This package provides functions to simplify application of forecast verification metrics to large datasets of ensemble forecasts. The design goals of easyVerification are:

The forecast metrics are imported from the SpecsVerification package. Additional verification metrics not available through SpecsVerification are implemented directly. At the time of publication, the package offers functionality to compute the following deterministic and probabilitistic scores and skill scores:

  1. Mean error (EnsMe), mean absolute error(EnsMae), mean squared error (EnsMse), and root mean squared error (EnsRmse) of the ensemble mean and their skill scores (e.g. EnsRmsess)
  2. Correlation with the ensemble mean (EnsCorr)
  3. Spread to error ratio (EnsSprErr and FairSprErr)
  4. Area under the ROC curve (EnsRoca) and its skill score (EnsRocss)
  5. Fair (FairRps) and standard (EnsRps) rank probability scores and skill scores (e.g. FairRpss)
  6. Fair (FairCrps) and standard (EnsCrps) continuous ranked probability scores and skill scores (e.g. FairCrpss)
  7. Dressed scores (DressIgn, DressCrps) and their skill scores (DressIgnSs, DressCrpss) with default ensemble dressing method ("silverman")
  8. The generalized discrimination score for ensembles (Ens2AFC)

Additional forecast verification metrics can be added by the user following the examples above.


You can get the latest version from CRAN


You can get the latest development version using


Getting started

You can find out more about the package and its functionality in the vignette.


The following example illustrates how to compute the continous ranked probability skill score of an ensemble forecast:


## check out what is included in easyVerification
#>  [1] "convert2prob" "count2prob"   "Ens2AFC"      "EnsCorr"     
#>  [5] "EnsError"     "EnsErrorss"   "EnsMae"       "EnsMaess"    
#>  [9] "EnsMe"        "EnsMess"      "EnsMse"       "EnsMsess"    
#> [13] "EnsRmse"      "EnsRmsess"    "EnsRoca"      "EnsRocss"    
#> [17] "EnsSprErr"    "FairSprErr"   "toyarray"     "toymodel"    
#> [21] "veriApply"

## set up the forecast and observation data structures
## assumption: we have 13 x 5 spatial instances, 15 forecast 
## times and 51 ensemble members
tm <- toyarray(c(13,5), N=15, nens=51)
fo.crpss <- veriApply("EnsCrpss", fcst=tm$fcst, obs=tm$obs)

## if the data are organized differently such that forecast
## instance and ensemble members are NOT the last two array
## dimensions, this has to be indicated

## alternative setup:
## forecast instance, ensemble members, all forecast locations
## collated in one dimension
fcst2 <- array(aperm(tm$fcst, c(3,4,1,2)), c(15, 51, 13*5))
obs2 <- array(aperm(tm$obs, c(3,1,2)), c(15, 13*5))
fo2.crpss <- veriApply("EnsCrpss", fcst=fcst2, obs=obs2, 
                       ensdim=2, tdim=1)

## The forecast evaluation metrics are the same, but the 
## data structure is different in the two cases
#> [1] 13  5
#> [1] 65
range(fo.crpss$crpss - c(fo2.crpss$crpss))
#> [1] 0 0

Parallel processing

As of easyVerification, parallel processing is supported under *NIX systems. The following minimal example illustrates how to use the parallel processing capabilities of easyVerification.

## generate a toy-model forecast observation set of 
## 10 x 10 forecast locations (e.g. lon x lat)
tm <- toyarray(c(10,10))

## run and time the ROC skill score for tercile forecasts without parallelization
  tm.rocss <- veriApply("EnsRocss", tm$fcst, tm$obs, prob=1:2/3)  
#>    user  system elapsed 
#>   1.984   0.008   2.000

## run the ROC skill score with parallelization
  tm.rocss.par <- veriApply("EnsRocss", tm$fcst, tm$obs, prob=1:2/3, parallel=TRUE)
#> Loading required namespace: parallel
#> [1] "Number of CPUs 3"
#>    user  system elapsed 
#>   0.088   0.040   0.824

To get additional help and examples please see the vignette vignette('easyVerification') or the help pages of the functions in easyVerification (e.g. help(veriApply)).

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easyVerification documentation built on Dec. 4, 2017, 5:04 p.m.