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Open AccessArticle

Robust Nonparametric Methods of Statistical Analysis of Wind Velocity Components in Acoustic Sounding of the Lower Layer of the Atmosphere

1
Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
2
Institute of Monitoring of Climatic and Ecological Systems SB RAS, 634050 Tomsk, Russia
3
Kurgan State University, 640000 Kurgan, Russia
4
V.E. Zuev Institute of Atmospheric Optics SB RAS, 634021 Tomsk, Russia
5
National Research Tomsk State University, 634050 Tomsk, Russia
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(8), 961; https://doi.org/10.3390/sym11080961
Received: 13 June 2019 / Revised: 8 July 2019 / Accepted: 17 July 2019 / Published: 31 July 2019
(This article belongs to the Special Issue Information Technologies and Electronics)
Statistical analysis of the results of minisodar measurements of vertical profiles of wind velocity components in a 5–200 m layer of the atmosphere shows that this problem belongs to the class of robust nonparametric problems of mathematical statistics. In this work, a new consecutive nonparametric method of adaptive pendular truncation is suggested for outlier detection and selection in sodar data. The method is implemented in a censoring algorithm. The efficiency of the suggested algorithm is tested in numerical experiments. The algorithm has been used to calculate statistical characteristics of wind velocity components, including vertical profiles of the first four moments, the correlation coefficient, and the autocorrelation and structure functions of wind velocity components. The results obtained are compared with classical sample estimates. View Full-Text
Keywords: robust nonparametric pendular truncation method; outlier detection and selection; acoustic sounding; statistical characteristics of vertical profiles of wind velocity components robust nonparametric pendular truncation method; outlier detection and selection; acoustic sounding; statistical characteristics of vertical profiles of wind velocity components
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MDPI and ACS Style

Krasnenko, N.; Simakhin, V.; Shamanaeva, L.; Cherepanov, O. Robust Nonparametric Methods of Statistical Analysis of Wind Velocity Components in Acoustic Sounding of the Lower Layer of the Atmosphere. Symmetry 2019, 11, 961. https://doi.org/10.3390/sym11080961

AMA Style

Krasnenko N, Simakhin V, Shamanaeva L, Cherepanov O. Robust Nonparametric Methods of Statistical Analysis of Wind Velocity Components in Acoustic Sounding of the Lower Layer of the Atmosphere. Symmetry. 2019; 11(8):961. https://doi.org/10.3390/sym11080961

Chicago/Turabian Style

Krasnenko, Nikolay; Simakhin, Valerii; Shamanaeva, Liudmila; Cherepanov, Oleg. 2019. "Robust Nonparametric Methods of Statistical Analysis of Wind Velocity Components in Acoustic Sounding of the Lower Layer of the Atmosphere" Symmetry 11, no. 8: 961. https://doi.org/10.3390/sym11080961

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