ProjectField {s2dv}

Project anomalies onto modes of variability

Description

Project anomalies onto modes of variability to get the temporal evolution of the EOF mode selected. It returns principal components (PCs) by area-weighted projection onto EOF pattern (from EOF()) or REOF pattern (from REOF() or EuroAtlanticTC()). The calculation removes NA and returns NA if the whole spatial pattern is NA.

Usage

ProjectField(
  ano,
  eof,
  time_dim = "sdate",
  space_dim = c("lat", "lon"),
  mode = NULL,
  ncores = NULL
)

Arguments

ano

A numerical array of anomalies with named dimensions. The dimensions must have at least 'time_dim' and 'space_dim'. It can be generated by Ano().

eof

A list that contains at least 'EOFs' or 'REOFs' and 'wght', which are both arrays. 'EOFs' or 'REOFs' must have dimensions 'mode' and 'space_dim' at least. 'wght' has dimensions space_dim. It can be generated by EOF() or REOF().

time_dim

A character string indicating the name of the time dimension of 'ano'. The default value is 'sdate'.

space_dim

A vector of two character strings. The first is the dimension name of latitude of 'ano' and the second is the dimension name of longitude of 'ano'. The default value is c('lat', 'lon').

mode

An integer of the variability mode number in the EOF to be projected on. The default value is NULL, which means all the modes of 'eof' is calculated.

ncores

An integer indicating the number of cores to use for parallel computation. The default value is NULL.

Value

A numerical array of the principal components in the verification format. The dimensions are the same as 'ano' except 'space_dim'.

See Also

EOF, NAO, PlotBoxWhisker

Examples

ano <- Ano_CrossValid(sampleData$mod, sampleData$obs)
eof_exp <- EOF(ano$exp, sampleData$lat, sampleData$lon) 
eof_obs <- EOF(ano$obs, sampleData$lat, sampleData$lon) 
mode1_exp <- ProjectField(ano$exp, eof_exp, mode = 1)
mode1_obs <- ProjectField(ano$obs, eof_obs, mode = 1)

## Not run: 
 # Plot the forecast and the observation of the first mode for the last year 
 # of forecast
 sdate_dim_length <- dim(mode1_obs)['sdate']
 plot(mode1_obs[sdate_dim_length, 1, 1, ], type = "l", ylim = c(-1, 1), 
      lwd = 2)
 for (i in 1:dim(mode1_exp)['member']) {
   par(new = TRUE)
   plot(mode1_exp[sdate_dim_length, 1, i, ], type = "l", col = rainbow(10)[i], 
        ylim = c(-15000, 15000))
 }

## End(Not run)