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

Abstract

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.

Semiparametric and semi-nonparametric estimates of the kinetic energy in the atmospheric boundary layer from minisodar measurements

“…This can be done by using various criteria, for example, the Grubbs criterion, or by constructing various algorithms of cluster analysis, for example, based on neural networks. [12][13][14][15] This approach has different independent applications, for example, for solving security and intrusion detection problems, but difficulties arise when nonparametric tests are being constructed, and the algorithms of cluster analysis are subjective. Second, the robust decision-making algorithms are constructed based on different robustness criteria that do not automatically consider the effect of the outliers.…”

Diurnal Dynamics of the Umov Kinetic Energy Density Vector in the Atmospheric Boundary Layer from Minisodar Measurements

“…Formula (4) was used to calculate the diurnal dynamics of the Umov vector components by post-processing of big volume of data measured with a commercial triaxial Doppler monostatic minisodar AV4000 (Atmospheric Systems Corporation, Santa Clarita, CA, USA) [13] in the vicinity of Santa Clarita, CA, USA, on 16 September 2006 [15]. The minisodar sensing range was 5-200 m; its vertical resolution was ∆z = 5 m. The acoustic antenna was an array of 50 speakers used to both transmit and receive acoustic signals at a frequency of 4900 Hz.…”

“…Statistical analysis of the results of minisodar measurements of vertical profiles of wind velocity components in the 5-200 m layer of the atmosphere performed in [12] showed that this problem belongs to the class of robust nonparametric problems of mathematical statistics. The raw minisodar data series were preprocessed to exclude outliers using the consecutive nonparametric method of adaptive pendular truncation for outlier detection developed by us and implemented in the censoring algorithm described in detail in [13]. The confidence intervals were found using bootstrap.…”

“…First of all, long series of heterogeneous data comprised a large number of outliers and unknown distribution of results of measurements. This necessitated preprocessing of big volume of raw minisodar data with application of original statistical data processing algorithms described in [12,13]. The next problem was that the typical sizes of turbulent vortices in the lower layer of the ABL changed from several meters to several tens of meters.…”