Description Usage Arguments Details Value Examples
Generate artificial data for ensemble verification using a signal-plus-noise model
1 2 3 |
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 |
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.
A list with elements:
N-vector of observations
N*K matrix of ensemble members
N*K.ref matrix of reference ensemble members
1 2 | l <- GenerateToyData()
with(l, EnsCrps(ens, obs))
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