Сравнение методов

	Эмпирический ортогональный телесвязный анализ	Анализ максимальной ковариации	Модель WRF
Область	Метеорология	Метеорология	Метеорология
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1956	1992	2000
Автор метода	Lorenz, Wallace	Bretherton, Wallace	Skamarock and Klemp
Тип	Data analysis and pattern identification	Covariance decomposition method	Atmospheric simulation system
Основополагающий источник	Wallace, J. M., & Gutzler, D. S. (1981). Teleconnections in the geopotential height field during the Northern Hemisphere winter. Monthly Weather Review, 109(4), 784-812.	Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., & Blade, I. (1992). The effective number of spatial degrees of freedom of a time-varying field. Journal of the Atmospheric Sciences, 49(11), 1063-1083.	Skamarock, W. C., Klemp, J. B., Dudhia, J., et al. (2008). A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR.
Другие названия	EOF analysis, Empirical orthogonal function, Teleconnection patterns, PCA meteorology	MCA, Singular value decomposition, SVD analysis, Covariance analysis	Weather Research and Forecasting, WRF, ARW, NMM
Связанные	2	2	4
Сводка	Empirical orthogonal function (EOF) analysis is a statistical technique that identifies dominant spatial patterns and temporal variability in atmospheric or oceanic data. When applied to geographically distant locations, EOF analysis reveals teleconnection patterns—coherent patterns of variability that link weather systems across ocean basins and continents.	Maximum covariance analysis (MCA) is a statistical technique that identifies coupled patterns of variability between two spatially distributed fields (e.g., sea surface temperature and precipitation). Unlike EOF analysis which focuses on variance in a single field, MCA identifies spatial patterns that are maximally correlated between two different fields.	The Weather Research and Forecasting (WRF) model is a mesoscale atmospheric simulation system used for weather forecasting, research, and climate applications. Developed cooperatively by NCAR, NOAA, and academic institutions, WRF became operational in 2004 and has become one of the most widely used atmospheric models worldwide.