An ensembleData object that includes ensemble forecasts,
verification observations and possibly dates.
Missing values (indicated by NA) are allowed.
This need not be the data used for the model fit,
although it must include the same ensemble members.
nSamples
The number of simulation samples for CRPS via simulation.
For the normal model, the default is analytic computation of the CRPS.
For the gamma model with a point mass at 0 (precipitation),
the CRPS is always computed by simulation,
with default nSamples = 10000.
seed
Argument to set.seed for random number generation in simulation.
dates
The dates for which the CRPS will be computed.
These dates must be consistent with fit and ensembleData.
The default is to use all of the dates in fit.
The dates are ignored if fit originates from fitBMA,
which also ignores date information.
...
Included for generic function compatibility.
Details
These methods are generic, and can be applied to all ensemble forecasting
models.
For gamma0 model for precipitation and the gamma model
for wind speed the CRPS is only available through simulation.
The default number of simulation samples is 10,000.
Note that the gamma0 model for precipitation and the
gamma model for wind speed may have been applied to a power
transformation of the data.
For normal models for temperature and pressure, analytic computation
of the CRPS is the default. CRPS will be computed via simulation for
normal models only if nSamples is set to a positive value.
For the bivariate normal model for wind speed and direction, the
CRPS is computed for the marginal wind speed distribution.
Value
crps is a matrix giving the CRPS for each instance in the data
for both the raw ensemble and the median probabilistic forecast. CRPS is a vector giving the mean of the CRPS over all
instances for the raw ensemble and the median probabilistic forecast.
References
T. Gneiting and A. E. Raftery,
Strictly proper scoring rules, prediction and estimation,
Journal of the American Statistical Association 102:359–378, 2007.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
ensembleBMA: An R Package for Probabilistic Forecasting
using Ensembles and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, 2007 (revised 2010).
See Also
ensembleBMA,
fitBMA
Examples
data(ensBMAtest)
ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")
obs <- paste("T2","obs", sep = ".")
ens <- paste("T2", ensMemNames, sep = ".")
tempTestData <- ensembleData( forecasts = ensBMAtest[,ens],
dates = ensBMAtest[,"vdate"],
observations = ensBMAtest[,obs],
station = ensBMAtest[,"station"],
forecastHour = 48,
initializationTime = "00")
## Not run: # R check
tempTestFit <- ensembleBMAnormal( tempTestData, trainingDays = 30)
## End(Not run)
# for quick run only; use more training days for forecasting
tempTestFit <- ensembleBMAnormal( tempTestData[1:20,], trainingDays = 8)
crpsValues <- crps( tempTestFit, tempTestData)
colMeans(crpsValues)
CRPS( tempTestFit, tempTestData)