Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 10
Articles | Volume 17, issue 10
https://doi.org/10.5194/amt-17-3103-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3103-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 10
Research article
 | 
23 May 2024
Research article |  | 23 May 2024

Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs

Hai Nguyen, Derek Posselt, Igor Yanovsky, Longtao Wu, and Svetla Hristova-Veleva

Viewed

Total article views: 2,337 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,700 545 92 2,337 103 121
  • HTML: 1,700
  • PDF: 545
  • XML: 92
  • Total: 2,337
  • BibTeX: 103
  • EndNote: 121
Views and downloads (calculated since 16 Nov 2023)
Cumulative views and downloads (calculated since 16 Nov 2023)

Viewed (geographical distribution)

Total article views: 2,337 (including HTML, PDF, and XML) Thereof 2,276 with geography defined and 61 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Feb 2026
Download
Short summary
Accurate global wind estimation is crucial for weather prediction and environmental modeling. Our study investigates a method to refine atmospheric motion vectors (AMVs) by comparing them with high-precision active-sensor winds. Leveraging supervised learning, we discovered that using high-precision active-sensor data can significantly reduce biases in passive-sensor winds in addition to providing estimates of the wind errors, thereby improving their reliability.
Read more
Share