make.SpatialVx: Spatial Verification Sets -- SpatialVx Object

Description

A list object containing the verification sets of spatial verification and forecast fields with pertinent information.

Usage

make.SpatialVx(X, Xhat, thresholds = NULL,
    loc = NULL, projection = FALSE, subset = NULL,
    time.vals = NULL, reg.grid = TRUE, map = FALSE,
    loc.byrow = FALSE, field.type = "", units = "",
    data.name = c("X", "Xhat"),
    q = c(0, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95),
    qs = NULL)
## S3 method for class 'SpatialVx':
hist(x, ..., time.point = 1, model = 1)
## S3 method for class 'SpatialVx':
plot(x, ..., set.pw = FALSE, time.point = 1, model = 1,
                 col, zlim, horizontal = TRUE)
## S3 method for class 'SpatialVx':
print(x, ...)
## S3 method for class 'SpatialVx':
summary(object, ...)

Arguments

An n X m matrix or n X m X T array giving the verification field of interest. If an array, T is the number of time points.

Xhat

An n X m matrix or n X m X T array giving the forecast field of interest, or a list of such matrices/arrays with each component of the list an n X m matrix or n X m X T array defining a separate forecast model.

thresholds

single numeric, numeric vector, or Nu X Nf matrix, where Nu are the number of thresholds and Nf the number of forecast models plus one (for the verification) giving the threshold values of interest for the verification set or components of the set. If NU

loc

If lon/lat coordinates are available, then this is an n * m X 2 matrix giving the lon/lat coordinates of each grid point or location. Should follow the convention used by the maps package.

projection

logical, are the grids projections onto the globe? If so, when plotting, it will be attempted to account for this by using the poly.image function from package fields. In this case, each column of loc will be conve

subset

vector identifying which specific grid points should be included (if not all of them). This argument may be ignored by most functions and is included for possible future functionality.

time.vals

If more than one time point is available in the set (i.e., the set is of n X m X T arrays, with T > 1), then this argument can be used to define the time points. If missing, the default will yield the vector 1:T. But, it is possible to incl

reg.grid

logical, is the verification set on a regular grid? This is another feature intended for possible future funcitonality. Most functions in this package assume the set is on a regular grid.

map

logical, should the plot function attempt to place a map onto the plot? Only possible if the loc argument is given.

field.type, units

character used for plot labelling and printing information to the screen. Describes what variable and in what units the field represents.

data.name

character vector describing the verification set. May have length equal to the number of forecasts and verification fields or it may additionally have as the first entry an overall name for the set. For example, for the geometric cases from the ICP, one

numeric vector giving the values of quantiles to be used for thresholds. Only used if thresholds is NULL.

character vector describing the quantiles used. Again, only used if thresholds is NULL. This is for subsequent plot/print labelling purposes.

set.pw

logical, should a panel of plots be determined and set by the function.

time.point

numeric or character indicating which time point from the SpatialVx verification set to select for analysis.

model

numeric indicating which forecast model to select for the analysis.

col, zlim, horizontal

optional arguments to image, and/or fields functions poly.image and image.plot

x, object

list object of class SpatialVx.

loc.byrow

logical determining whether to set up the location matrices using byrow = TRUE or FALSE (for use with poly.image.

...

hist method: optional arguments to hist.

plot method: if time.point is a function, then these allow for optional arguments to this function to be passed.

print and summary met

Value

A list object with two (unnamed) components:
1matrix or array (same as input argument) giving the observation
2Either a matrix or array (same as input argument) or a list of such objects if more than one forecast model.
Several attributes are also included among the following:
xdimnumeric of length 2 or 3 giving the dimensions of the verification set (i.e., m, n and T, if relevant).
timevector giving the time values
thresholdsmatrix giving the thresholds for each field. If there is more than one forecast, and they use the same threshold, this matrix may have only two columns.
udimthe dimensions of the thresholds matrix.
locnm X 2 matrix giving the locations. If loc was not given, this will be c(rep(1:n, m), rep(1:m, each=n)).
subsetIf given, this is a numeric vector describing a subset of loc to be used.
data.namecharacter vector giving (same as input argument).
nforecastsingle numeric giving the number of different forecast models contained in the object.
field.type, unitscharacter strings, same as input arguments.
projectionlogical, is the grid a projection?
reg.gridlogical, is the grid a regular grid?
maplogical, should a map be added to image plots of the data?
qscharacter vector giving the names of the threshold quantiles.
msgA message involving the data name, field type and units for adding info to plots, etc.

Details

This function merely describes a spatial verification set that includes the actual data as well as numerous attributes that are used by several of the subsequent functions that might be employed. In many cases, the attribute information may be passed on to output from other functions for plot labelling and printing purposes (e.g., in order to identify the verification set, time point(s), etc.).

All (or perhaps most) subsequent functions in this package utilize objects of this class and the information contained in the attributes. This function simply gathers information and data sets into a particular form.

