eof.dmc {clim.pact} | R Documentation |
Common EOFs for daily December-February 2-meter temperature (T(2m)) and sea level pressure (SLP).
data(eof.dmc)
EOF | EOF patterns. |
W | Eigen values. |
PC | Principal components of common PCA. |
n.fld | Number of different predictors (see mixFields). |
tot.var | Sum of all W squared. |
id.t | Time labels for the fields (see catFields) - used in DS. |
id.x | Spatial labels for the fields (see mixFields) - used in plotEOF. |
id.lon | Spatial labels for the fields (see mixFields) - used in plotEOF. |
id.lat | Spatial labels for the fields (see mixFields) - used in plotEOF. |
region | Describes the region analysed. |
tim | Time information (usually redundant). |
lon | Longitudes associated with EOF patterns. |
lat | Latitudes associated with EOF patterns. |
var.eof | Fractional variances associated with EOF patterns. |
yy | years. |
mm | months. |
dd | days. |
v.name | Name of element. |
c.mon | Month-season information. |
f.name | File name of original data. |
The common EOF was produced using EOF
, with the combined
December-February (DJF) 2-meter air temperature data field from European
Centre for Medium-Range Weather Forecasts (ECMWF; UK) reanalysis ( see URL:
http://www.ecmwf.int/) and HIRHAM dynamically dowwnscaled
scenarios from the ECHAM4-GSDIO scenario (Max-Planck Institute for Meteorology, Hamburg, Germany; URL: http://www.mpimet.mpg.de/). The region is 5E - 25E, 58N - 65N.
Reference to methodology: R.E. Benestad (2001), "A comparison between two empirical downscaling strategies", Int. J. Climatology, vol 210, pp.1645-1668. [DOI 10.1002/joc.703].
library(clim.pact) ## Not run: x.1.dm<-retrieve.nc("/data1/era15/ERA-15_t2m.nc",x.rng=c(5,25),y.rng=c(58,65)) X.1.dm<-retrieve.nc("/data1/hirham/T2M_198001-199912.nc",x.rng=c(5,25), y.rng=c(58,65)) Y.1.dm<-retrieve.nc("/data1/hirham/T2M_203001-204912.nc",x.rng=c(5,25), y.rng=c(58,65)) Y.1.dm$yy <- Y.1.dm$yy + 50 # It is important that demean=FALSE when concatinating the two time slices # from the model simulations, if a study of climate change is the objective. xX.1.dm <- catFields(X.1.dm,Y.1.dm,demean=FALSE) xX.1.dm <- catFields(x.1.dm,xX.1.dm) x.2.dm<-retrieve.nc("/data1/era15/ERA-15_slp.nc",x.rng=c(5,25),y.rng=c(58,65)) X.2.dm<-retrieve.nc("/data1/hirham/PSL_198001-199912.nc",x.rng=c(5,25), y.rng=c(58,65)) Y.2.dm<-retrieve.nc("/data1/hirham/PSL_203001-204912.nc",x.rng=c(5,25), y.rng=c(58,65)) Y.2.dm$yy <- Y.2.dm$yy + 50 # It is important that demean=FALSE when concatinating the two time slices # from the model simulations, if a study of climate change is the objective. xX.2.dm <- catFields(X.2.dm,Y.2.dm,demean=FALSE) xX.2.dm <- catFields(x.2.dm,xX.2.dm) xX.dm <- mix.fields(xX.1.dm,xX.2.dm,mon=1) eof.dmc <- eof(xX.dm,mon=1) ## End(Not run) # To read the data: data(eof.dmc)