ACRE. The distribution of UT clouds in the LP–ACRE plane shows a very large spread in ACRE for small LP, which is gradually reduced towards larger LP. The mean ACRE per MCS increases with LP, ranging from 50 to 115 W m-2. As expected, the shapes of the LH profiles of mature MCSs show that larger, more organized MCSs have a larger contribution of stratiform rain than the smaller MCSs. Convective organization enhances the mean ACRE of the MCS by up to 20 W m-2.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Xiaoting Chen, Claudia J. Stubenrauch, and Giulio Mandorli

Status: open (until 26 Dec 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Xiaoting Chen, Claudia J. Stubenrauch, and Giulio Mandorli
Xiaoting Chen, Claudia J. Stubenrauch, and Giulio Mandorli

Viewed

Total article views: 58 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
47 8 3 58 11 0 0
  • HTML: 47
  • PDF: 8
  • XML: 3
  • Total: 58
  • Supplement: 11
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 14 Nov 2024)
Cumulative views and downloads (calculated since 14 Nov 2024)

Viewed (geographical distribution)

Total article views: 56 (including HTML, PDF, and XML) Thereof 56 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Nov 2024
Download
Short summary
Strongly precipitating mesoscale convective systems produce large 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 vertical gradient of radiative effects of the mesoscale convective systems.