The plot method function attempts to create an image plot of each field in the set (at each time point). If projection is TRUE, then it will attempt to preserve the projection (via poly.image of package fields). It will also add white contour lines showing the thresholds. If map is TRUE and loc was supplied, then a map will also be added, if possible.

Examples

Run this code

data(UKobs6)
data(UKfcst6)
data(UKloc)

hold <- make.SpatialVx(UKobs6, UKfcst6, thresholds=c(0.01, 20.01),
    loc=UKloc, field.type="Precipitation", units="mm/h",
    data.name=c("Nimrod", "Observations 6", "Forecast 6"), map=TRUE)

hold

plot(hold)

# Stage 2 Observation files from
# NSSL/NCEP Spring 2005 Forecast Experiment.
data(obs0426)
data(obs0513)
data(obs0514)
data(obs0518)
data(obs0519)
data(obs0525)
data(obs0601)
data(obs0603)
data(obs0604)

st2 <- array(c(c(obs0426), c(obs0513), c(obs0514), c(obs0518),
    c(obs0519), c(obs0525), c(obs0601), c(obs0603), c(obs0604)),
    dim=c(601, 501, 9))

rm(obs0426, obs0513, obs0514, obs0518, obs0519, obs0525, obs0601,
    obs0603, obs0604)

# wrf2caps
data(wrf2caps0425)
data(wrf2caps0512)
data(wrf2caps0513)
data(wrf2caps0517)
data(wrf2caps0518)
data(wrf2caps0524)
data(wrf2caps0531)
data(wrf2caps0602)
data(wrf2caps0603)

# wrf4ncar
data(wrf4ncar0425)
data(wrf4ncar0512)
data(wrf4ncar0513)
data(wrf4ncar0517)
data(wrf4ncar0518)
data(wrf4ncar0524)
data(wrf4ncar0531)
data(wrf4ncar0602)
data(wrf4ncar0603)

# wrf4ncep
data(wrf4ncep0425)
data(wrf4ncep0512)
data(wrf4ncep0513)
data(wrf4ncep0517)
data(wrf4ncep0518)
data(wrf4ncep0524)
data(wrf4ncep0531)
data(wrf4ncep0602)
data(wrf4ncep0603)

wrf2caps <- array(c(c(wrf2caps0425), c(wrf2caps0512),
    c(wrf2caps0513), c(wrf2caps0517), c(wrf2caps0518),
    c(wrf2caps0524), c(wrf2caps0531), c(wrf2caps0602),
    c(wrf2caps0603)),
    dim=c(601, 501, 9))

wrf4ncar <- array(c(c(wrf4ncar0425), c(wrf4ncar0512),
    c(wrf4ncar0513), c(wrf4ncar0517), c(wrf4ncar0518),
    c(wrf4ncar0524), c(wrf4ncar0531), c(wrf4ncar0602),
    c(wrf4ncar0603)),
    dim=c(601, 501, 9))

wrf4ncep <- array(c(c(wrf4ncep0425), c(wrf4ncep0512),
    c(wrf4ncep0513), c(wrf4ncep0517), c(wrf4ncep0518),
    c(wrf4ncep0524), c(wrf4ncep0531), c(wrf4ncep0602),
    c(wrf4ncep0603)),
    dim=c(601, 501, 9))

rm(wrf2caps0425, wrf2caps0512, wrf2caps0513, wrf2caps0517,
    wrf2caps0518, wrf2caps0524, wrf2caps0531, wrf2caps0602,
    wrf2caps0603, wrf4ncar0425, wrf4ncar0512, wrf4ncar0513,
    wrf4ncar0517, wrf4ncar0518, wrf4ncar0524, wrf4ncar0531,
    wrf4ncar0602, wrf4ncar0603, wrf4ncep0425, wrf4ncep0512,
    wrf4ncep0513, wrf4ncep0517, wrf4ncep0518, wrf4ncep0524,
    wrf4ncep0531, wrf4ncep0602, wrf4ncep0603)

fcst <- list(wrf2caps, wrf4ncar, wrf4ncep)

rm(wrf2caps, wrf4ncar, wrf4ncep)

# Now, create the object.
data(ICPg240Locs)

ICPreal <- make.SpatialVx(st2, fcst, thresholds=c(0.1, 20.1),
    loc=ICPg240Locs, projection=TRUE, loc.byrow = TRUE,
    time.vals=c(2005042600, 2005051300, 2005051400, 2005051800,
        2005051900, 2005052500, 2005060100, 2005060300, 2005060400),
    map=TRUE, field.type="Precipitation", units="mm/h",
    data.name=c("Stage II Analysis", "WRF 2 CAPS", "WRF 4 NCAR",
        "WRF 4 NCEP"))

pdf("ICPreal.pdf")
plot(ICPreal, set.pw=FALSE)
dev.off()