SpatialVx (version 1.0-2)

fss2dfun: Various Verification Statistics on Possibly Neighborhood-Smoothed Fields.

Description

Functions to calculate various verification statistics on possibly neighborhood smoothed fields. Used by hoods2d, but can be called on their own.

Usage

fss2dfun(sPy, sPx, subset = NULL, verbose = FALSE)
fuzzyjoint2dfun(sPy, sPx, subset = NULL)
MinCvg2dfun(sIy, sIx, subset = NULL)
multicon2dfun(sIy, Ix, subset = NULL)
pragmatic2dfun(sPy, Ix, mIx = NULL, subset = NULL)
upscale2dfun(sYy, sYx, threshold = NULL, which.stats = c("rmse",
                 "bias", "ts", "ets"), rule = ">=", subset = NULL)

Value

In the case of fss2dfun, a single numeric giving the FSS value is returned. In the other cases, list objects are returned with one or more of the following components, depending on the particular function.

fuzzy

fuzzyjoint2dfun returns a list with this list as one component. The list component fuzzy has the components: pod, far and ets.

joint

fuzzyjoint2dfun returns a list with this list as one component. The list component joint has the components: pod, far and ets.

pod

numeric giving the probability of detection, or hit rate.

far

numeric giving the false alarm ratio.

ets

numeric giving the equitable threat score, or Gilbert Skill Score.

f

numeric giving the false alarm rate.

hk

numeric giving the Hanssen-Kuipers statistic.

bs

Brier Score

bss

Brier Skill Score. The pragmatic2dfun returns the bs and bss values. The Brier Skill Score here uses the mean square error between the base rate and the Ix field as the reference forecast.

ts

numeric giving the threat score.

bias

numeric giving the frequency bias.

Arguments

sPy

n by m matrix giving a smoothed binary forecast field.

sPx

n by m matrix giving a smoothed binary observed field.

sIy

n by m matrix giving a binary forecast field.

sIx

n by m matrix giving a binary observed field (the s indicates that the binary field is obtained from a smoothed field).

Ix

n by m matrix giving a binary observed field.

mIx

(optional) single numeric giving the base rate. If NULL, this will be calculated by the function. Simply a computation saving step if this has already been calculated.

sYy

n by m matrix giving a smoothed forecast field.

sYx

n by m matrix giving a smoothed observed field.

threshold

(optional) numeric vector of length 2 giving the threshold over which to calculate the verification statistics: bias, ts and ets. If NULL, only the rmse will be calculated.

which.stats

character vector naming which statistic(s) should be caluclated for upscale2dfun.

subset

(optional) numeric indicating over which points the summary scores should be calculated. If NULL, all of the points are used.

rule

character string giving the sort of thresholding process desired. See the help file for thresholder for more information.

verbose

logical, should progress information be printed to the screen?

Author

Eric Gilleland

Details

These are modular functions that calculate the neighborhood smoothing method statistics in spatial forecast verification (see, e.g., Ebert, 2008, 2009; Gilleland et al., 2009, 2010; Roberts and Lean,2008). These functions take fields that have already had the neighborhood smoothing applied (e.g., using kernele2d) when appropriate. They are called by hoods2d, so need not be called by the user, but they can be.

References

Ebert, E. E. (2008) Fuzzy verification of high resolution gridded forecasts: A review and proposed framework. Meteorol. Appl., 15, 51--64. doi:10.1002/met.25

Ebert, E. E. (2009) Neighborhood verification: A strategy for rewarding close forecasts. Wea. Forecasting, 24, 1498--1510, doi:10.1175/2009WAF2222251.1.

Gilleland, E., Ahijevych, D., Brown, B. G., Casati, B. and Ebert, E. E. (2009) Intercomparison of Spatial Forecast Verification Methods. Wea. Forecasting, 24, 1416--1430, doi:10.1175/2009WAF2222269.1.

Gilleland, E., Ahijevych, D. A., Brown, B. G. and Ebert, E. E. (2010) Verifying Forecasts Spatially. Bull. Amer. Meteor. Soc., October, 1365--1373.

Roberts, N. M. and Lean, H. W. (2008) Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78--97. doi:10.1175/2007MWR2123.1.

See Also

hoods2d,kernel2dsmooth,vxstats, thresholder

Examples

x <- y <- matrix( 0, 100, 100)
x[ sample(1:100, 10), sample(1:100, 10)] <- 1
y[ sample(1:100, 20), sample(1:100, 20)] <- 1
Px <- kernel2dsmooth( x, kernel.type="boxcar", n=9, xdim=c(100, 100))
Py <- kernel2dsmooth( y, kernel.type="boxcar", n=9, xdim=c(100, 100))
par( mfrow=c(2,2))
image( x, col=c("grey", "darkblue"), main="Simulated Observed Events")
image( y, col=c("grey", "darkblue"), main="Simulated Forecast Events")
image( Px, col=c("grey", tim.colors(256)), main="Forecast Event Frequencies (9 nearest neighbors)")
image( Py, col=c("grey", tim.colors(256)), main="Smoothed Observed Events (9 nearest neighbors)")
fss2dfun( Py, Px)