Model estimates of short-wave solar radiation fluxes for summer conditions in the southern part of Western Siberia in the presence of single-layer of the low and high-level clouds are presented. The cloudiness characteristics were obtained on the basis of statistical models based on long-term satellite MODIS observations: the ranges of variability of the optical thickness and effective particle radius were 1.5-40 and 7.5-25 mkm for liquid clouds and 1-5 and 15-50 mkm for ice clouds, respectively. These low-level clouds characteristics variations lead to changes in the average daily radiation fluxes in the range of 150-300 W/m2 at the bottom (BOA) and 130-230 W/m2 at the top (TOA) of the atmosphere and for ice clouds 230-310 W/m2 at the BOA and 140-220 W/m2 at the TOA. At a fixed optical depth, there is a relatively weak dependence of the average daily radiation fluxes on the effective particle radius.
The algorithm’s approbation results for estimating the cloud base height from passive remote sensing data from space are presented. We apply the technology of artificial neural networks. The algorithm combines two existing approaches in this area: the use of statistical relationships between the cloud base height and other cloud features, and the use of the "donor-recipient" concept. We apply the Kohonen self-organizing map as a classifier. CALIOP data (CALIPSO satellite) and MODIS data (Aqua satellite) are used at the training stage of the selected neural network. Retrieving of the cloud base height by a tuned classifier is already carried out only on the basis of the passive remote sensing results from space. The algorithm makes it possible to estimate the studied parameter for low and high-level clouds at 15 . We discuss the results of retrieving the cloud base height from MODIS satellite images obtained over the territory of Western Siberia in 2013.
The formation results of statistical model for characteristics of atmospheric internal waves and their signatures based on satellite data and upper-air observations are presented. We consider some physical parameters of wave processes and geometric features of their cloud manifestations. The model formation is based on the determination of distribution laws that describe fluctuations in the characteristic values of atmospheric internal waves and their signatures. We use twoparameter families of absolutely continuous distributions. The observation episode description of atmospheric internal waves is based on the use of MODIS satellite data and the results of upper-air sounding by a network of ground-based weather stations. We consider the Pacific coast of the Russian Federation as the region under study. Promising areas for using the formation results of statistical model are discussed.
We developed a method for analyzing the variations in characteristics of different cloud types on the basis of cloud classification and thematic processing of MODIS satellite data. The studies were performed using an original algorithm of recognition of 16 cloud types in snow-free periods and 12 cloud types in snow-covered period, based on the technology of artificial neural networks and fuzzy logic methods. The algorithm and software are described. We discuss the studies of seasonal and annual variations in physical parameters of different cloud types over Western Siberian climatic zones: tundra, forest-tundra, bogs, taiga, and forest-steppe.
Characteristics of atmospheric internal waves and their signatures are analyzed using satellite data and results of upperair sounding. We consider the geometric features of cloud manifestations of wave processes and the corresponding physical parameters, as well as the current environmental state. The results of searching for the episodes of observations of atmospheric internal waves and their signatures over the Pacific coast of the Russian Federation from 2012 to 2017 are presented. We determined areas in the study region with the most frequent occurrence of these phenomena. The annual behavior of these parameters of wave processes is considered, and the results of analysis of their interannual variations over this region are discussed.
Statistical models are presented of the image texture and cloudiness physical characteristics over various natural zones of the Russian Federation during periods of snow cover. These models are based on the determination of the distribution laws and estimation of their parameters, which describe the fluctuations in the values of the cloud characteristics. The results are discussed of a comparative analysis of statistical cloud models for various natural zones, as well as cloud models, averaged over them, over snow-covered territories and a snow-free underlying surface. A description is presented of the cloud classification algorithm based on the application of artificial neural network technology and fuzzy logic methods. The results are presented of recognition of 12 cloud types from MODIS satellite data for various natural zones during seasons with snow cover.
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