Data assimilation for clear air turbulence by upstream lidar observation

Ryoichi Yoshimura, Aiko Yakeno, Shigeru Obayashi, Takashi Misaka, Ryota Kikuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationAIAA AVIATION 2020 FORUM
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105982
DOIs
Publication statusPublished - 2020
EventAIAA AVIATION 2020 FORUM - Virtual, Online
Duration: 2020 Jun 152020 Jun 19

Publication series

NameAIAA AVIATION 2020 FORUM
Volume1 PartF

Conference

ConferenceAIAA AVIATION 2020 FORUM
CityVirtual, Online
Period20/6/1520/6/19

ASJC Scopus subject areas

  • Nuclear Energy and Engineering
  • Aerospace Engineering
  • Energy Engineering and Power Technology

Fingerprint Dive into the research topics of 'Data assimilation for clear air turbulence by upstream lidar observation'. Together they form a unique fingerprint.

Cite this