TY - GEN
T1 - Data assimilation for clear air turbulence by upstream lidar observation
AU - Yoshimura, Ryoichi
AU - Yakeno, Aiko
AU - Obayashi, Shigeru
AU - Misaka, Takashi
AU - Kikuchi, Ryota
N1 - Publisher Copyright:
© 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2020
Y1 - 2020
N2 - We numerically investigated possibility of a data assimilation methodology for reproduction of Clear Air Turbulence (CAT) observed by LIDAR. The three-dimensional vortices induced by the shear layer instability were predicted by combining pseudo Lidar observation and the 6th-order accuracy numerical simulation based on the Euler equations by using the data assimilation. The assimilation performance was verified in identical-twin experiments; the true state and the observation data were generated artificially. We considered three methods for pseudo observation to verify the sensitivity of observed variables to prediction accuracy: observing only horizontal velocity, horizontal and vertical velocity components on the flight path, and wind speeds projected on the LIDAR’s line of sight. The results of the identical-twin experiments indicated that observing u and vertical wind components reduced the forecast error, and the data assimilation was able to restore the information of these two essential wind components from a wind speed data observed by LIDAR when its line of sight was inclined in the z-direction.
AB - We numerically investigated possibility of a data assimilation methodology for reproduction of Clear Air Turbulence (CAT) observed by LIDAR. The three-dimensional vortices induced by the shear layer instability were predicted by combining pseudo Lidar observation and the 6th-order accuracy numerical simulation based on the Euler equations by using the data assimilation. The assimilation performance was verified in identical-twin experiments; the true state and the observation data were generated artificially. We considered three methods for pseudo observation to verify the sensitivity of observed variables to prediction accuracy: observing only horizontal velocity, horizontal and vertical velocity components on the flight path, and wind speeds projected on the LIDAR’s line of sight. The results of the identical-twin experiments indicated that observing u and vertical wind components reduced the forecast error, and the data assimilation was able to restore the information of these two essential wind components from a wind speed data observed by LIDAR when its line of sight was inclined in the z-direction.
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U2 - 10.2514/6.2020-2822
DO - 10.2514/6.2020-2822
M3 - Conference contribution
AN - SCOPUS:85092780903
SN - 9781624105982
T3 - AIAA AVIATION 2020 FORUM
BT - AIAA AVIATION 2020 FORUM
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA AVIATION 2020 FORUM
Y2 - 15 June 2020 through 19 June 2020
ER -