Articles | Volume 18, issue 4
https://doi.org/10.5194/essd-18-2929-2026
https://doi.org/10.5194/essd-18-2929-2026
Data description article
 | 
28 Apr 2026
Data description article |  | 28 Apr 2026

Global open-ocean daily turbulent heat flux dataset (1992–2020) from SSM/I via deep learning

Haoyu Wang, Mengjiao Wang, and Xiaofeng Li

Viewed

Total article views: 759 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
488 221 50 759 119 40 75
  • HTML: 488
  • PDF: 221
  • XML: 50
  • Total: 759
  • Supplement: 119
  • BibTeX: 40
  • EndNote: 75
Views and downloads (calculated since 06 Oct 2025)
Cumulative views and downloads (calculated since 06 Oct 2025)

Viewed (geographical distribution)

Total article views: 759 (including HTML, PDF, and XML) Thereof 759 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Apr 2026
Download
Short summary
DeepFlux provides a global, gap-free, daily record of air temperature, humidity, and turbulent heat flux from 1992 to 2020. Using satellite data and deep learning, it fills missing observations and delivers continuous estimates. Tests against in situ measurements show it is closer to reality and more reliable than existing products. This open resource supports improved climate studies and model evaluation.
Share
Altmetrics
Final-revised paper
Preprint