Empirical Orthogonal Functions

2006 ◽  
Author(s):  
Huug van den Dool
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Natural Extension ◽  
Principal Component ◽  
Space Time ◽  
Empirical Orthogonal Functions ◽  
Point Of View ◽  
Empirical Modeling ◽  
Orthogonal Functions ◽  
Set Up

The purpose of this chapter 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. Jolliffe’s 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 space–time set-ups already alluded to in Equations (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 post-processing 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. EOF may or may not look like teleconnections. Therefore, as a diagnostic tool, EOFs may not always allow the interpretation some would wish. This has led to many proposed “simplifications” of the EOFs, which hopefully are more like teleconnections. However, regardless of physical interpretation, since EOFs are maximally efficient in retaining as much of the data set’s information as possible for as few degrees of freedom as possible they are ideally suited for empirical modeling. Indeed EOFs are an extremely popular tool these days.

scholarly journals On the Nonlinearity of Winter Northern Hemisphere Atmospheric Variability

2019 ◽  
Vol 76 (1) ◽  
pp. 333-356 ◽  
Author(s):  
A. Hannachi ◽  
W. Iqbal
Keyword(s):  
North Atlantic ◽  
Polar Vortex ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
The Arctic ◽  
Climate Change Signal ◽  
Two Dimensional ◽  
Orthogonal Functions ◽  
Local Flow ◽  
The North

Abstract Nonlinearity in the Northern Hemisphere’s wintertime atmospheric flow is investigated from both an intermediate-complexity model of the extratropics and reanalyses. A long simulation is obtained using a three-level quasigeostrophic model on the sphere. Kernel empirical orthogonal functions (EOFs), which help delineate complex structures, are used along with the local flow tendencies. Two fixed points are obtained, which are associated with strong bimodality in two-dimensional kernel principal component (PC) space, consistent with conceptual low-order dynamics. The regimes reflect zonal and blocked flows. The analysis is then extended to ERA-40 and JRA-55 using daily sea level pressure (SLP) and geopotential heights in the stratosphere (20 hPa) and troposphere (500 hPa). In the stratosphere, trimodality is obtained, representing disturbed, displaced, and undisturbed states of the winter polar vortex. In the troposphere, the probability density functions (PDFs), for both fields, within the two-dimensional (2D) kernel EOF space are strongly bimodal. The modes correspond broadly to opposite phases of the Arctic Oscillation with a signature of the negative North Atlantic Oscillation (NAO). Over the North Atlantic–European sector, a trimodal PDF is also obtained with two strong and one weak modes. The strong modes are associated, respectively, with the north (or +NAO) and south (or −NAO) positions of the eddy-driven jet stream. The third weak mode is interpreted as a transition path between the two positions. A climate change signal is also observed in the troposphere of the winter hemisphere, resulting in an increase (a decrease) in the frequency of the polar high (low), consistent with an increase of zonal flow frequency.

Mutual information applications to estimate the solar/ geomagnetic signatures in the drought indices in the Danube basin

2020 ◽  
Author(s):  
Ileana Mares ◽  
Venera Dobrica ◽  
Constantin Mares ◽  
Crisan Demetrescu
Keyword(s):  
Mutual Information ◽  
Drought Index ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Drought Indices ◽  
Drought Severity ◽  
Orthogonal Functions ◽  
Danube Basin ◽  
The Difference ◽  
Geomagnetic Activities

<p>The climatic condition for the dry or wet situations from 15 meteorological stations in the Danube basin has been evaluated using four indices: Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Weighted PDSI (WPLM) and Palmer Z-index (ZIND).</p><p>The overall temporal characteristic of the four indices has been analysed by means of the principal component of the Multivariate Empirical Orthogonal Functions decomposition (PC1-MEOF). Also, a simple drought index (TPPI) calculated as the difference between PC1 of the standardized temperature and precipitation, was considered.</p><p>To find the simultaneous influence of both solar and geomagnetic activities on drought indices in the Danube basin, the difference between synergistic and redundant components for each season was estimated, using the mutual information between the analyzed variables. The greater this difference is, the greater the simultaneous signature of the two variables in the drought indices is more significant, than by taking each of the two variables separately.</p><p>The solar activity was highlighted by Wolf numbers for the period 1901-2000 and for 1948-2000 by solar radio flux. For both periods the geomagnetic activity was quantified by the aa index.</p><p>The most significant results for the 100-year period were obtained for the autumn season for which the two predictors representing solar and geomagnetic activities, if considered simultaneously could be one of the causes that produce extreme hydroclimatic events. The analysis from 1948-2000 revealed that the simultaneous consideration of the two external factors is more significant in the summer and autumn time.</p>

