SpatialVx (version 0.1-5)

griddedVgram: Variograms for a Gridded Verification Set

Description

Find (and plot) variograms for each field in a gridded verification set.

Usage

griddedVgram(object, zero.in = TRUE, zero.out = TRUE, ...)
## S3 method for class 'griddedVgram':
plot(x, ...)

Arguments

object

any list object from any SpatialVx object class containing information on the verification set (e.g., from FeatureSuitePrep, hoods2dPrep, etc.).

zero.in,zero.out

logical, should the variogram be calculated over the entire field (zero.in), and/or over only the non-zero values (zero.out)?

x

list object as returned by griddedVgram.

...

In the case of griddedVgram, these are optional arguments to the vgram.matrix function from package fields. In the case of plot.griddedVgram, these are optional arguments to plot.vgram.matrix

Value

• A list object containing the entire list passed in by the object argument, and components:
• Vx.cgram.matrix, Fcst.vgram.matrix
  list objects as returned by vgram.matrix containing the variogram information for each field.
• No value is returned by plot.griddedVgram, plots are created showing the empirical variogram (circles), along with directional empirical variograms (dots), and the variogram by direction (image plot).

Details

Here, the terms semi-variogram and variogram are used interchangeably.

This is a simple wrapper function to vgram.matrix (entire field) from fields and/or variogram.matrix (non-zero grid points only) for finding the variogram between two gridded fields. It calls this function for each of two fields in a verification set. This function allows one to do the diagnostic analysis proposed in Marzban and Sangathe (2009).

References

Marzban, C. and Sandgathe, S. (2009) Verification with variograms. Wea. Forecasting, 24 (4), 1102--1120, doi:10.1175/2009WAF2222122.1.

See Also

vgram.matrix, FeatureSuitePrep, hoods2dPrep, locmeasures2dPrep

Examples

data(ExampleSpatialVxSet)

x <- ExampleSpatialVxSet$vx
xhat <- ExampleSpatialVxSet$fcst

hold <- FeatureSuitePrep("xhat", "x")
res <- griddedVgram(hold,R=8)
plot(res)

data(pert004)
data(pert000)
hold <- FeatureSuitePrep("pert004","pert000")
res <- griddedVgram(hold, R=8)
plot(res)