The use of methods of the nonlinear spatial-temporal data regularization for the analysis of meteorological observations
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| 1Kostyuchenko, Yu.V, 2Bilous, Yu.H, 3Movchan, DM, 2Kopachevsky, IM, 4Yushchenko, MV, 2Artemenko, IG, 5Popadyuk, LV 1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine 2State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Science of the National Academy of Sciences of Ukraine», Kyiv, Ukraine 3State institution «Scientific Centre for Aerospace Research of the Earth” of the Institute of Geological Science of the National Academy of Sciences of Ukraine, Kyiv, Ukraine 4State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine 5State institution «Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine», Kyiv |
| Kosm. nauka tehnol. 2013, 19 ;(5):42–49 |
| https://doi.org/10.15407/knit2013.05.042 |
| Publication Language: Ukrainian |
Abstract: On the basis of an analysis of investigated parameters, a method for statistical analysis of observations (including archive data and observations from different sources) is proposed. The resulted distributions are presented in units invariant toward the initial data metrics. For deriving distributions of meteorological measurements, which are regular in space and time, at a regional scale, the algorithm based on the Kernel Principal Component Analysis (KPCA) is proposed. The results of the analysis of climate parameters regional distributions (multiyear meteorological measurements) are compared with known conventional re-analysis models (NCEP/NCAR). Some spatial and temporal features of climate parameter change are defined at the regional scale. Our results are analyzed in comparison with changes of vegetation productivity and greenhouse gases (CO2), derived from remote sensing data. It should be mentioned that the proposed method gives more correct estimations of regional risk parameters and security. |
| Keywords: disaster, greenhouse gases, satellite data, the climate parameter change |
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