Test cases used for the ICP. In particular, those actually analyzed in the special collection of the journal, Weather and Forecasting. Includes the nine “real” cases, five simple geometric cases, and the seven perturbed “real” cases.
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The format is: num [1:601, 1:501] 0 0 0 0 0 0 0 0 0 0 ...
The format is: num [1:301101, 1:2] -110 -110 -110 -110 -110 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:2] "lon" "lat"
One of the nine ICP “real” cases is from one version of the Weather Research Forecast (WRF) model denoted
wrf4ncar (see Kain et al. 2008; Ahijevych et al., 2009 for complete details), and the corresponding “observed” field is stage II reanalyses denoted here by “obs”. The model is a 24-h forecast so that the valid time is for the next day (e.g.,
obs0531 corresponds with
These data are a subset from the 2005 Spring Program of the Storm Prediction Center/National Severe Storms Laboratory (SPC/NSSL, cf. Weiss et al., 2005; Kain et al., 2008). Units for the real cases are in mm/h, and are on the NCEP g240 grid (~4-km resolution) with 601 X 501 grid points. Both SPC and NSSL should be cited as sources for these cases, as well as Weiss et al. (2005) and possibly also Kain et al. (2008). The data were made available to the ICP by M. E. Baldwin.
The five geometric cases are simple ellipses (each with two intensities) that are compared against the verification case (
geom000) on the same NCEP g240 grid as the nine real cases. See Ahijevych et al. (2009) for complete details. Case
geom001 is exactly the same as
geom000, but is displaced 50 grid points to the right (i.e., ~200 km too far east). Case
geom002 is also identical to
geom000, but displaced 200 grid points to the right. case
geom003 is displaced 125 grid points to the right, and is also too big 9i.e., has a spatial extent, or coverage, bias). Case
geom004 is also displaced 125 grid points to the right, but also has a different orientation (note, however, that it is not a true rotation of
geom005 is displaced 125 grid points to the right, and has a huge spatial extent bias. This last case is also the only one that actually overlaps with
geom000, and therefore may be regarded by some as the best case. It is certainly the case that comes out on top by the traditional verification statistics that are calculated on a grid point by grid point basis. Ahijevych et al. (2009) should be cited if these geometric cases are used for publications, etc.
The longitude and latitude information for each grid (the NCEP g240 grid) is contained in the
Other data sets for the ICP can be obtained from the ICP web site (https://ral.ucar.edu/projects/icp/). MesoVICT data sets are also available there. All of the ICP test cases used to be available in this package, but had to be removed because of space concerns on CRAN.
Ahijevych, D., Gilleland, E., Brown, B. G. and Ebert, E. E. (2009) Application of spatial verification methods to idealized and NWP gridded precipitation forecasts. Wea. Forecasting, 24 (6), 1485–1497.
Kain, J. S., Weiss, S. J., Bright, D. R., Baldwin, M. E. Levit, J. J. Carbin, G. W. Schwartz, C. S. Weisman, M. L. Droegemeier, K. K. Weber, and D. B. Thomas, K. W. (2008) Some Practical Considerations Regarding Horizontal Resolution in the First Generation of Operational Convection-Allowing NWP. Wea. Forecasting, 23, 931–952.
Weiss, S., Kain, J. Levit, J. Baldwin, M. E., Bright, D. Carbin, G. and Hart, J. (2005) NOAA Hazardous Weather Testbed. SPC/NSSL Spring Program 2005 Program Overview and Operations Plan. 61pp.
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## Not run: data( "obs0601" ) data( "wrf4ncar0531" ) data( "ICPg240Locs" ) ## Plot verification sets with a map. ## Two different methods. # First way does not preserve projections. locr <- c( range( ICPg240Locs[,1]), range( ICPg240Locs[,2])) zl <- range( c( c(obs0601), c( wrf4ncar0531) ) ) par( mfrow=c(2,1), mar=rep(0.1,4)) image( obs0601, axes=FALSE, col=c("grey", tim.colors(256)), zlim=zl) par( usr=locr) # if( map.available) map( add=TRUE, database="state") # from library( "maps" ) image( wrf4ncar0531, axes=FALSE, col=c("grey", tim.colors(256)), zlim=zl) par( usr=locr) # if( map.available) map( add=TRUE, database="state") image.plot( obs0601, legend.only=TRUE, horizontal=TRUE, col=c("grey", tim.colors(256)), zlim=zl) # Second way preserves projections, but values are slighlty interpolated. zl <- range( c( c(obs0601), c( wrf4ncar0531) ) ) par( mfrow=c(2,2), mar=rep(2.1,4)) image(as.image(c(t(obs0601)), x=ICPg240Locs, nx=601, ny=501, na.rm=TRUE), zlim=zl, col=c("grey", tim.colors(64)), axes=FALSE, main="Stage II Reanalysis 4/26/05 0000 UTC") # map(add=TRUE, lwd=1.5) # map(add=TRUE, database="state", lty=2) image(as.image(c(t(wrf4ncar0531)), x=ICPg240Locs, nx=601, ny=501, na.rm=TRUE), zlim=zl, col=c("grey", tim.colors(64)), axes=FALSE, main="WRF NCAR valid 4/26/05 0000 UTC") image.plot(obs0601, col=c("grey", tim.colors(64)), zlim=zl, legend.only=TRUE, horizontal=TRUE) ## End(Not run)
Loading required package: spatstat Loading required package: spatstat.data Loading required package: nlme Loading required package: rpart spatstat 1.64-1 (nickname: ‘Help you I can, yes!’) For an introduction to spatstat, type ‘beginner’ Note: spatstat version 1.64-1 is out of date by more than 10 months; we recommend upgrading to the latest version. Loading required package: fields Loading required package: spam Loading required package: dotCall64 Loading required package: grid Spam version 2.5-1 (2019-12-12) is loaded. Type 'help( Spam)' or 'demo( spam)' for a short introduction and overview of this package. Help for individual functions is also obtained by adding the suffix '.spam' to the function name, e.g. 'help( chol.spam)'. Attaching package: ‘spam’ The following objects are masked from ‘package:base’: backsolve, forwardsolve See https://github.com/NCAR/Fields for an extensive vignette, other supplements and source code Loading required package: smoothie Loading required package: smatr Loading required package: turboEM Loading required package: doParallel Loading required package: foreach Loading required package: iterators Loading required package: parallel Loading required package: numDeriv Loading required package: quantreg Loading required package: SparseM Attaching package: ‘SparseM’ The following object is masked from ‘package:spam’: backsolve The following object is masked from ‘package:base’: backsolve Attaching package: ‘turboEM’ The following objects are masked from ‘package:numDeriv’: grad, hessian
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