easyVerification (version 0.1.8)

veriApply: Apply verification metrics to large datasets

Description

This wrapper applies verification metrics to arrays of forecast ensembles and verifying observations. Various formats array-based formats are supported. Additionally, continuous forecasts (and observations) are transformed to category forecasts using user-defined absolute thresholds or percentiles of the long-term climatology (see details).

Usage

veriApply(verifun, fcst, obs, fcst.ref = NULL, tdim = length(dim(fcst)) - 1,
  ensdim = length(dim(fcst)), prob = NULL, threshold = NULL,
  na.rm = FALSE, parallel = FALSE, maxncpus = 16, ncpus = NULL, ...)

Arguments

verifun

Name of function to compute verification metric (score, skill score)

fcst

array of forecast values (at least 2-dimensional)

obs

array or vector of verifying observations

fcst.ref

array of forecast values for the reference forecast (skill scores only)

tdim

index of dimension with the different forecasts

ensdim

index of dimension with the different ensemble members

prob

probability threshold for category forecasts (see below)

threshold

absolute threshold for category forecasts (see below)

na.rm

logical, should incomplete forecasts be used?

parallel

logical, should pararllel execution of verification be used (see below)?

maxncpus

upper bound for self-selected number of CPUs

ncpus

number of CPUs used in parallel computation, self-selected number of CPUs is used when is.null(ncpus) (the default).

...

additional arguments passed to verifun

Parallel processing

Parallel processing is enabled using the parallel package. Prallel verification is using ncpus FORK clusters or, if ncpus are not specified, one less than the autodetected number of cores. The maximum number of cores used for parallel processing with autodetection of the number of available cores can be set with the maxncpus argument.

Progress bars are available for non-parallel computation of the verification metrics. Please note, however, that the progress bar only indicates the time of computation needed for the actual verification metrics, input and output re-arrangement is not included in the progress bar.

Conversion to category forecasts

To automatically convert continuous forecasts into category forecasts, absolute (threshold) or relative thresholds (prob) have to be supplied. For some scores and skill scores (e.g. the ROC area and skill score), a list of categories will be supplied with categories ordered. That is, if prob = 1:2/3 for tercile forecasts, cat1 corresponds to the lower tercile, cat2 to the middle, and cat3 to the upper tercile.

Absolute and relative thresholds can be supplied in various formats. If a vector of thresholds is supplied with the threshold argument, the same threshold is applied to all forecasts (e.g. lead times, spatial locations). If a vector of relative thresholds is supplied using prob, the category boundaries to be applied are computed separately for each space-time location. Relative boundaries specified using prob are computed separately for the observations and forecasts, but jointly for all available ensemble members.

Location specific thresholds can also be supplied. If the thresholds are supplied as a matrix, the number of rows has to correspond to the number of forecast space-time locations (i.e. same length as length(fcst)/prod(dim(fcst)[c(tdim, ensdim)])). Alternatively, but equivalently, the thresholds can also be supplied with the dimensionality correpsonding to the obs array with the difference that the forecast dimension in obs contains the category boundaries (absolute or relative) and thus may differ in length.

See Also

convert2prob for conversion of continuous into category forecasts (and observations)

Examples

tm <- toyarray()
f.me <- veriApply('EnsMe', tm$fcst, tm$obs)

## find more examples and instructions in the vignette
devtools::install_github("MeteoSwiss/easyVerification", build_vignettes=TRUE)
library('easyVerification')
vignette('easyVerification')