Physical insights into principal component thermography

2020 ◽  
Vol 62 (5) ◽  
pp. 277-280 ◽  
Author(s):  
K Kaur ◽  
A Sharma ◽  
A Rani ◽  
V Kher ◽  
R Mulaveesala
Keyword(s):  
Principal Component ◽  
Steel Specimen ◽  
Vital Role ◽  
Empirical Orthogonal Functions ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Orthogonal Functions ◽  
Pulsed Thermography ◽  
Physical Insight ◽  
Non Destructive

Among widely used non-destructive testing (NDT) methods, infrared thermography (IRT) has gained importance due to its fast, whole-field, remote and quantitative inspection capabilities for the evaluation of various materials. Being fast and easy to implement, pulsed thermography (PT) plays a vital role in the infrared thermographic community. This paper provides a physical insight into the selection of empirical orthogonal functions obtained from principal component pulsed thermography for the detection of subsurface defects located inside a mild steel specimen.

scholarly journals A Comparison Study of EOF Techniques: Analysis of Nonstationary Data with Periodic Statistics

1999 ◽  
Vol 12 (1) ◽  
pp. 185-199 ◽  
Author(s):  
Kwang-Y. Kim ◽  
Qigang Wu
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Second Mode ◽  
Nonstationary Data ◽  
Cyclic Statistics ◽  
Surface Temperature Field ◽  
Climate Studies ◽  
Moving Patterns

Abstract Identification of independent physical/dynamical modes and corresponding principal component time series is an important aspect of climate studies for they serve as a tool for detecting and predicting climate changes. While there are a number of different eigen techniques their performance for identifying independent modes varies. Considered here are comparison tests of eight eigen techniques in identifying independent patterns from a dataset. A particular emphasis is given to cyclostationary processes such as deforming and moving patterns with cyclic statistics. Such processes are fairly common in climatology and geophysics. Two eigen techniques that are based on the cyclostationarity assumption—cyclostationary empirical orthogonal functions (EOFs) and periodically extended EOFs—perform better in identifying moving and deforming patterns than techniques based on the stationarity assumption. Application to a tropical Pacific surface temperature field indicates that the first dominant pattern and the corresponding principal component (PC) time series are consistent among different techniques. The second mode and the PC time series, however, are not very consistent from one another with hints of significant modal mixing and splitting in some of derived patterns. There also is a detailed difference of intraannual scale between PC time series of a stationary technique and those of a cyclostationary one. This may bear an important implication on the predictability of El Niño. Clearly there is a choice of eigen technique for improved predictability.

scholarly journals A METHODOLOGY TO SIMULATE MEDIUM TERM MORPHOLOGICAL CHANGES IN A PRACTICAL COMPUTING TIME

2011 ◽  
Vol 1 (32) ◽  
pp. 58
Author(s):  
Nestor Jimenez ◽  
Roberto Mayerle
Keyword(s):  
Baltic Sea ◽  
Morphological Changes ◽  
Morphological Evolution ◽  
Computing Time ◽  
Empirical Orthogonal Functions ◽  
Medium Term ◽  
Orthogonal Functions ◽  
Preliminary Results ◽  
Realistic Representation ◽  
Set Up

In this paper the assessment of the preliminary results of a methodology to enable predictions of medium-term morphodynamics accounting for the effects of storms is carried out. The methodology integrates the approaches based on a morphological acceleration factor and on the empirical orthogonal functions to account respectively for the morphological changes on the medium and short term. In a very simplified fashion, the effects of the storms are represented by a superposition of most relevant bathymetrical changes. The effectiveness of the methodology was assessed for a coastal stretch along the German Baltic Sea. The analysis of the simulations of morphodynamics for a period of 10 years showed that the method is able to predict volumetric changes along the coastal stretches reasonably well. However it fails to describe the spatial variation of the morphological changes near the coast. Sensitivity studies show also that the results are significantly affected by the set-up scheme of the methodology. Preliminary results during the assessment of the methodology gave clues about the evolution of the morphology of the German Baltic Sea coast. The methodology can be used as a practical tool for initial assessments of tendencies of morphological evolution. Obviously, in this investigation, the method proposed to account for to the storms is a simplified representation of the reality. In this regard, further research is needed to include a more realistic representation of the chronology taking into account their intensity.

scholarly journals Metastable Decomposition of High-Dimensional Meteorological Data with Gaps

2008 ◽  
Vol 65 (11) ◽  
pp. 3479-3496 ◽  
Author(s):  
Illia Horenko ◽  
Stamen I. Dolaptchiev ◽  
Alexey V. Eliseev ◽  
Igor I. Mokhov ◽  
Rupert Klein
Keyword(s):  
Markov Models ◽  
Transition Probabilities ◽  
Meteorological Data ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Metastable States ◽  
High Dimensional ◽  
Observation Data ◽  
Orthogonal Functions ◽  
Mean Values

