Description Usage Arguments Details Value References See Also Examples
Computes the continuous ranked probability score (CRPS) for univariate ensemble forecasting models.
1 
fit 
A model fit to ensemble forecasting data, obtained using

ensembleData 
An 
dates 
The dates for which the CRPS will be computed.
These dates must be consistent with 
... 
Included for generic function compatibility. 
These methods are generic, and can be applied to all ensemble
forecasting models. Missing values in forecasts and/or observations
result in NA
values in the CRPS vector.
crps
is a matrix giving the CRPS for each instance in the data for
both the raw ensemble and the probabilistic forecast.
T. Gneiting and A. E. Raftery, Strictly proper scoring rules, prediction and estimation, Journal of the American Statistical Association 102:359–378, 2007.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  data("ensBMAtest", package = "ensembleBMA")
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")
tempTestFit < ensembleMOS(tempTestData, trainingDays = 25,
dates = "2008010100",
model = "normal")
crpsValues < crps(tempTestFit, tempTestData)
colMeans(crpsValues)

Loading required package: ensembleBMA
Loading required package: chron
Loading required package: evd
Attaching package: 'ensembleMOS'
The following objects are masked from 'package:ensembleBMA':
brierScore, cdf, crps, quantileForecast, trainingData
modeling for date 2008010100 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
17.42 0.26 0.19 0.22 0.00 0.24
T2.ngps T2.tcwb T2.ukmo
0.15 0.00 0.00
1.14 0.00
ensemble EMOS
2.510113 2.269935
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