30 November 2017 Study of phase clustering method for analyzing large volumes of meteorological observation data
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Proceedings Volume 10466, 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 104665L (2017) https://doi.org/10.1117/12.2286873
Event: XXIII International Symposium, Atmospheric and Ocean Optics, Atmospheric Physics, 2017, Irkutsk, Russian Federation
Abstract
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.
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Yu. V. Volkov, Yu. V. Volkov, V. A. Krutikov, V. A. Krutikov, I. A. Botygin, I. A. Botygin, V. S. Sherstnev, V. S. Sherstnev, A. I. Sherstneva, A. I. Sherstneva, } "Study of phase clustering method for analyzing large volumes of meteorological observation data", Proc. SPIE 10466, 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 104665L (30 November 2017); doi: 10.1117/12.2286873; https://doi.org/10.1117/12.2286873
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