R: Generate artificial data for ensemble verification using a...

GenerateToyData {SpecsVerification} | R Documentation

## Generate artificial data for ensemble verification using a signal-plus-noise model

### Description

Generate artificial data for ensemble verification using a signal-plus-noise model

### Usage

    GenerateToyData(
      N = 20,
      mu.y = 0,
      s.s = 7,
      s.eps = 6,
      mu.x = 0,
      beta = 0.2,
      s.eta = 8,
      K = 10,
      mu.x.ref = NA,
      beta.ref = NA,
      s.eta.ref = NA,
      K.ref = NA
    )

### Arguments

`N` | number of forecasts and observations
`mu.y` | expectation value of the observations
`s.s` | standard deviation of the predictable signal
`s.eps` | standard deviation of the unpredictable noise
`mu.x` | expectation value of the ensemble
`beta` | weighting parameter of the signal in the ensemble forecasting system
`s.eta` | average spread of the ensemble
`K` | number of members of the ensemble
`mu.x.ref` | expectation value of the reference ensemble
`beta.ref` | weighting parameter of the signal in the reference ensemble forecasting system
`s.eta.ref` | average spread of the reference ensemble
`K.ref` | number of members of the reference ensemble

### Details

The function simulates data from the latent variable model:

y_t = mu_y + s_t + eps_t

x_t,r = mu_x + beta * s_t + eta_t,r

where y_t is the observation at time t, and x_t,r is the r-th ensemble member at time t. The latent variable s_t is to be understood as the "predictable signal" that generates correlation between observations and ensemble members. If all arguments that end in ".ref" are specified, a reference ensemble is returned to also test comparative verification.

### Value

A list with elements:

obs

  N-vector of observations

ens

  N*K matrix of ensemble members

ens.ref

  N*K.ref matrix of reference ensemble members

### Examples

     l <- GenerateToyData()
     with(l, EnsCrps(ens, obs))

[Package SpecsVerification version 0.5-3 Index]