This paper considers the use of various clustering methods to characterize the seasonal features of the temperature stratification of the atmosphere surface layer. As initial data, we used series of observations of temperature profiles in a layer up to 1 km, obtained by a passive microwave profiler MTP-5. Clustering methods with automatic determination of the number of clusters are considered: DBSCAN, Affinity Propagation, MeanShift. It is shown that the DBSCAN method is of little use for identifying typical conditions for continuous series of observations. Affinity Propagation and MeanShift methods give similar results, but require fine tuning of the clustering parameters. The results of the temperature profiles clustering are presented with the identification of cluster centers and their parameters: the average temperature profile gradient and extreme values of the profile curvature.
Metrological support and meteorological lidar's data verification is an urgent task. The article discusses original methods and tools for verification of the main operational parameters of meteorological lidar such as aerosol profilers and coherent pulsed wind Doppler lidar. It is shown that the problem of lidar metrological support can be effectively solved using fiber-optic technologies.
Wind profiler based on continuous laser source is considers in this work. This lidar allows to measure wind profile up to 300 m altitude. Hardware level signal processing technic is developed by JSC «BANS». Increasing accuracy and speed of wind parameters calculation signal processing technics have been studied in this research. The practice results are presented.
Impulse wind lidar (IWL) signal processing software developed by JSC «BANS» recovers full wind speed vector by radial projections and provides wind parameters information up to 2 km distance. Increasing accuracy and speed of wind parameters calculation signal processing technics have been studied in this research. Measurements results of IWL and continuous scanning lidar were compared. Also, IWL data processing modeling results have been analyzed.
KEYWORDS: LIDAR, Data modeling, Atmospheric modeling, Mathematical modeling, Algorithm development, Atmospheric particles, Data processing, Statistical modeling, Signal processing, Software development
Impulse wind lidar (IWL) signal processing software developed by JSC «BANS» recovers full wind speed vector by radial projections and provides wind parameters information up to 2 km distance. Increasing accuracy and speed of wind parameters calculation signal processing technics have been studied in this research. New double-elevation scan scheme and IWL data processing algorithm was developed and tested on lidar data base obtained during 70 hour atmosphere scan. Also, developed new IWL scanning scheme results were analyzed and compared to classic scheme
Wind profiler based on continuous laser source is considers in this work. This lidar allows to solve the task of wind profile restoration up to 300 m altitude. Hardware level signal processing technic is developed by JSC «BANS». Increasing accuracy and speed of wind parameters calculation signal processing technics have been studied in this research.
It is shown that the maximum frequency distribution of icing pireps at the Novosibirsk International Airport in January 2015 was accounted for the height layer from 0 to 1 km. With the increasing height aircraft icing is less common and, starting at 5 km, it is not recorded. Maximum frequency distribution for the Tomsk International Airport in winter 2014 – 2015 was also recorded at these altitudes, but it is not so pronounced, and from 4 km icing has not been reported. Altitude dependencies of frequency distribution for Novosibirsk and Tomsk airports are significantly different from that in the continental United States [1] and from the results published in [2].
Remote sensing technique of detection of potential aircraft icing areas based on temperature profile measurements, using meteorological temperature profiler, and the data of the Airfield Measuring and Information System (AMIS-RF), was proposed, theoretically described and experimentally validated during the field project in 2012 - 2013 in the Tomsk Bogashevo Airport. Spatial areas of potential aircraft icing were determined using the RAP algorithm and Godske formula. The equations for the reconstruction of profiles of relative humidity and dew point using data from AMIS-RF are given. Actual data on the aircraft icing for the Tomsk Bogashevo Airport on 11 October 2012 and 17 March 2013 are presented in this paper. The RAP algorithm and Godske formula show similar results for the location of spatial areas of potential icing. Though, the results obtained using the RAP algorithm are closer to the actual data on the icing known from aircraft crew reports.
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