Field Significance Method of Elmore et al. (2006)

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Description

Apply field significance method of Elmore et al. (2006).

Usage

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spatbiasFS(X, Y, loc = NULL, block.length = NULL, alpha.boot = 0.05, field.sig = 0.05,
    bootR = 1000, ntrials = 1000, verbose = FALSE)

## S3 method for class 'spatbiasFS'
summary(object, ...)

## S3 method for class 'spatbiasFS'
plot(x, ...)

Arguments

X,Y

m by n matrices giving the verification and forecast fields, resp., for each of m time points (rows) and n locations (columns).

x,object

list object as returned by spatbiasFS.

loc

optional (for subsequent plotting) n by 2 matrix giving the lon/lat coordinates for the locations.

block.length

numeric giving the block length to be used n the block bootstrap algorithm. If NULL, floor(sqrt(n)) is used.

alpha.boot

numeric between 0 and 1 giving the confidence level desired for the bootstrap algorithm.

field.sig

numeric between 0 and 1 giving the desired field significance level.

bootR

numeric integer giving the number of bootstrap replications to use.

ntrials

numeric integer giving the number of Monte Carol iterations to use.

verbose

logical, should progress information be printed to the screen?

...

not used.

Details

See Elmore et al. (2006) for details.

Value

A list object with components:

data.name

character vector giving the name of the verification and forecast spatio-temporal fields used, and the associated location object (if not NULL).

block.boot.results

object of class LocSig

sig.results

list object containing information about the significance of the results.

field.significance,alpha.boot

field significance level and bootstrap CI level as input by field.sig alpha.boot arguments.

bootR,ntrials

same as arguments above.

Author(s)

Eric Gilleland and Kimberly L. Elmore

References

Elmore, K. L., Baldwin, M. E. and Schultz, D. M. (2006) Field significance revisited: Spatial bias errors in forecasts as applied to the Eta model. Mon. Wea. Rev., 134, 519–531.

See Also

MCdof, LocSig, tsboot

Examples

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data(GFSNAMfcstEx)
data(GFSNAMobsEx)
data(GFSNAMlocEx)
id <- GFSNAMlocEx[,"Lon"] >=-95 & GFSNAMlocEx[,"Lon"] <= -75 & GFSNAMlocEx[,"Lat"] <= 32
loc <- GFSNAMlocEx[id,]
GFSobsSub <- GFSNAMobsEx[,id]
GFSfcstSub <- GFSNAMfcstEx[,id]
look <- spatbiasFS(GFSobsSub, GFSfcstSub, loc=loc, bootR=500, ntrials=500)
plot(look)
summary(look)

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