veriUnwrap | R Documentation |

Decomposes input arguments into forecast, verifying observations, and reference forecast and hands these over to the function provided.

```
veriUnwrap(
x,
verifun,
nind = c(nens = ncol(x) - 1, nref = 0, nobs = 1, nprob = 0, nthresh = 0),
ref.ind = NULL,
...
)
```

`x` |
n x k + 1 matrix with n forecasts of k ensemble members plus the verifying observations |

`verifun` |
character string with function name to be executed |

`nind` |
named vector with number of ensemble members, ensemble members of reference forecasts, observations (defaults to 1), probability or absolute thresholds (see details) |

`ref.ind` |
list with specifications for the reference forecast (see details) |

`...` |
additional arguments passed on to |

Forecast verification metrics are only computed for forecasts with non-missing verifying observation and at least one non-missing ensemble member. Metrics for all other forecasts are set to missing. For aggregate metrics (e.g. skill scores) the metric is computed over non-missing observation/forecast pairs only.

For computation of skill scores, reference forecasts can be provided. That
is, the first `nens`

columns of `x`

contain the forecasts, the
`(nens + 1):(ncol(x) - 1)`

following columns contain the reference
forecast, and the final column contains the observations. If no reference
forecast is provided (i.e. `ncol(x) == nens + 1`

), a climatological
forecast is constructed from the `n`

verifying observations.

The elements of vector `nind`

have to be named with `nens`

containing the number of ensemble members, `nref`

the number of
ensemble members in the reference forecast for skill scores, `nobs`

the number of observations (only one supported), `nprob`

the number of
probability thresholds, and `nthresh`

the number of absolute threshold
for conversion of continuous forecasts to category forecasts.

`ref.ind`

specifies the set-up of the climatological reference
forecast for skill scores if no explicit reference forecast is provided
(see `indRef`

). Also, `ref.ind`

is used to determine the
baseline to estimate the percentile-based category boundaries to convert
continuous forecasts to category probabilities.

Out-of-sample reference forecasts are now fully supported.

`veriApply`

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