Description Usage Arguments List of functions to be called Conversion to category forecasts Out-of-sample reference forecasts Parallel processing Note See Also Examples

This wrapper applies verification metrics to arrays of forecast ensembles and verifying observations. Various array-based data 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).

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`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) |

`strategy` |
type of out-of-sample reference forecasts or namelist with
arguments as in |

`na.rm` |
logical, should incomplete forecasts be used? |

`fracmin` |
fraction of forecasts that are not-missing for forecast to
be evaluated. Used to determine |

`nmin` |
number of forecasts that are not-missing for forecast to
be evaluated. If both |

`parallel` |
logical, should parallel 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 |

`...` |
additional arguments passed to |

The selection of verification
functions supplied with this package and as part of
`SpecsVerification`

can be enquired using
`ls(pos='package:easyVerification')`

and
`ls(pos='package:SpecsVerification')`

respectively. Please note,
however, that only some of the functions provided as part of
`SpecsVerification`

can be used with `veriApply`

.
Functions that can be used include for example the (fair) ranked
probability score `EnsRps`

,
`FairRps`

, and its skill score
`EnsRpss`

,
`FairRpss`

, or the continuous ranked
probability score `EnsCrps`

, etc.

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
corresponding 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.

`strategy`

specifies the
set-up of the climatological reference forecast for skill scores if no
explicit reference forecast is provided. The default is `strategy = "none"`

,
that is all available observations are used as equiprobable
members of a reference forecast. Alternatively, `strategy = "crossval"`

can be used for leave-one-out cross-validated reference forecasts,
or `strategy = "forward"`

for a forward protocol (see `indRef`

).

Alternatively, a list with named parameters corresponding to the input
arguments of `indRef`

can be supplied for more fine-grained
control over standard cases. Finally, also a list with observation indices
to be used for each forecast can be supplied (see `generateRef`

).

Parallel processing is enabled using the
`parallel`

package. Parallel verification is using
`ncpus`

`FORK`

clusters or, if `ncpus`

are not specified,
one less than the auto-detected number of cores. The maximum number of cores
used for parallel processing with auto-detection 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.

If the forecasts and observations are only available as category
probabilities (or ensemble counts as used in `SpecsVerification`

) as
opposed to as continuous numeric variables, `veriApply`

cannot be used
but the atomic verification functions for category forecasts have to be
applied directly.

Out-of-sample reference forecasts are not fully supported for
categorical forecasts defined on the distribution of forecast values (e.g.
using the argument `prob`

). Whereas only the years specified in
`strategy`

are used for the reference forecasts, the probability
thresholds for the reference forecasts are defined on the collection of
years specified in `strategy`

.

`convert2prob`

for conversion of continuous into
category forecasts (and observations)

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easyVerification documentation built on Dec. 4, 2017, 5:04 p.m.

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