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Empirical Motion Compensation for Turbulence Intensity Measurement by Floating LiDARs
by
Araki, Ryuzo
, Asou, Hiroshi
, Misaki, Takeshi
, Hamada, Kohei
, Konagaya, Mizuki
, Uchiyama, Shogo
, Ohsawa, Teruo
in
Algorithms
/ Comparative analysis
/ Datasets
/ floating LiDAR system
/ Measurement
/ motion compensation
/ Offshore
/ offshore measurement
/ Optical radar
/ Remote sensing
/ sensitivity analysis
/ Sensors
/ Standard deviation
/ turbulence intensity
2025
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Empirical Motion Compensation for Turbulence Intensity Measurement by Floating LiDARs
by
Araki, Ryuzo
, Asou, Hiroshi
, Misaki, Takeshi
, Hamada, Kohei
, Konagaya, Mizuki
, Uchiyama, Shogo
, Ohsawa, Teruo
in
Algorithms
/ Comparative analysis
/ Datasets
/ floating LiDAR system
/ Measurement
/ motion compensation
/ Offshore
/ offshore measurement
/ Optical radar
/ Remote sensing
/ sensitivity analysis
/ Sensors
/ Standard deviation
/ turbulence intensity
2025
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Do you wish to request the book?
Empirical Motion Compensation for Turbulence Intensity Measurement by Floating LiDARs
by
Araki, Ryuzo
, Asou, Hiroshi
, Misaki, Takeshi
, Hamada, Kohei
, Konagaya, Mizuki
, Uchiyama, Shogo
, Ohsawa, Teruo
in
Algorithms
/ Comparative analysis
/ Datasets
/ floating LiDAR system
/ Measurement
/ motion compensation
/ Offshore
/ offshore measurement
/ Optical radar
/ Remote sensing
/ sensitivity analysis
/ Sensors
/ Standard deviation
/ turbulence intensity
2025
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Empirical Motion Compensation for Turbulence Intensity Measurement by Floating LiDARs
Journal Article
Empirical Motion Compensation for Turbulence Intensity Measurement by Floating LiDARs
2025
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Overview
We propose an empirical motion compensation algorithm for a better turbulence intensity (TI) measurement by Floating LiDAR systems (FLSs) with a newly introduced motion parameter, the significant tilt angle θα,1/3, using four datasets from three different FLSs in Japan. The parameter was compared to other environmental parameters; it was confirmed to well represent various types of buoy motion. A sensitivity assessment was conducted for the error of the FLS’s standard deviation of wind speed to the buoy motion. The strong correlation obtained by the assessment suggests that the error of the FLS TI is dominated by the motion and that it is possible to offset the error by applying the relationship back to the measurement. The corrected TI shows good agreement with that of a reference fixed vertical LiDAR (VL). Moreover, the similarity of the relationships for the same type of VL mounted on different buoys implies that the correction may be VL-specific rather than FLS-specific, and, therefore, universal regardless of the FLS type. The successful validation suggests that the correction based on θα,1/3 can be applied not only to the future campaign but also to those performed in the past to revitalize numerous existing FLS datasets.
Publisher
MDPI AG
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