Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 11
Articles | Volume 18, issue 11
https://doi.org/10.5194/amt-18-2523-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2523-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 11
Research article
 | 
13 Jun 2025
Research article |  | 13 Jun 2025

Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements

Michael D. Himes, Ghassan Taha, Daniel Kahn, Tong Zhu, and Natalya A. Kramarova

Viewed

Total article views: 1,076 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
812 206 58 1,076 64 190
  • HTML: 812
  • PDF: 206
  • XML: 58
  • Total: 1,076
  • BibTeX: 64
  • EndNote: 190
Views and downloads (calculated since 10 Jul 2024)
Cumulative views and downloads (calculated since 10 Jul 2024)

Viewed (geographical distribution)

Total article views: 1,076 (including HTML, PDF, and XML) Thereof 1,068 with geography defined and 8 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Nov 2025
Download
Short summary
The Ozone Mapping and Profiler Suite's Limb Profiler (OMPS LP) yields near-global coverage and information about how aerosols from volcanic eruptions and major wildfires is vertically distributed through the atmosphere. We developed a machine learning method to characterize aerosols using OMPS LP measurements about 60 times faster than the current approach.
Read more
Share