Topic Notes Book Chap Dates (2014) Intro 1chap2.ppt

Topic Notes Book Chap Dates (2014) Intro 1chap2.ppt 2 April 2

Teleconnections-EOT 2achap4.ppt 4 April 2 EOF 3 chap5.ppt 5 April 9

Degrees of freedom 4a 6 April 9 Constructed Analogue 4bchap7.ppt 7 April 9

Regression at Pattern Level 6 8 April 23 CPC forecasts 5 9 April 23

5. Empirical Orthogonal Functions The purpose is to discuss Empirical Orthogonal Functions (EOF), both in method and application. When dealing with teleconnections in the previous chapter we came very close to EOF, so it will be a natural extension of that theme. However, EOF opens the way to an alternative point of view about space-time relationships, especially correlation across distant times as in analogues. EOFs have been treated in book size texts, most recently in Jolliffe (2002), a principal older reference being Preisendorfer(1988). The subject is extremely interdisciplinary, and each field has its own nomenclature, habits and notation. Jolliffes book is probably the best attempt to unify various fields. The term EOF appeared first in meteorology in Lorenz(1956). Zwiers and von Storch(1999) and Wilks(1995) devote lengthy single chapters to the topic. Here we will only briefly treat EOF or PCA (Principal Component Analysis) as it is called in most fields. Specifically we discuss how to set up the covariance matrix, how to calculate the EOF, what are their properties, advantages, disadvantages etc. We will do this in both spacetime set-ups already alluded to in Eqs (2.14) and (2.14a). There are no concrete rules as to how one constructs the covariance matrix. Hence there are in the literature matrices based on correlation, based on covariance etc. Here we follow the

conventions laid out in Chapter 2. The postprocessing and display conventions of EOFs can also be quite confusing. Examples will be shown, for both daily and seasonal mean data, for both the Northern and Southern Hemisphere. 5.1 Methods and definitions 5.1.1 Working definition: Here we cite Jolliffe (2002, p 1). The central idea of principal component analysis (PCA) is to reduce the dimensionality of a data set consisting of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This is achieved by transforming to a new set of variables, the principle components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. The italics are Jolliffes. PCA and EOF analysis is the same. 5.1.2 The Covariance Matrix One might say we traditionally looked upon a data set f(s,t) as a collection of time series of

length nt at each of ns locations. In Chapter 2 we described that after taking out a suitable reference { } from a data set f(s,t), usually the space dependent time-mean (or climatology), the covariance matrix Q can be formed with elements as given by (2.14): qij = f (si, t) f (sj , t) / nt t where si and sj are the i-th and j-th point (gridpoint or station) in space. The matrix Q is square, has dimension ns, is symmetric and consists of real numbers. The average of all q ii (the main diagonal) equals the space time variance (STV) as given in (2.16). The elements of Q have great appeal to a meteorological audience. Fig.4.1 featured two columns of the correlation version of Q in map form, the NAO and PNA spatial patterns, while Namias(1981) published all columns of Q (for seasonal mean 700 mb height data) in map form in an atlas. 5.1.3. The Alternative Covariance Matrix One might say with equal justification that we look upon f(s,t) alternatively as a collection of nt maps of size ns. The alternative covariance matrix Qa contains the covariance in space between two times ti and tj given as in (2.14a):

qaij = f (s, ti ) f (s, tj ) / ns s where the superscript a stands for alternative. Qa is square, symmetric and consists of real numbers, but the dimension is nt by nt , which frequently is much less than ns by ns , the dimension of Q. As long as the same reference {f} is removed from f(s,t) the average of the q aii over all i, i.e. the average of main diagonal elements of Qa, equals the space-time variance given in (2.16). The average of the main diagonal elements of Q a and Q are thus the same. The elements of Qa have apparently less appeal than those of Q (seen as PNA and NAO in Fig.4.1). It is only in such contexts as in analogues, see Chapter 7, that the elements of Q a have a clear interpretation. The qaij describe how (dis)similar two maps at times ti and tj are. When we talk throughout this text about reversing the role of time and space we mean using Qa instead of Q. The use of Q is more standard for explanatory purposes in most textbooks, while the use of Qa is more implicit,

