SpatialVx (version 0.1-2)

upscale2d: Perform upscaling neighborhood verification on a 2-d verification set

Description

Perform upscaling neighborhood veriication on a 2-d verification set.

Usage

upscale2d(object, thresholds = NULL, verbose = FALSE)
## S3 method for class 'upscale2d':
plot(x, ...)

Arguments

object

list object output from the hoods2dPrep function.

thresholds

(optional) alternative thresholds to use on the upscaled fields. If NULL, the thresholds from the hoods2dPrep object are used.

verbose

logical, should progress information be printed to the screen?

x

list object of class "upscale2d" as returned by upscale2d.

...

optional arguments to the image.plot function from package fields.

Value

• upscale2d returns a list of class "upscale2d" with components:
• rmse
  numeric vector giving the root mean square error for each neighborhood size provided by object.
• bias,ts,ets
  numeric matrices giving the frequency bias, ts and ets for each neighborhood size (rows) and threshold (columns).

Details

Upscaling is performed via neighborhood smoothing. Here, a boxcar kernel is convolved (using the convolution theorem with FFT's) to obtain an average over the nearest n^2 grid squares at each grid point. This is performed on the raw forecast and verification fields. The root mean square error (RMSE) is taken for each threshold (Yates et al., 2006; Ebert, 2008). Further, binary fields are obtained for each smoothed field via thresholding, and frequency bias, threat score ts) and equitable threat score (ets) are calculated (Zepeda-Arce et al., 2000; Ebert, 2008).

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 Available at http://www.ecmwf.int/newsevents/meetings/workshops/2007/jwgv/METspecialissueemail.pdf

Yates, E., S. Anquetin, V. Ducrocq, J.-D. Creutin, D. Ricard, and K. Chancibault, 2006: Point and areal validation of forecast precipitation fields. Meteorol. Appl., 13, 1--20.

Zepeda-Arce, J., E. Foufoula-Georgiou, K. K. Droegemeier, 2000: Space-time rainfall organization and its role in validating quantitative precipitation forecasts. J. Geophys. Res., 105(D8), 10,129--10,146.

See Also

hoods2d, hoods2dPrep, kernel2dsmooth, kernel2dmeitsjer, fft

Examples

x <- matrix( 0, 50, 50)
x[ sample(1:50,10), sample(1:50,10)] <- rexp( 100, 0.25)
y <- kernel2dsmooth( x, kernel.type="disk", r=6.5)
x <- kernel2dsmooth( x, kernel.type="gauss", nx=50, ny=50, sigma=3.5)
hold <- hoods2dPrep("y", "x", thresholds=c(1e-8, 0.05, 0.1), levels=c(1, 3, 20))
look <- upscale2d( hold, thresholds=seq(0.01,1,,5))
look
plot( look)

plot( look)

data(geom001)
data(geom000)
data(ICPg240Locs)
hold <- hoods2dPrep( "geom001", "geom000", thresholds=c(0.01,50.01), levels=c(1, 3, 9, 17, 33, 65, 129, 257), loc=ICPg240Locs, units="in/100")
look <- upscale2d(hold, thresholds=c(1e-8, 0.1, 0.5, 45, 50.1), verbose=TRUE)
plot( look) # Might want to use 'pdf' to print these out so that all of them can be observed.
data(pert004)
data(pert000)
hold <- hoods2dPrep( "pert004", "pert000", thresholds=c(1,10,50), levels=c(1, 3, 17, 33, 65, 129, 257), loc=ICPg240Locs, units="mm/h")
look <- upscale2d( hold, thresholds=c(1e-8, 3:5, 45, 50.1), verbose=TRUE)
plot( look)