Description Usage Arguments Value Cateogry forecasts Verification metrics See Also Examples
Verifies daily seasonal hindcasts (re-forecasts) of indices for gridded or station data. It is based on the packages easyVerification
and SpecsVerification
.
1 2 3 4 5 6 7 8 9 10 |
obs |
Observations. "climindvis" object of type p/grid as output from |
hc |
Hindcasts. "climindvis_index" object of type hc_p/hc_grid as output from |
hc_ref |
Reference forecast for calculation of skill scores. "climindvis" object of type hc_p/hc_grid as output from |
index |
Name of index to be calculated., e.g. index = "dd" or index = "tnn". For list of indices see |
index_args |
List of arguments for index. See |
selyears |
Integer array of years to be selected from data. For indices with quantiles, years as defined in index_args are used for quantile calculation, but only selected years are plotted. For functions using seasonal forecast data, selyears is only applied to hindcasts. |
veri_metrics |
Character array of verification metrics as defined in the |
veri_args |
Optional list of additional arguments for verification, see |
Object of class index and "climindvis_verification" with following entries:
verification: Named list with verification outputs for each metric in veri_metric.
veri_info: Information needed for plotting of verification results, such as limits and dimensions of metric
years: years used for verification
lon: Array of longitudes
lat: Array of latitudes
data_info$obs and $hc ($hc_ref):Data_info taken from obs and hc input objects, e.g. name of dataset
For category skill scores (e.g. ROC area and Rps(s)) forecasts have to be provided in categories. The easyVerification package automatically converts continuous forecast to category forecasts. The optional parameter veri_args$ncat determines the number of equidistant forecast categories (e.g. 3:terciles, 4:quartiles, 5:quintiles). This corresponds to setting the variable prob of easyVerification{veriApply}
to prob=c(1:((ncat-1)/ncat)). However, if you want to use other probability thresholds you can use the argument veri_args$prob. The use of absolute thresholds (as possible in easyVerification{veriApply}
) is currently not implemented.
The verify_index function can be used of calculating all of the skill metrics defined in the packages easyVerification
and SpecsVerification
. Skill scores ending in "ss" denote the corresponding skill scores (see below).
Currently the following verification metrics from the easyVerification
package can be calculated (the ending "ss" denotes the corresponding skill scores):
Generalized Discrimination Score
Correlation with Ensemble Mean
Ensemble mean absolute error
Ensemble mean squared error
Square root of ensemble mean squared error
Area Under the ROC Curve
Spread to Error Ratio
Fair Spread to Error Ratio
Ranked probability skill score
Fair Ranked probability skill score
Continuous Ranked probability skill score
Fair Continuous Ranked probability skill score
More information about the skill scores can be found in the respective package.
Skill scores are defined as (mean score - mean score for reference) / (perfect score - mean score for reference). The skill score is zero if the mean score of the forecast equals the mean score of the reference forecast, and equals one if the mean score of the forecast equals the best possible score. If hc_ref is not defined, the climatology is taken as reference.
For more information about Fair scores, see e.g. Ferro, C. A. T. (2014). Fair scores for ensemble forecasts. Quarterly Journal of the Royal Meteorological Society, 140(683), 1917-1923.
1 2 3 4 5 6 7 8 9 | ## load example hindcast and observational data for stations
## (see \code{\link{example_climindvis_objects}}):
data("object_hc_st", "object_st")
## verify hindcasts of indices using the skill metrics
## "EnsRmsess", "EnsRpss", "EnsCorr":
veri<-verify_index(obs = object_st, hc = object_hc_st, index="txx", index_args=list(aggt="seasonal") , veri_metrics = c("EnsRmsess", "EnsRpss", "EnsCorr"))
|
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