or altogether invisible. For understanding it is important to see the EOF process both ways. 5.1.4 The covariance matrix: context The covariance matrix typically occurs in a multiple linear regression problem where f (s,t) are the predictors, and y(t) is a dummy predictand, 1 <= t <= nt . Here we first follow Wilks(1995; p368-369). A forecast of y (denoted as y*) is sought as follows: y*(t) = f (s, t) b (s) + constant, s (5.1) where b(s) is the set of weights to be determined. As long as the time mean of f was removed, the constant is zero. The residual U = t

{ y (t) - y*(t) }2 needs to be minimized. U / b(s) = 0 leads to the normal equations, see Eq 9.23 in Wilks(1995), given by: Q b = a, where Q is the covariance matrix and a and b are vectors of size ns. The elements of vector a consist of f (t , si) y(t) / nt. Since Q and a are known, b can be solved for, in principle. t --- continued ---- 5.1.4 The covariance matrix: context continued Note that Q is the same for any y. (Hence y is a dummy.) The above can be repeated alternatively for a dummy y(s) y*(s) = f(s, t) b(t)

(5.1a) where the elements of b are a function of time. This leads straightforwardly to matrix Q a. (5.1a) will also be the formal approach to constructed analogue, see chapter 7. Q and Qa occur in a wide range of linear prediction problems and Q and Q a depend only on f(s,t), here designated as the predictor data set. In the context of linear regression it is an advantage to have orthogonal predictors, because one can add one predictor after another and add information (variance) without overlap, i.e. new information not accounted for by other predictors. In such cases there is no need for backward/forward regression and one can reduce the total number of predictors in some rational way. We are thus interested in diagonalized versions of Q and Q a (and the linear transforms of f(s,t) underlying the diagonalized version of the two Qs). 5.1.5 EOF calculated through eigen-analysis of cov matrix In general a set of observed f(s,t) are not orthogonal,

i.e. f (si, t) f (sj , t) and f (s, ti ) f (s, tj ) are not zero for i j. Put another way: neither Q nor Qa are diagonal. Here some basic linear algebra can be called upon to diagonalize these matrices and transform the f (s,t) to become a set of uncorrelated or orthogonal predictors. For a square, symmetric and real matrix, like Q or Qa, this can be done easily, an important property of such matrices being that all eigenvalues are real and positive and the eigenvectors are orthogonal. The classical eigenproblem for matrix B can be stated: B em = m em (5.2) where e is the eigenvector and is the eigenvalue, and for this discussion B is either Q or Q a. The index m indicates there is a set of eigenvalues and vectors. Notice the non-uniqueness of (5.2) - any multiplication of em by a positive or negative constant still satisfies (5.2). Often it will be convenient to assume that the norm |e| is 1 for each m. Any symmetric real matrix B has these properties: 1) The ems are orthogonal

2) E-1 B E, where matrix E contains all em , results in a matrix with the elements m at the main diagonal, and all other elements zero. This is one obvious recipe to diagonalize B (but not the only recipe!). 3) all m > 0, m=1, ...M. Because of property 1 the em(s) are a basis, orthogonal in space, which can be used to express: f ( s, t) = m(t)em(s) (5.3) where the m(t) are calculated, or thought of, as projection coefficients, see Eq (2.6). But the m(t) are orthogonal by virtue of property #2. It is actually only the 2 nd step/property that is needed to construct orthogonal predictors in the context of Q. Here we thus have the very remarkable property of bi-orthogonality of EOFs - both m(t) and em(s) are an orthogonal set. With justification the m(t) can be looked upon as basis functions also, and (5.3) is also satisfied when the em(s) are calculated by projecting the data onto m(t).

