Empirical orthogonal functions and related techniques in atmospheric science: A review

By Abdelwaheb Hannachi, Ian T. Jolliffe, David B. Stephenson. 2007

1,226 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c7640d5016d024424d5b8fee7b91afa6b92894bf

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Citations in the corpus

Characterising performance of environmental models (2013, methodology): “Another approach to data transformation is to use Empirical Orthogonal Functions (EOF), which has been extensively used, for instance, for analysis of spatial models in meteorology, climatology and atmospheric science (see Hannachi et al., 2007 for a thorough review).”
Independent Component Analysis of Climate Data: A New Look at EOF Rotation (2009, methodology): “[For a comprehensive review of EOFs and REOFs, refer to Hannachi et al. (2007) and references therein.]”
Methods for Exploring Spatial and Temporal Variability of Extreme Events in Climate Data (2008, methodology): “weights) that account for maximal variance (Hannachi et al. 2007).”
Low‐frequency nonlinearity and regime behavior in the Northern Hemisphere extratropical atmosphere (2017, methodology): “The EOFs are the eigenvectors of the symmetric covariance matrix, ordered so that the maximum amount of space-time variance is explained by the fewest modes.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, neutral): “[254] A.”
Empirical Orthogonal Functions: The Medium is the Message (2009, methodology): “We present a brief review of those aspects of EOF analysis that are relevant to the present discussion; a more comprehensive discussion can be found, for example, in Wilks (1995), von Storch and Zwiers (1999), or the recent review by Hannachi et al. (2007).”
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, background): “Popular dimensionality reduction approaches like empirical orthogonal functions (Hannachi et al., 2007) summarize complex data succinctly but may not necessarily do so in a way that maximizes information useful for predicting a specific variable.”