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.
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.
the Creative Commons Attribution 4.0 License.
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
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Derek Posselt
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Igor Yanovsky
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Longtao Wu
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Svetla Hristova-Veleva
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
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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.
Accurate global wind estimation is crucial for weather prediction and environmental modeling....