Articles | Volume 17, issue 10
https://doi.org/10.5194/amt-17-3103-2024
https://doi.org/10.5194/amt-17-3103-2024
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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Cited articles

Bies, R. R., Muldoon, M. F., Pollock, B. G., Manuck, S., Smith, G., and Sale, M. E.: A genetic algorithm-based, hybrid machine learning approach to model selection, J. Pharmacokinet. Phar., 33, 195–221, 2006. a
Blanchet, F. G., Legendre, P., and Borcard, D.: Forward selection of explanatory variables, Ecology, 89, 2623–2632, 2008.  a
Bormann, N. and Thépaut, J.-N.: Impact of MODIS polar winds in ECMWF's 4DVAR data assimilation system, Mon. Weather Rev., 132, 929–940, 2004. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a, b, c, d
Chase, R. J., Harrison, D. R., Burke, A., Lackmann, G. M., and McGovern, A.: A machine learning tutorial for operational meteorology. Part I: Traditional machine learning, Weather Forecast., 37, 1509–1529, 2022. a, b, c
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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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