We present the results of a comparison of high-level cloud (HLC) episodes with different ice crystal orientations from EARLINET ground-based lidar measurements and MODIS satellite data for the period 2015-2022. Cloud particles with total attenuated backscatter values greater than 5-10-6m -1 -sr-1 are considered preferentially oriented in the horizontal plane if the volume linear depolarization coefficient is less than 0.1, and randomly oriented if it is greater than 0.4. The following parameters of HLC with different orientation of ice crystals were considered: optical thickness, effective particle radius, waterpath, cloud-top height, reflection ratio, and effective emissivity ratio. The results of comparing the features of HLC with different particle orientations from ground-based and satellite data are discussed. It was found that over Europe (as well as over Western Siberia) HLC with reflection ratios greater than 0.15 and effective emissivity ratios greater than 0.5 consist of preferentially oriented ice crystals in the horizontal plane. The typical values of HLC parameters with different particle orientations over Europe were found. We present the analysis results of HLC with specularly reflecting layers having anomalous values of reflection ratios and effective emissivity ratio.
We present the results of testing the algorithm of cloud-base height (CBH) estimation to determine this parameter for multi-layer clouds from data of passive remote sensing by MODIS spectroradiometer (Aqua satellite). Episodes of observations of two- and three-layer clouds over Western Siberia in summer time for the period from 2010 to 2015 were considered mainly with the following parameters: optical thickness less than 20, distance between the layers more than 1 km, and multi-layer flag value greater than 3. Sampling was based on the results of remote sensing by CALIOP lidar (CALIPSO satellite) and CPR radar (CloudSat satellite). The most repeated combinations of clouds in different levels observed simultaneously as part of multi-layer clouds were determined. The results of CBH estimation for multi-layer clouds and their statistical analysis are discussed. We found that the algorithm generally gives underestimated values of the CBH relative to the overlying level and overestimated values in comparison to the underlying. The perspective ways for development of this work are given
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.
An algorithm for detecting specular-reflecting layers in high-level clouds was tested using passive satellite sounding data. We study cirrus clouds with an optical thickness of less than 5 consisting of horizontally oriented ice crystals, which was observed over the territory of Western Siberia from 2006 to 2007. A description of the technique for detecting specular-reflecting layers in high-level clouds and performing a statistical analysis of their parameters using MODIS data is presented. We discuss the seasonal-latitudinal dependences of the considered parameters of clouds over Western Siberia, which consist of horizontally oriented ice crystals.
The article presents study of diurnal cloud transformations over Western Siberia using MODIS and VIIRS satellite data obtained in the period from 2017 to 2020, as well as estimates of the variability for cloud features. The following cloud parameters are considered: top height, pressure, temperature, phase and effective emissivity. The paper introduces with a description of the used satellite images and data products. A specification of the methodology for analyzing diurnal cloud transformations and variability of cloud parameters using polynomial approximation methods is described. The results of comparison of the obtained trends in cloud features with the data of ground-based weather stations in Western Siberia are discussed. The article presents the most typical cloud transformations and their reasons over the studied region for the considered period of time.
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.
The comparison results for the characteristics of various cloud types according to MODIS and VIIRS data obtained for similar time are presented. The territory of Western Siberia is considered as the studied region. I have described the methodology for comparing satellite imagery of VIIRS and MODIS obtained for similar time. The cloud classification results by VIIRS day/night images are used for this. I have identified promising MODIS spectral bands for separating subtypes of some main cloud forms. The comparison results for the characteristics of various cloud types and their variability at nighttime are discussed. Promising directions for the development of this paper are presented.
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.
The investigation results of atmospheric gravity waves cloudy manifestations observed over the water area of the Kuril Island ridge during the propagation of powerful transoceanic tsunami 2009-2010 are shown. The description of tsunami characteristics is based on the use of information from autonomous deep-water stations of the Institute of Marine Geology and Geophysics FEB RAS in the Southern Kuril Islands and the Tsunami Warning Service telemetering recorder located in one of the ports on Paramushir Island. The environment condition information was extracted from the results of remote sensing of the Earth from space by the MODIS sensor and aerological measurements at the meteorological station of Severo-Kurilsk. The results of analyzing the characteristics of wave processes in the atmosphere and the ocean are discussed and their comparison is carried out.
A software system for classifying 14 types of one-layer and convective clouds is presented; it is based on the use of neural network classifier and information on texture of MODIS satellite images with the spatial resolution of 250 m. The used classification algorithm and a modified method for its learning are described. The estimates of the generalizing capability of the developed classifier and the reliability of results of the cloud classification on the basis of test sample are discussed.
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