Generate artificial data for ensemble verification using a signal-plus-noise model
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
)
number of forecasts and observations
expectation value of the observations
standard deviation of the predictable signal
standard deviation of the unpredictable noise
expectation value of the ensemble
weighting parameter of the signal in the ensemble forecasting system
average spread of the ensemble
number of members of the ensemble
expectation value of the reference ensemble
weighting parameter of the signal in the reference ensemble forecasting system
average spread of the reference ensemble
number of members of the reference ensemble
A list with elements:
N-vector of observations
N*K matrix of ensemble members
N*K.ref matrix of reference ensemble members
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.
# NOT RUN {
l <- GenerateToyData()
with(l, EnsCrps(ens, obs))
# }
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