ToyModel: Synthetic forecast generator imitating seasonal to decadal...

In s2dverification: Set of Common Tools for Forecast Verification

Synthetic forecast generator imitating seasonal to decadal forecasts. The components of a forecast: (1) predictabiltiy (2) forecast error (3) non-stationarity and (4) ensemble generation. The forecast can be computed for real observations or observations generated artifically.

Description

The toymodel is based on the model presented in Weigel et al. (2008) QJRS with an extension to consider non-stationary distributions prescribing a linear trend. The toymodel allows to generate an aritifical forecast based on obsevations provided by the input (from Load) or artificially generated observations based on the input parameters (sig, trend). The forecast can be specfied for any number of start-dates, lead-time and ensemble members. It imitates components of a forecast: (1) predictabiltiy (2) forecast error (3) non-stationarity and (4) ensemble generation. The forecast can be computed for real observations or observations generated artifically.

Usage

ToyModel(
  alpha = 0.1,
  beta = 0.4,
  gamma = 1,
  sig = 1,
  trend = 0,
  nstartd = 30,
  nleadt = 4,
  nmemb = 10,
  obsini = NULL,
  fxerr = NULL
)

Arguments

alpha	Predicabiltiy of the forecast on the observed residuals Must be a scalar 0 < alpha < 1.
beta	Standard deviation of forecast error Must be a scalar 0 < beta < 1.
gamma	Factor on the linear trend to sample model uncertainty. Can be a scalar or a vector of scalars -inf < gammay < inf. Defining a scalar results in multiple forecast, corresponding to different models with different trends.
sig	Standard deviation of the residual variability of the forecast. If observations are provided 'sig' is computed from the observations.
trend	Linear trend of the forecast. The same trend is used for each lead-time. If observations are provided the 'trend' is computed from the observations, with potentially different trends for each lead-time. The trend has no unit and needs to be defined according to the time vector [1,2,3,... nstartd].
nstartd	Number of start-dates of the forecast. If observations are provided the 'nstartd' is computed from the observations.
nleadt	Number of lead-times of the forecats. If observations are provided the 'nleadt' is computed from the observations.
nmemb	Number of members of the forecasts.
obsini	Observations that can be used in the synthetic forecast coming from Load (anomalies are expected). If no observations are provided artifical observations are generated based on Gaussian variaiblity with standard deviation from 'sig' and linear trend from 'trend'.
fxerr	Provides a fixed error of the forecast instead of generating one from the level of beta. This allows to perform pair of forecasts with the same conditional error as required for instance in an attribution context.

Value

List of forecast with $mod including the forecast and $obs the observations. The dimensions correspond to c(length(gamma), nmemb, nstartd, nleadt)

Author(s)

History:
1.0 - 2014-08 (O.Bellprat) - Original code 1.1 - 2016-02 (O.Bellprat) - Include security check for parameters

Examples

# Example 1: Generate forecast with artifical observations
# Seasonal prediction example
a <- 0.1
b <- 0.3
g <- 1
sig <- 1
t <- 0.02
ntd <- 30
nlt <- 4
nm <- 10
toyforecast <- ToyModel(alpha = a, beta = b, gamma = g, sig = sig, trend = t, 
                       nstartd = ntd, nleadt = nlt, nmemb = nm)

# Example 2: Generate forecast from loaded observations
# Decadal prediction example
 ## Not run: 
data_path <- system.file('sample_data', package = 's2dverification')
expA <- list(name = 'experiment', path = file.path(data_path,
            'model/$EXP_NAME$/$STORE_FREQ$_mean/$VAR_NAME$_3hourly',
            '$VAR_NAME$_$START_DATE$.nc'))
obsX <- list(name = 'observation', path = file.path(data_path,
            '$OBS_NAME$/$STORE_FREQ$_mean/$VAR_NAME$',
            '$VAR_NAME$_$YEAR$$MONTH$.nc'))

# Now we are ready to use Load().
startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
sampleData <- Load('tos', list(expA), list(obsX), startDates,
                  output = 'areave', latmin = 27, latmax = 48,
                  lonmin = -12, lonmax = 40)
 
## End(Not run)
 

a <- 0.1
b <- 0.3
g <- 1
nm <- 10

toyforecast <- ToyModel(alpha = a, beta = b, gamma = g, nmemb = nm, 
                       obsini = sampleData$obs, nstartd = 5, nleadt = 60)
 
PlotAno(toyforecast$mod, toyforecast$obs, startDates, 
       toptitle = c("Synthetic decadal temperature prediction"), 
       fileout = "ex_toymodel.eps")
 

s2dverification documentation built on April 20, 2022, 9:06 a.m.