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DOI	10.1175/JCLI-D-20-0266.1
	Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems
	Guilloteau C.; Mamalakis A.; Vulis L.; Le P.V.V.; Georgiou T.T.; Foufoula-Georgiou E.
发表日期	2021
ISSN	0894-8755
起始页码	715
结束页码	736
卷号	34 期号:2
英文摘要	Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3-60-day periods) in both GPH and SST and El Niño-Southern Oscillation (ENSO) at low frequencies (2-7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics. © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
英文关键词	Atmospheric pressure; Climatology; Economic and social effects; Mechanical waves; Oceanography; Surface waters; Daily time series; Frequency resolutions; Geo-potential heights; Propagation dynamics; Sea surface temperature (SST); Southern oscillation; Spatiotemporal patterns; Specific frequencies; Frequency estimation; detection method; El Nino-Southern Oscillation; empirical orthogonal function analysis; geopotential; pressure; principal component analysis; sea surface temperature; spatiotemporal analysis; spectral analysis
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170952
作者单位	Department of Civil and Environmental Engineering, University of California Irvine, Irvine, CA, United States; Faculty of Hydrology Meteorology and Oceanography, Vietnam National University, Hanoi, Viet Nam; Department of Mechanical and Aerospace Engineering, University of California Irvine, Irvine, CA, United States; Department of Earth Systems Science, University of California Irvine, Irvine, CA, United States
推荐引用方式 GB/T 7714	Guilloteau C.,Mamalakis A.,Vulis L.,et al. Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems[J],2021,34(2).
APA	Guilloteau C.,Mamalakis A.,Vulis L.,Le P.V.V.,Georgiou T.T.,&Foufoula-Georgiou E..(2021).Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems.Journal of Climate,34(2).
MLA	Guilloteau C.,et al."Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems".Journal of Climate 34.2(2021).