## 1 Introduction
We studied the diurnal hourly dynamics of kinetic wind energy in the lower 200 m layer of the atmosphere based on minisodar measurements of the wind velocity vector that obeys the distribution \(F(\zeta,\overset{\rightarrow}{\theta})\). Like all big data arrays, the minisodar measurements of altitude profiles of the wind velocity vectors have outliers and measurement errors. The integral estimate of the kinetic wind energy in the ABL \(E_{\Sigma}\) can be obtained from sample means and variances of the vertical profiles of the wind velocity vectors. To estimate the energy of the mean wind velocity, the unbiased, consistent, and effective estimates of the parameters of distribution \(G(\zeta,\overset{\rightarrow}{\theta})\) should be obtained, based on the measured vertical profiles of the wind velocity vector with the distribution \(F(\zeta,\overset{\rightarrow}{\theta})\). This can be achieved by using robust estimates of the parameters of the distribution \(F(\zeta,\overset{\rightarrow}{\theta})\). Robust statistics is an actively developing branch of mathematics [8; 9]. At present, different methods (including subjective ones) of obtaining robust estimates are proposed based on the minimization of various robustness criteria. Thus, in works [9; 10; 11], the criteria of minimizing the maximum asymptotic bias and the maximum variance were proposed. The advantages and disadvantages of the criterion of minimizing the maximum entropy were analyzed in [6; 9]. The criteria of the maximum robustness and of the maximum distance were used for the minimum distance (MD) estimates in works [11; 12]. The adaptive estimators were studied in works [6; 11]. For example, for the shear parameter, about 50 different robust estimates were used, for example, based on robust M-estimators [11; 13], regression credibility [14], or robust linear regression models [15]. This is obviously caused by the fact that in robust statistics, there is no established robustness criterion, unlike the efficiency criterion in classical mathematical statistics [16]. First, the robustness and efficiency criteria turned out to be contradictory, which stimulated the application of the robust estimates with intermediate characteristics (for example, the MD and Hellinger distance estimates [10; 11] and the adaptive estimates [6; 11]). Second, for the asymmetric outlier characteristics typical of the ABL [6; 17; 18; 12], the robust estimates are biased and inconsistent [6; 19]. Third, methods for obtaining the robust estimates are aimed at the removal of external outliers distant from the bulk of observations, whereas the presence of internal outliers can lead to essential errors in decision-making. All these problems stimulate the development of new robust, efficient algorithms for a wide class of outlier distributions, including asymmetric distributions and distributions with internal outliers. Such algorithms should adapt to the priori distributions. In the present work, robust algorithms are synthesized by the maximum likelihood method that converge to the effective algorithms for the inhomogeneous minisodar measurement data. Unfortunately, real measurement data usually do not fit the mathematical models on which mathematical statistics heavily relies. Their processing requires solutions of a number of mathematical problems by robust nonparametric statistical methods [6]. Indeed, the problems of studying the spatiotemporal dynamics of kinetic wind energy in the ABL depend significantly on prior information on the distributions \(F(\zeta,\stackrel{{\rightarrow}}{{\theta}})\), \(G(\zeta,\stackrel{{\rightarrow}}{{\theta}})\), and \(H(\zeta)\).