SpatialVx (version 0.1-2)

pphindcast2d: Function to perform the practically perfect hindcast neighborhood verification method

Description

Function to perform the practically perfect hindcast neighborhood verification method. Finds the optimal threhsold, Pthresh, and calculates the desired statistic for that threshold.

Usage

pphindcast2d(obj, which.score = "ets", verbose = FALSE, ...)

Arguments

obj

A list object returned by the hoods2dPrep function.

which.score

character stating which verification score is to be used. Must be one that is accepted by vxstats.

verbose

logical, should progress information be printed ot the screen?

...

optional arguments to the optim function. May not include lower, upper or method as these are hard coded into the function.

Value

• A list object is returned with components:
• which.score
  value of which.score, same as the argument passed in.
• Pthresh
  l X q matrix giving the value of Pthresh applied at each level (rows) and threshold (columns).
• values
  l X q matrix giving the value of which.score found for each level (rows) and threshold (columns).

Warning

The value Pthresh is optimized under the assumption that larger values of which.score are better.

Details

The practically perfect hindcast method is described in Ebert (2008). Using a similar notation as that described therein (and in the help page for hoods2d), the method is a SO-NF approach that first compares the observed binary field (obtained from the trheshold(s) provided by obj from hoods2dPrep), Ix, with the smoothed binary field, s. This smoothed binary field is thresholded by Pthresh to obtain a new binary field. The value of Pthresh that maximizes the verification score (provided by the which.score argument) is then used to compare Ix with s, the binary forecast field obtained by thresholding the smoothed binary forecast field Iy using the value of Pthresh found above. The verification statistic determined by which.score is calculated between Ix and s.

References

Ebert EE, 2008. Fuzzy verification of high resolution gridded forecasts: A review and proposed framework. Meteorol. Appl., 15, 51-64. DOI: 10.1002/met.25 Available at http://www.ecmwf.int/newsevents/meetings/workshops/2007/jwgv/METspecialissueemail.pdf

See Also

hoods2d, kernel2dsmooth, vxstats, hoods2dPlot, optim

Examples

x <- y <- matrix( 0, 50, 50)
x[ sample(1:50,10), sample(1:50,10)] <- rexp( 100, 0.25)
y[ sample(1:50,20), sample(1:50,20)] <- rexp( 400)
hold <- hoods2dPrep("y", "x", thresholds=c(0.1, 0.5), levels=c(1, 3))
look <- pphindcast2d( hold)
look
data(geom001)
data(geom000)
data(ICPg240Locs)
hold <- hoods2dPrep( "geom001", "geom000", thresholds=c(0.01,50.01), levels=c(1, 3, 65), loc=ICPg240Locs, units="in/100")
look <- pphindcast2d( hold, verbose=TRUE)
par( mfrow=c(1,2))
hoods2dPlot( look$values, args=list( thresholds=hold$thresholds, units="in/100", levels=hold$levels), main="Gilbert Skill Score")