We can diagonalize Qa in the same way, by calculating its eigenvectors. Now the transformed maps (linear combinations of original maps) become orthogonal due to step 2, and the transformed time series are a basis because of property 1. (Notation may be a bit confusing here, since, except for constants, the es will be s and vice versa, when using Q a instead of Q.) One may write: Q em = m em (5.2) Qa ema = ma ema (5.2a) such that the es are calculated as eigenvectors of Qa or Q. We then have f ( s, t) = m(t) em(s) (5.3) f ( s, t) = ema(t) m(s) (5.3a) where the s and s are obtained by projection, and the es are obtained as eigenvector. When ordered by EV, m = ma , and except for multiplicative constants m(s)=em(s) and m(t)=ema(t), so (5.3) alone suffices to describe EOF. Note that m(t) and em(s) cannot both be normed at the same time while satisfying (5.3).

This causes considerably confusion. In fact all one can reasonably expect is: f ( s, t) = m(t)/c em(s)*c (5.3b) where c is a constant (positive or negative). (5.3b) is consistent with both (5.3) and (5.3a). Neither the polarity, nor the norm is settled in an EOF procedure. The only unique parameter is m. Since there is only one set of bi-orthogonal functions, it follows that during the above procedure Q and Qa are simultaneously diagonalized, one explicitly, the other implicitly for free. It is thus advantageous in terms of computing time to choose the covariance matrix with 5.1.6 Explained variance EV The eigenvalues can be ordered: 1 > 2 > 3 ..... > M > 0. Moreover: M ns m = qii /ns = STV m=1 i=1

The m are thus a spectrum, descending by construction, and the sum of the eigenvalues equals the space time variance. Likewise M M m = qakk /nt = STV m=1 k=1 The eigenvalues for Q and Qa are the same and have the units of variance. The total number of eigenvalues, M, is at most the smaller of ns and nt In the context of Q one can also write: explained variance of mode m ( m) = 2m(t) /nt as long as |e|=1. Jargon: mode m explains m of STV or m / m *100. % EV 5.2 Examples next Fig.5.1 Display of four leading EOFs for seasonal

(JFM) mean 500 mb height. Shown are the maps and the time series. A postprocessing is applied, see Appendix I, such that the physical units (gpm) are in the time series, and the maps have norm=1. Contours every 0.2, starting contours +/- 0.1. Data source: NCEP Global Reanalysis. Period 19482005. Domain 20N-90N Fig.5.2. Same as Fig 5.1, but now daily data for all Decembers,

Januaries and Februaries during 1998-2002. Fig.5.3. Same as Fig 5.2, but now the SH. Seasonal EOF IN SH Fig.5.4 Display of four leading alternative EOT for seasonal (JFM) mean 500 mb height. Shown are the regression coefficient between the basepoint in time (1989 etc) and all other

years (timeseries) and the maps of 500mb height anomaly (geopotential meters) observed in 1989, 1955 etc . In the upper left for raw data, in the upper right after removal of the first EOT mode, lower left after removal of the first two modes. A postprocessing is applied, see Appendix I, such that the physical units (gpm) are in the time series, and the maps have norm=1. Contours every 0.2, starting contours +/- 0.1. Data source: NCEP Global Reanalysis. Period 1948-2005. Domain 20N-

(Time) DEV 25 20 EV(%) 15 10 5 0 48

51 54 57 60 63 66 69 72

75 78 81 Year-1900 84 87 90 93

96 99 102 105 108 111 Fig.5.5, the same as Fig 5.1, but obtained by starting the iteration method (see Appendix II) from alternative EOTs,

instead of regular EOT. Compared to Fig.5.1 only the polarity may have changed. Fig.5.1 Display of four leading EOFs for seasonal (JFM) mean 500 mb height. Shown are the maps and the time series. A postprocessing is applied, see Appendix I, such that the physical units (gpm) are in the time series, and the maps have norm=1. Contours every 0.2, starting contours +/- 0.1. Data

