Description Usage Arguments Details Value Author(s) References See Also Examples

Intensity Scale (IS) verification based on Casat et al (2004) and Casati (2010).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
waveIS(x, th = NULL, J = NULL, wavelet.type = "haar", levels
= NULL, max.n = NULL, smooth.fun = "hoods2dsmooth",
smooth.params = NULL, rule = ">=", verbose = FALSE,
...)
## S3 method for class 'SpatialVx'
waveIS(x, th = NULL, J = NULL, wavelet.type = "haar", levels
= NULL, max.n = NULL, smooth.fun = "hoods2dsmooth",
smooth.params = NULL, rule = ">=", verbose = FALSE,
..., time.point = 1, obs = 1, model = 1 )
## Default S3 method:
waveIS(x, th = NULL, J = NULL, wavelet.type = "haar", levels
= NULL, max.n = NULL, smooth.fun = "hoods2dsmooth",
smooth.params = NULL, rule = ">=", verbose = FALSE,
...)
## S3 method for class 'waveIS'
plot(x, main1 = "X", main2 = "Y",
which.plots = c("all", "mse", "ss", "energy"),
level.label = NULL, ...)
## S3 method for class 'waveIS'
summary(object, ...)
``` |

`x` |
For |

`object` |
list object returned by |

`main1,main2` |
character giving labels for the plots where |

`which.plots` |
character vector naming one or more specific plots to do. |

`level.label` |
optional character vector to use for level names on the plot(s). |

`J` |
numeric integer giving the number of levels to use. If NULL and the field is dyadic, this will be log2(min(dim(X))), where X is a field from the verification set. If NULL and the field is not dyadic, then |

`wavelet.type` |
character giving the name of the wavelet type to use as accepted by |

`th` |
list object with named components “X” and “Xhat” giving the thresholds to use for each field. If null, taken from teh thresholds attribute for “SpatialVx” objects. |

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

`obs, model` |
numeric indicating which observation/forecast model to select for the analysis. |

`levels` |
numeric vector giving the successive values of the smoothing parameter. For example, for the default method, these are the neighborhood lengths over which the levels^2 nearest neighbors are averaged for each point. Values should make sense for the specific smoothing function. For example, for the default method, these should be odd integers. |

`max.n` |
(optional) single numeric giving the maximum neighborhood length to use. Only used if levels are NULL. |

`smooth.fun` |
character giving the name of a smoothing function to be applied. Default is an average over the n^2 nearest neighbors, where n is taken to be each value of the |

`smooth.params` |
list object containing any optional arguments to |

`rule` |
If |

`verbose` |
logical, should progress information be printed to the screen? |

`...` |
Not used by |

This function applies various statistics to the detail fields (in wavelet space) of a discrete wavelet decomposition (DWT) of the binary error fields for a verification set. In particular, the statistics described in Casati et al (2004) and Casati (2010) are calculated. This function depends on the `waverify2d`

or `mowaverify2d`

function (depending on whether the fields are dyadic or not, resp.), which themselves depend on the `dwt.2d`

and `idwt.2d`

or `modwt.2d`

and `imodwt.2d`

functions.

See the references herein and the help files and references therein for `dwt.2d`

and `modwt.2d`

for more information on this approach, as well as Percival and Guttorp (1994) and Lindsay et al. (1996).

A list object of class “waveIS” that contains the entire prep object passed in by obj, as well as additional components:

`EnVx,EnFcst` |
J by q matrices giving the energy for the verification and forecast fields, resp., for each threshold (columns) and scale (rows). |

`MSE,SS ` |
J by q matrices giving the mean square error and IS skill score for each threshold (column) and scale (rows). |

`Bias` |
numeric vector of length q giving the frequency bias of the original fields for each threshold. |

plot.waveIS does not return any value. A plot is created on the current graphic device. summary.waveIS returns a list invisibly with the same components as returned by waveIS along with extra components:

`MSEu,SSu,EnVx.u,EnFcst.u` |
length q numeric vectors giving the MSE, SS, and Vx and Fcst energy for each threshold (i.e., ignoring the wavelet decomposition). |

`MSEperc,EnVx.perc,EnFcst.perc` |
J by q numeric matrices giving percentage form of MSE, Vx Energy and Fcst Energy values, resp. |

`EnRelDiff` |
J by q numeric matrix giving the energy relative difference. |

Eric Gilleland

Casati, B., Ross, G. and Stephenson, D. B. (2004) A new intensity-scale approach for the verification of spatial precipitation forecasts. *Meteorol. Appl.* **11**, 141–154.

Casati, B. (2010) New Developments of the Intensity-Scale Technique within the Spatial Verification Methods Inter-Comparison Project. *Wea. Forecasting* **25**, (1), 113–143, doi:10.1175/2009WAF2222257.1.

Lindsay, R. W., Percival, D. B. and Rothrock, D. A. (1996) The discrete wavelet transform and the scale analysis of the surface properties of sea ice. *IEEE Transactions on Geoscience and Remote Sensing*, **34** (3), 771–787.

Percival, D. B. and Guttorp, P. (1994) Long-memory processes, the Allan variance and wavelets. In *Wavelets in Geophysics*, Foufoula-Georgiou, E. and Kumar, P., Eds., New York: Academic, 325–343.

`IS`

, `int.scale.verify`

from package verification,

`dwt.2d`

, `modwt.2d`

, `idwt.2d`

, `imodwt.2d`

, `hoods2d`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
data( "UKobs6" )
data( "UKfcst6" )
data( "UKloc" )
hold <- make.SpatialVx( UKobs6, UKfcst6,
thresholds = c(0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50),
loc = UKloc, map = TRUE, field.type = "Rainfall", units = "mm/h",
data.name = "Nimrod", obs.name = "UKobs6", model.name = "UKfcst6" )
look <- waveIS(hold, J=8, levels=2^(8-1:8), verbose=TRUE)
plot(look, which.plots="mse")
plot(look, which.plots="ss")
plot(look, which.plots="energy")
summary(look)
## Not run:
data( "pert004" )
data( "pert000" )
hold <- make.SpatialVx( pert000, pert004, thresholds = c(1, 10, 50),
loc = ICPg240Locs, projection = TRUE, map = TRUE, loc.byrow = TRUE,
field.type = "Precipitation", units = "mm/h",
data.name = "Perturbed ICP", obs.name = "pert000", model.name = "pert004" )
look <- waveIS(hold, levels=1:4, verbose=TRUE)
plot(look, which.plots="mse")
plot(look, which.plots="ss")
plot(look, which.plots="energy")
summary(look)
## End(Not run)
``` |

SpatialVx documentation built on July 10, 2018, 1:05 a.m.

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