Abstract This paper presents an extension of the recently developed method for simultaneous dimension reduction and metastability analysis of high-dimensional time series. The modified approach is based on a combination of ensembles of hidden Markov models (HMMs) with state-specific principal component analysis (PCA) in extended space (guaranteeing that the overall dynamics will be Markovian). The main advantage of the modified method is its ability to deal with the gaps in the high-dimensional observation data. The proposed method allows for (i) the separation of the data according to the metastable states, (ii) a hierarchical decomposition of these sets into metastable substates, and (iii) calculation of the state-specific extended empirical orthogonal functions simultaneously with identification of the underlying Markovian dynamics switching between those metastable substates. The authors discuss the introduced model assumptions, explain how the quality of the resulting reduced representation can be assessed, and show what kind of additional insight into the underlying dynamics such a reduced Markovian representation can give (e.g., in the form of transition probabilities, statistical weights, mean first exit times, and mean first passage times). The performance of the new method analyzing 500-hPa geopotential height fields [daily mean values from the 40-yr ECMWF Re-Analysis (ERA-40) dataset for a period of 44 winters] is demonstrated and the results are compared with information gained from a numerically expensive but assumption-free method (Wavelets–PCA), and the identified metastable states are interpreted w.r.t. the blocking events in the atmosphere.

Singular systems analysis as a moving-window spectral method

1998 ◽  
Vol 9 (1) ◽  
pp. 55-79 ◽  
Author(s):  
A. C. FOWLER ◽  
G. KEMBER
Keyword(s):  
Systems Analysis ◽  
Singular Systems ◽  
A Priori ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Moving Window ◽  
Window Length ◽  
Sampled Data ◽  
Orthogonal Functions ◽  
Local Filters

Singular Systems Analysis (SSA), or time domain Principal Component Analysis (PCA), is most appropriately analysed in terms of local, moving-window spectral analysis. The behaviour of Empirical Orthogonal Functions (EOF) of this theory are examined, for continuously sampled data, in the limits of large and small window length, and for centre or end projection. Filters obtained by projecting on to these EOFs are shown to approximate local, linear band pass filters, where the EOFs depend upon the correlation structure (or the power spectral density) of the signal and the window length. Power in the spectra is not generally conserved, and projection to the endpoints of a window may not converge to the underlying signal in the absence of noise. The filters are independent of the phase of the Fourier transform, and are therefore unable to distinguish dynamically between a signal and a surrogate (phase-randomized) transform of it. Iteration of such local filters using a prediction error-based stopping criterion can and does lead to improved results, but the choice of window length must be made a priori. Hence, we introduce an iterative local filter with the window length being determined as part of the filtering procedure. This involves the determination of the predictability of the projected time series, and hence allows SSA to be used in a genuinely nonlinear way.

scholarly journals Annual Cycle of Surface Longwave Radiation

2011 ◽  
Vol 50 (6) ◽  
pp. 1212-1224 ◽  
Author(s):  
Pamela E. Mlynczak ◽  
G. Louis Smith ◽  
Anne C. Wilber ◽  
Paul W. Stackhouse
Keyword(s):  
Annual Cycle ◽  
Water Cycle ◽  
Thermal Inertia ◽  
Longwave Radiation ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Orthogonal Functions ◽  
Annual Cycles

AbstractThe annual cycles of upward and downward longwave fluxes at the earth’s surface are investigated by use of the NASA Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget Dataset. Principal component analysis is used to quantify the annual cycles. Because of the immense difference between the heat capacity of land and ocean, the surface of the earth is partitioned into these two categories. Over land, the first principal component describes over 95% of the variance of the annual cycle of the upward and downward longwave fluxes. Over ocean the first term describes more than 87% of these annual cycles. Empirical orthogonal functions show the corresponding geographical distributions of these cycles. Phase-plane diagrams of the annual cycles of upward longwave fluxes as a function of net shortwave flux show the thermal inertia of land and ocean.

On the Nature of Zonal Jet EOFs

2006 ◽  
Vol 19 (24) ◽  
pp. 6409-6424 ◽  
Author(s):  
Adam H. Monahan ◽  
John C. Fyfe
Keyword(s):  
Time Series ◽  
Covariance Structure ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Physical Processes ◽  
Linear Combinations ◽  
Orthogonal Functions ◽  
The Mean ◽  
Systematic Perturbation ◽  
The Individual

Abstract Analytic results are obtained for the mean and covariance structure of an idealized zonal jet that fluctuates in strength, position, and width. Through a systematic perturbation analysis, the leading empirical orthogonal functions (EOFs) and principal component (PC) time series are obtained. These EOFs are built of linear combinations of basic patterns corresponding to monopole, dipole, and tripole structures. The analytic results demonstrate that in general the individual EOF modes cannot be interpreted in terms of individual physical processes. In particular, while the dipole EOF (similar to the leading EOF of the midlatitude zonal mean zonal wind) describes fluctuations in jet position to leading order, its time series also contains contributions from fluctuations in strength and width. No simple interpretations of the other EOFs in terms of strength, position, or width fluctuations are possible. Implications of these results for the use of EOF analysis to diagnose physical processes of variability are discussed.

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