EOF: Empirical Orthogonal Functions (EOFs).

In metno/esd: Climate analysis and empirical-statistical downscaling (ESD) package for monthly and daily data

Empirical Orthogonal Functions (EOFs).

Description

Computes EOFs (a type of principal component analysis) for combinations of data sets, typically from gridded data, reanalysis and climate models results.

Usage

EOF(
  X,
  ...,
  it = NULL,
  is = NULL,
  n = 20,
  lon = NULL,
  lat = NULL,
  verbose = FALSE,
  anomaly = TRUE
)

Arguments

X	a 'field' or 'pca' object
...	additional arguments
it	see subset
is	Spatial subsetting - see subset.eof
n	number of EOFs
lon	set longitude range - see t2m.NCEP
lat	set latitude range
verbose	TRUE - clutter the screen with messages
anomaly	When TRUE, subtract the mean before SVD.

Details

[ref: Benestad (2001), "A comparison between two empirical downscaling strategies", Int. J. Climatology, vol 21, Issue 13, pp.1645-1668. DOI 10.1002/joc.703]. and mixFields prepares for mixed-field EOF analysis [ref. Bretherton et al. (1992) "An Intercomparison of Methods for finding Coupled Patterns in Climate Data", J. Climate, vol 5, 541-560; Benestad et al. (2002), "Empirically downscaled temperature scenarios for Svalbard", Atm. Sci. Lett., doi.10.1006/asle.2002.0051].

Uncertainty estimates are computed according to North et al. (1982), "Sampling Errors in the Estimation of Empirical Orthogonal Functions", Mon. Weather Rev., vol 110, 699-706.

The EOFs are based on svd.

See the course notes from Environmental statistics for climate researchers http://www.gfi.uib.no/~nilsg/kurs/notes/course.html for a discussion on EOF analysis.

The method PCA is similar to EOF, but designed for parallel station series (e.g. grouped together with merge). PCA does not assume gridded values and hence does not weigth according to grid area. PCA is useful for downscaling where the spatial covariance/coherence is important, e.u involving different variables from same site, same variable from different sites, or a mix between these. For instance, PCA can be applied to the two wind components from a specific site and hence extract the most important wind directions/speeds. PCA.matrix is just a wrapper function for svd that makes sure that the dimensions of the input are in order.

Value

File containing an 'eof' object which is based on the 'zoo' class.

Author(s)

R.E. Benestad

See Also

as.eof

Examples

# Simple EOF for annual mean SST:
sst <- sst.NCEP(lon=c(-90,20),lat=c(0,70))
SST <- annual(sst, FUN="mean", nmin=12)
eof.sst <- EOF(SST)
plot(eof.sst)

# EOF of July SST:
eof.sst7 <- EOF(sst,it="Jul")
plot(eof.sst7)

# common EOF for model
# Get some sample data, extract regions:
GCM <- t2m.NorESM.M()
gcm <- subset(GCM,is=list(lon=c(-50,60),lat=c(30,70)))
t2m.dnmi <- t2m.DNMI()
dnmi <- subset(t2m.dnmi,is=list(lon=c(-50,60),lat=c(30,70)))

OBS <- annual(dnmi, FUN="mean", nmin=12)
GCM <- annual(gcm, FUN="mean", nmin=12)
OBSGCM <- combine(OBS,GCM,dimension='time')

ceof <- EOF(OBSGCM)
plot(ceof)

# Example for using PCA in downscaling
## nacd <- station(src='nacd')
## X <- annual(nacd)
X <- station(src='nacd')
nv <- function(x) sum(is.finite(x))
ok <- (1:dim(X)[2])[apply(X,2,nv) == dim(X)[1]]
X <- subset(X,is=ok)
pca <- PCA(X)
map(pca)

slp <- slp.NCEP(lon=c(-20,30),lat=c(30,70))
eof <- EOF(slp,it="Jan")
ds <- DS(pca,eof)
# ds is a PCA-object
plot(ds)

# Recover the station data:
Z <- as.station(pca)
plot(Z,plot.type='multiple')

metno/esd documentation built on May 9, 2024, 11:21 p.m.