The spatiotemporal dynamics of the three wind velocity components in the atmospheric boundary layer is analyzed on the basis of Doppler minisodar measurements. The data were processed and analyzed with the help of robust nonparametric methods based on the weighted maximum likelihood method and classical methods. Distribution laws were obtained for each wind velocity component. There are outliers in the distribution functions; both right and left asymmetry of the distributions are observed. For the x- and ycomponents, the width of the distribution increases as the observation altitude is increased, but the maximum of the distribution function decreases, which is in agreement with the data available in the literature. For the zcomponents the width of the distribution remains practically constant, but the value of the maximum also decreases with altitude. Analysis of the hourly semidiurnal dynamics showed that all three components have maxima in the morning and evening hours. For the y- and z-components the maxima in the evening hours are more strongly expressed than in the morning hours. For the x- and y-components the horizontal wind shear is closely tracked in the evening hours. It is shown that adaptive estimates on the efficiency significantly exceed the classical parametric estimates and allow one to analyze the spatiotemporal dynamics of the wind velocity, and reveal jets and detect wind shears.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
N. P. Krasnenko, Acoustic Sounding of the Atmospheric Boundary Layer [in Russian], Vodolei, Tomsk (2001).
S. Breadly, Atmospheric Acoustic Remote Sensing. Principles and Applications, CRC Press Taylor & Francis Group, Boca Raton, Florida (2007).
A. B. Rykhlov, Climatological estimate of the wind-energy potential at different altitudes (in the Example of Southwestern Russia), Author’s Abstract of Doct. Geograph. Sci. Dissert., Kazan, Russia (2012).
P. J. Huber and E. M. Ronchetti, Robust Statistics, John Wiley & Sons, New York (2009).
V. A. Simakhin, Robust Nonparametric Estimates, Lambert Academic Publishing, Saarbrücken, Germany (2011).
V. A. Simakhin and O. S. Сherepanov, Int. J. Innovat. Comput. Inform. Control, 487, 397–405 (2014).
V. A. Simakhin and O. S. Сherepanov, Proceedings of the XXth International Symposium “Optics of the Atmosphere and Ocean. Atmospheric Physics” [in Russian] [Electronic resource], Izd. IOA SB RAS, Tomsk (2014), pp. 277–281.
N. P. Krasnenko, M. V. Tarasenkov, and L. G. Shamanaeva, Russ. Phys. J., 57, No. 11, 1539–1546 (2014).
O. F. Kapegesheva, N. P. Krasnenko, and L. G. Shamanaeva, Proc. SPIE. 20th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 92924N (November 25, 2014); Vol. 9292, doi: 10.1117/12.2075651 (6 pp.).
O. F. Kapegesheva, N. P. Krasnenko, and L. G. Shamanaeva, Proceedings of the XXth International Symposium “Optics of the Atmosphere and Ocean. Atmospheric Physics” [in Russian] [Electronic resource], Izd. IOA SB RAS, Tomsk (2014), pp. D118–D121. CD-ROM.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Fizika, No. 12, pp. 176–181, December, 2015.
Rights and permissions
About this article
Cite this article
Simakhin, V.A., Cherepanov, O.S. & Shamanaeva, L.G. Spatiotemporal Dynamics of the Wind Velocity from Minisodar Measurement Data. Russ Phys J 58, 1868–1874 (2016). https://doi.org/10.1007/s11182-016-0728-5
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11182-016-0728-5