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

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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.
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