SpecsVerification (version 0.5-3)

# DressEnsemble: Transform an ensemble forecast to a continuous forecast distribution by kernel dressing.

## Description

Transform an ensemble forecast to a continuous forecast distribution by kernel dressing.

## Usage

`DressEnsemble(ens, dressing.method = "silverman", parameters = NA)`

## Arguments

ens

a N*R matrix representing N time instances of real-valued R-member ensemble forecasts

dressing.method

One of "silverman" (default), "akd", "akd.fit". See Details.

parameters

A list, containing the parameters for the dressing method. See Details.

## Value

The function returns a list with elements `ens` (a N*R matrix, where ens[t,r] is the mean of the r-th kernel at time instance t) and `ker.wd` (a N*R matrix, where ker.wd[t,r] is the standard deviation of the r-th kernel at time t)

## Details

The dressing methods currently implemented and their required parameters are:

"silverman" (default)

No parameters are given. At time instance `n` each ensemble member is replaced by a Gaussian kernel with mean ens[n, k] and variance (4 / 3 / K)^0.4 * var(ens[n, ]). This method is called "Silverman's rule of thumb" and provides a simple non-parametric method for smoothing a discrete ensemble.

"akd"

Affine Kernel Dressing. The required parameters are list(r1, r2, a, s1, s2). The `k`-th ensemble member at time instance `n` is dressed with a Gaussian kernel with mean r1 + r2 * mean(ens[n,]) + a * ens[n, k] and variance (4 / 3 / K)^0.4 * (s1 + s2 * a^2 * var(ens[n,])). Negative variances are set to zero. Note that parameters = list(r1=0, r2=0, a=1, s1=0, s2=1) yields the same dressed ensemble as dressing.method="silverman".

"akd.fit"

Affine Kernel Dressing with fitted parameters. The required parameters is list(obs), where `obs` is a vector of observations which are used to optimize the parameters r1, r2, a, s1, s2 by CRPS minimization. See ?FitAkdParameters for more information.

## References

Silverman, B.W. (1998). Density Estimation for Statistics and Data Analysis. London: Chapman & Hall/CRC. ISBN 0-412-24620-1. Broecker J. and Smith L. (2008). From ensemble forecasts to predictive distribution functions. Tellus (2008), 60A, 663--678. 10.1111/j.1600-0870.2008.00333.x.

## See Also

DressCrps, DressIgn, GetDensity, FitAkdParameters

## Examples

```# NOT RUN {
data(eurotempforecast)
d.silverman <- DressEnsemble(ens)
d.akd <- DressEnsemble(ens, dressing.method="akd",
parameters=list(r1=0, r2=0, a=1,
s1=0, s2=0))
d.akd.fit <- DressEnsemble(ens, dressing.method="akd.fit",
parameters=list(obs=obs))
# }
```