generateRef: Generate Probabilistic Climatological Ensemble Forecast from...

In easyVerification: Ensemble Forecast Verification for Large Data Sets

Generate Probabilistic Climatological Ensemble Forecast from Observations

Description

To generate reference ensemble forecasts for forecast evaluation based on the available observations, indRef implements the out-of-sample or in-sample protocol to be used and generateRef produces the corresponding ensemble forecast given the actual observations.

Usage

indRef(
  nfcst,
  type = c("none", "forward", "crossval", "block"),
  indices = 1:nfcst,
  blocklength = 1
)

generateRef(obs, ind)

Arguments

nfcst	number of forecast instances to be produce
type	type of out-of-sample protocol to be applied (see below)
indices	Subset of the observations / forecast times to be used for reference forecasts
blocklength	for cross-validation and split-sample
obs	vector of observations
ind	list or matrix of dimension (n x nref) of indices of the observations to be used for each forecast instance

Value

ind	A list of indices to be used for each forecast from 1 to nfcst

Cross-validation

Leave-one-out and leave-n-out cross-validation reference forecasts can be produced by setting type = "crossval". By default, the blocklength is set to 1, but moving blocks of length n can be specified by setting blocklength = n.

Split sample

In contrast to type="crossval", type="block" is used for split-sample validation with non-overlapping blocks of length blocklength retained for validation.

Forward

Correspondingly, reference forecasts that are only based on past (future) observations can be produced using type = "forward". For this, the first half of the reference forecasts only uses future information, i.e. observations 2:n for forecast 1, 3:n for 2 and so forth. The second half of the reference forecasts use only past observations, i.e. observations 1:(n-1) for forecast n, 1:(n-2) for n-1, etc.

Subsetting

In combination with the above, a subset of the observations can be specified for use as reference forecasts by providing the explicit indices of the observations to be used via indices=1:k. In combination with the forward method, all observations in indices will be used to construct the reference forecast for forecasts not included in indices (i.e. if nfcst > max(indices)).

easyVerification documentation built on Aug. 15, 2023, 9:06 a.m.