SpatialVx (version 0.3)

saller: Feature-based Analysis of a Field (Image)

Description

Feature-based analysis of a field (image)

Usage

saller(x, d = NULL, distfun = "rdist", ...)
## S3 method for class 'saller':
print(x, ...)
## S3 method for class 'saller':
summary(object, ...)

Arguments

x

saller: x is a list object returned by FeatureFinder or other feature identification function that returns a list with components X.feats, Y.feats (themselves lists with owin class objects d

object

summary: object the returned by saller.

d

(optional) the SAL (saller) method requires division by the longest distance between two border points. If NULL, this is taken to be simply the length of the longest side.

distfun

Function with which to calculate centroid distances. Default uses straight Euclidean. To do great-circle distance, use rdist.earth and be sure that object has a loc attribute with lon/lat coordinates.

...

Optional arguments to distfun. Not used by print or summary.

Value

• saller returns a list with components:
• A
  numeric giving the amplitude component.
• L
  numeric giving the lcoation component.
• S
  numeric giving the structure component.
• L1,L2
  numeric giving the values that sum together to give L.
• print invisibly returns a named vector with S, A and L.

  summary does not return anything.

Details

saller: Computes S, A, and L of the SAL method introduced by Wernli et al. (2008).

References

Wernli, H., Paulat, M., Hagen, M. and Frei, C. (2008) SAL--A novel quality measure for the verification of quantitative precipitation forecasts. Mon. Wea. Rev., 136, 4470--4487, doi:10.1175/2008MWR2415.1.

See Also

centroid.owin, connected, tiles, tess, deltamm, make.SpatialVx

Examples

data(ExampleSpatialVxSet)

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

q <- mean( c(c(x[x>0]),c(xhat[xhat>0])), na.rm=TRUE)

hold <- make.SpatialVx(x, xhat, field.type="contrived", units="none",
    data.name=c("Example", "x", "xhat"))

hold2 <- FeatureFinder(hold, smoothpar=5, thresh=q)
plot(hold2)

look <- saller(hold2)   
summary(look)

data(pert000)
data(pert004)
data(ICPg240Locs)

hold <- make.SpatialVx(pert000, pert004, loc=ICPg240Locs,
    projection=TRUE, map=TRUE, loc.byrow = TRUE,
    field.type="Precipitation", units="mm/h",
    data.name=c("Perturbed ICP Cases", "pert000", "pert004"))

look <- FeatureFinder(hold, smoothpar=10.5)
summary(look)
plot(look)

saller(look)