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Paper Abstract and Keywords
Presentation 2022-07-01 15:40
Wind field reconstruction using LIDAR measurement and EnKF data assimilation
Takayuki Kitamura, Yusuke Ito, Satoshi Kageme (Mitsubishi Electric Corp.) SANE2022-20
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the use of consumer drones in various fields has become more active worldwide due to their higher performance and lower price. Drone flight safety is greatly affected by weather conditions, especially wind speed along the flight route. To fly drones safely for the various applications, nowcasting is necessary to accurately predict wind conditions at the time and place of drone flight. For this reason, the use of LIDAR has begun to be considered as a means of accurately determining wind speeds over drone flight areas. LIDAR performs observations by scanning laser beams in both horizontal and vertical directions in a three-dimensional observation space, but this has the disadvantage that the distance between the laser beams at a distance makes the observation data sparse with respect to the observation area. In this paper, we discuss a method to densify the sparse LIDAR wind data by signal processing, assuming that LIDAR will be used for nowcasting wind conditions along drone flight routes. The technical details of the wind field densification method and the numerical results of the study will also be presented.
Keyword (in Japanese) (See Japanese page) 
(in English) Scanning LIDAR / Wind Field Prediction / Data Assimilation / Ensemble Kalman Filter / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 92, SANE2022-20, pp. 27-32, July 2022.
Paper # SANE2022-20 
Date of Issue 2022-06-24 (SANE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SANE  
Conference Date 2022-07-01 - 2022-07-01 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SANE 
Conference Code 2022-06-SANE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Wind field reconstruction using LIDAR measurement and EnKF data assimilation 
Sub Title (in English)  
Keyword(1) Scanning LIDAR  
Keyword(2) Wind Field Prediction  
Keyword(3) Data Assimilation  
Keyword(4) Ensemble Kalman Filter  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Takayuki Kitamura  
1st Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
2nd Author's Name Yusuke Ito  
2nd Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
3rd Author's Name Satoshi Kageme  
3rd Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
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Speaker Author-1 
Date Time 2022-07-01 15:40:00 
Presentation Time 25 minutes 
Registration for SANE 
Paper # SANE2022-20 
Volume (vol) vol.122 
Number (no) no.92 
Page pp.27-32 
#Pages 6 
Date of Issue 2022-06-24 (SANE) 

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