source: NCEP Global Reanalysis. Period 19482005. Domain 20N-90N 1948-2005 to 1948-2014 EOT-normal Q tells about Teleconnections Matrix Q with elements: qij= f(si,t)f(sj,t) t Q is diagonalized Qa is not diagonalized (1) is satisfied with m orthogonal

iteration Rotation EOF Discrete Data set f(s,t) 1 t nt ; 1 s ns Laudable goal: f(s,t)= m(t)em(s) (1) m Both Q and Qa Diagonalized (1) satisfied Both m and em orthogonal

m (em) is eigenvector of Qa(Q) Rotation Matrix Qa with elements: qija=f(s,ti)f(s,tj) s Qa tells about Analogues Fig.5.6: Summary of EOT/F procedures. iteration Arbitrary state iteration

EOT-alternative Q is not diagonalized Qa is diagonalized (1) is satisfied with em orthogonal EV as a function of mod JFM Z500 100 20 80 EV 15

10 5 0 eof E V -c u m u la tiv e 25 60 eot 40

eot-a 20 1 2 3 4 5 6 7 8 91011 12 13 14 15 16 17 18 19 20 21 22

23 24 25 0 mode number Fig 5.7. Explained Variance (EV) as a function of mode (m=1,25) for seasonal mean (JFM) Z500, 20N-90N, 1948-2005. Shown are both EV(m) (scale on the left, triangles) and cumulative EV(m) (scale on the right, squares). Red lines are for EOF, and blue and green for EOT and alternative EOT respectively. More examples Fig.5.8 Display of four leading EOFs for seasonal

(OND) mean SST. Shown are the maps on the left and the time series on the right. Contours every 1C, and a color scheme as indicated by the bar. Data source: NCEP Global Reanalysis. Period 1948-2004. Domain 45S-45N Fig.5.9 Display of four leading EOFs for seasonal (JFM) mean 500 mb streamfunction. Shown are the maps and the time series. A postprocessing is applied, see Appendix I, such that the

physical units (m*m/s) are in the time series, and the maps have norm=1. Contours every 0.2, starting contours +/- 0.1. Data source: NCEP Global Reanalysis. Period 1948-2005. Domain 20N-90N The prettiest EOF. simplifications

Why simplify? Rotated EOF EOT, EOT-alternative Surgical patches. Other Complex EOF Extended or joint EOF Cross-data-sets empirical orthogonal ., come listen April 23. Common misunderstandings EOF are eigen vectors EOF can only do standing patterns The 1st EOF escapes disadvantages of having to be orthogonal to the rest

EOF procedure forces La Nina to be exact opposite to El Nino Finally, how to calculate EOFs? Standard packages will follow the covariance matrix approach. This has limits, mainly CPU limits. How about doing 10**17 multiplications? Van den Dool et al(2000) describes an iteration which avoids filling the covariance matrix altogether, even if it uses properties of Q/Qa . This is useful and extremely simple. Especially useful for LARGE data sets. Significant Advance in Calculating

EOF From a Very Large Data set. Huug van den Dool [email protected] 36 Basics: f (s, t) = m m(t) em(s) (0) em(s) = t m(t) f (s, t) / t 2m(t) (1) m(t) = s em(s) f (s, t) / s e2m(s) (2) The above are orthogonality relationships. If we know m(t) and f(s,t), em(s) can be calculated trivially. If we know em(s) and

f(s,t), m(t) can be calculated trivially. This is the basis of the iteration. Randomly pick (or make) a time series 0(t), and stick into (1). This yields e0(t). Stick e0(t) into (2). This yields 1(t). This is one iteration. Etc. This generally converges to the first EOF 1(t), e1(s). CPU cost 2 units per iteration. freduced(s,t) = f(s,t) - 1(t) e1(s) and repeat. One finds mode#2. Etc. 37

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