Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 2, issue 1
Articles | Volume 2, issue 1
https://doi.org/10.5194/ascmo-2-49-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/ascmo-2-49-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 2, issue 1
10 Jun 2016
 | 10 Jun 2016

A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors

Laura D. Riihimaki, Jennifer M. Comstock, Kevin K. Anderson, Aimee Holmes, and Edward Luke

Viewed

Total article views: 1,817 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,086 594 137 1,817 146 147
  • HTML: 1,086
  • PDF: 594
  • XML: 137
  • Total: 1,817
  • BibTeX: 146
  • EndNote: 147
Views and downloads (calculated since 10 Jun 2016)
Cumulative views and downloads (calculated since 10 Jun 2016)

Cited

Latest update: 14 Dec 2024
Download
Short summary
Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled liquid droplets, or a mixture of both, impacting how they influence the atmospheric energy budget and challenging our ability to simulate climate change. Better cloud-phase measurements are needed to improve simulations. We demonstrate how a Bayesian method to identify cloud phase can improve on currently used methods by including information from multiple measurements and probability estimates.
Read more