verification (version 1.44)

fss: Fractional Skill Score

Description

Calculates the fractional skill score for spatial forecasts and spatial observations.

Usage

fss(obs, pred,w = 0, FUN = mean, ...)

Value

Return the fraction skill score.

Arguments

obs

A matrix of binomial observed values.

pred

A matrix of binomial forecasted values

w

Box width. When w = 0, each pixel is considered alone. w = 2 creates a box with a length of 5 units.

FUN

Function to be applied to each subgrid. By default, the mean of each box is used to calculate the fraction of each subgrid.

...

Optional arguments

Author

Matt Pocernich

References

Roberts, N.M and H.W. Lean: 2008: Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events. Monthly Weather Review 136, 78-97.

Examples

grid<- list( x= seq( 0,5,,100), y= seq(0,5,,100)) 
obj<-Exp.image.cov( grid=grid, theta=.5, setup=TRUE)
look<- sim.rf( obj)
look[look < 0] <- 0

look2 <- sim.rf( obj)
look2[look2<0] <- 0

fss(look, look2, w=5)


if (FALSE) {
#  The following example replicates Figure 4 in Roberts and Lean (2008).
####      examples

LAG <- c(1,3,11,21)
box.radius <- seq(0,24,2)

OUT <- matrix(NA, nrow = length(box.radius), ncol = length(LAG) )

for(w in 1:4){

FRCS <- OBS <- matrix(0, nrow = 100, ncol = 100)

obs.id        <- 50
OBS[,obs.id]  <- 1

FRCS[, obs.id + LAG[w]] <- 1

for(i in 1:length(box.radius)){

OUT[i, w] <- fss(obs = OBS, pred = FRCS, w = box.radius[i] )

} ### close i
} ### close w

b <- mean(OBS) ## base rate

fss.uniform  <- 0.5 + b/2
fss.random   <- b

matplot(OUT, ylim = c(0,1), ylab = "FSS", xlab = "grid squares", 
type = "b", lty = 1, axes = FALSE , lwd = 2)

abline(h = c(fss.uniform, fss.random), lty = 2)  
axis(2)
box()
axis(1, at = 1:length(box.radius), lab = 2*box.radius + 1)
grid()

legend("bottomright", legend = LAG, col = 1:4, pch = as.character(1:4), 
 title = "Spatial Lag", inset = 0.02, lwd = 2 )
}