A practical study of statistical modelling language packages R has been carried out using regularization algorithms, more precisely one of the algorithms called the Extreme Learning Machine (ELM). Due to its simple implementation, ELM requires less researcher intervention in setting its parameters. At the same time, the generalization performance of ELM is not sensitive to the dimensionality of the feature space (the number of hidden nodes). Even on a medium-power personal computer, this class of neural networks has made it possible to perform numerous experiments on model building, forecasting and identifying cause-effect relationships in meteorological time series, downloaded from the climate monitoring system of IMCES SB RAS in a reasonable amount of time.
The results of correlation and regression analysis of meteorological data from an ultrasonic weather station are presented. Automatic weather station AMK-03 was equipped with three units for measuring meteorological parameters installed on the radio mast at heights of 2, 8 and 28 meters. Discreteness of measurements was 1 min. Time series with temperature, wind speed, atmospheric pressure and relative humidity obtained for the period from 01.01.2017 to 31.12.2017 were processed in software experiments. The methodology of correlation-regression analysis meteorological data, additionally, was used for diagnostics of weather station operation. Inaccuracies in the measurement of some parameters surface layer of the atmosphere have been revealed. The conducted control measurements and processing allowed to improve the measuring system of automatic weather station AMK-03.
The authors developed the algorithm based on analytic signal theory, allowing grouping geophysical signals over various spatial and temporal scales. Surface temperature was selected as an integrated indicator of climate change. Algorithm can distinguish the climatic structures where multi-year temperature oscillations are congruent. To accomplish that, the information on temperature series amplitude temporal changes was used. Computing technology developed was applied to the data from 818 northern hemisphere meteorological stations over the period of 1955-2010. The classification for three correlation thresholds was obtained. Distinguished structures have strict geographical differentiation and defined by the highest synchronism level of temperature oscillations. Stations closest to each other spatially demonstrate the highest strength of relationship to typical class envelope.
The paper researches Northern hemisphere surface temperature field structure based on the data of 818 meteorological stations for different time frames. Surface temperature is an integrated indicator of the global and regional climate change. Authors classified the stations by the degree of congruence in their multi-year temperature dynamics at various yearly intervals, corresponding to global climate trends. Temperature observational series were interpreted as phase modulated oscillations. The suggested classification is based on the hypothesis of geographical dependence in temperature signal phase modulation specifics. Congruence, namely temperature oscillation phasing in particular regions serves as a criteria for classification. The totality of climate-regulating influences on climate system forms a complex kind of phase modulation, which is though in some correspondence to those disturbances. We believe, that changes in synchronization modes of climatic and natural processes, is consequent to system transition into a new quality. The paper shows that with a global temperature growth regional temperature fields restructure and degree of congruence in temperature dynamics changes. Those changes are not uniform over different regions of the hemisphere. Temperature field congruence tends to decrease. The search for synchronization in non-linear chaotic systems, sensitive to initial conditions, might become a promising way of predicting models optimization.
The article describes an iterative parallel phase grouping algorithm for temperature field classification. The algorithm is based on modified method of structure forming by using analytic signal. The developed method allows to solve tasks of climate classification as well as climatic zoning for any time or spatial scale. When used to surface temperature measurement series, the developed algorithm allows to find climatic structures with correlated changes of temperature field, to make conclusion on climate uniformity in a given area and to overview climate changes over time by analyzing offset in type groups. The information on climate type groups specific for selected geographical areas is expanded by genetic scheme of class distribution depending on change in mutual correlation level between ground temperature monthly average.
The paper provides the results of the analysis of a region-specific response of the surface temperature field in the Northern Hemisphere to the changes in solar activity. The surface temperature is selected as one of the main integral signs of climate change. Assimilation of the influence of solar activity on Earth changes depending on the location of a weather station and in the annual course. The hypothesis of the consistency of climatic processes is confirmed using the phase grouping algorithm. Climatic structures where temperature changes occur more consistently and response to external forcing influence differs are identified. Stations located in equatorial, sub-equatorial, and tropical belts and in zones affected by the largest marine centers of the climatic system activity are more sensitive to external influences.
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