Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 19
Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5701-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-5701-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 19
Research article
 | 
12 Oct 2022
Research article |  | 12 Oct 2022

Ice water path retrievals from Meteosat-9 using quantile regression neural networks

Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh

Viewed

Total article views: 1,510 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
985 473 52 1,510 56 41 27
  • HTML: 985
  • PDF: 473
  • XML: 52
  • Total: 1,510
  • Supplement: 56
  • BibTeX: 41
  • EndNote: 27
Views and downloads (calculated since 04 Jul 2022)
Cumulative views and downloads (calculated since 04 Jul 2022)

Viewed (geographical distribution)

Total article views: 1,510 (including HTML, PDF, and XML) Thereof 1,500 with geography defined and 10 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Read more