Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 7
Articles | Volume 16, issue 7
https://doi.org/10.5194/amt-16-1971-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-16-1971-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 7
Research article
 | 
14 Apr 2023
Research article |  | 14 Apr 2023

The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – towards the mitigation of 3D bias in airborne and spaceborne passive imagery cloud retrievals

Hong Chen, K. Sebastian Schmidt, Steven T. Massie, Vikas Nataraja, Matthew S. Norgren, Jake J. Gristey, Graham Feingold, Robert E. Holz, and Hironobu Iwabuchi

Viewed

Total article views: 2,986 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,254 647 85 2,986 61 94
  • HTML: 2,254
  • PDF: 647
  • XML: 85
  • Total: 2,986
  • BibTeX: 61
  • EndNote: 94
Views and downloads (calculated since 29 Jun 2022)
Cumulative views and downloads (calculated since 29 Jun 2022)

Viewed (geographical distribution)

Total article views: 2,986 (including HTML, PDF, and XML) Thereof 2,883 with geography defined and 103 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jul 2024
Download
Short summary
We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T) and propose a radiance self-consistency approach for quantifying and mitigating 3D bias in legacy airborne and spaceborne imagery retrievals due to spatially inhomogeneous clouds and surfaces.
Read more
Special issue
Cloud, Aerosol and Monsoon Processes Philippines Experiment...