Articles | Volume 11, issue 5
https://doi.org/10.5194/amt-11-3177-2018
https://doi.org/10.5194/amt-11-3177-2018
Research article
 | 
01 Jun 2018
Research article |  | 01 Jun 2018

Neural network cloud top pressure and height for MODIS

Nina Håkansson, Claudia Adok, Anke Thoss, Ronald Scheirer, and Sara Hörnquist

Viewed

Total article views: 4,526 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,001 1,399 126 4,526 118 116
  • HTML: 3,001
  • PDF: 1,399
  • XML: 126
  • Total: 4,526
  • BibTeX: 118
  • EndNote: 116
Views and downloads (calculated since 30 Jan 2018)
Cumulative views and downloads (calculated since 30 Jan 2018)

Viewed (geographical distribution)

Total article views: 4,526 (including HTML, PDF, and XML) Thereof 4,320 with geography defined and 206 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Jul 2025
Download
Short summary
In this paper a new algorithm for cloud top height retrieval from imager instruments like MODIS is presented. It uses artificial neural networks and reduces the mean absolute error by 32 % compared to two other operational cloud height algorithms. This means that improved cloud height retrieval for nowcasting, as input to models and in cloud climatologies is possible.
Share