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Activity Number: 666
Type: Invited
Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #307401
Title: Time-Structured PCA for Describing Modes of Variability in Large Global Climate Data Sets
Author(s): Cari Kaufman*+ and Ben Shaby
Companies: UC Berkeley and UC - Berkeley
Keywords: PCA ; Bayesian statistics ; climate
Abstract:

Principal components analysis (PCA) is widely used in atmospheric science under the moniker "Empirical Orthogonal Functions" (EOF). The results of this analysis are used to study the primary modes of variability in atmospheric data, which is often high-dimensional in space and time. PCA may be cast in a probabilistic framework and inference carried out from a Bayesian perspective (see e.g. Tipping and Bishop, 1999, JRSSB). We adapt this framework to take into account the spatial and temporal correlation we anticipate in atmospheric data. In addition to incorporating spatial dependence, we describe how to choose prior distributions for the temporal components of the model to isolate various timescales, using an autocorrelation function that includes a periodic component. The model can be used for exploratory purposes by pre-specifying time-scales of interest and examining the corresponding spatial patterns.


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