RAnEnExtra::biasCorrection

RAnEnExtra::biasCorrection carries out a bias correction routine on the analog ensembles using a linear regression method. This correction method is useful for predicting extreme event.

biasCorrection(
  analogs,
  target.forecasts,
  historical.forecasts,
  similarity.time.index,
  similarity.station.index = NULL,
  regression.forecasts = NULL,
  regression.observations = NULL,
  forecast.id = NULL,
  activation.func = NULL,
  show.progress = T,
  return.more = F,
  group.func = mean,
  ...
)

Arguments

analogs	A four-dimensional array for analog values with the dimensions [stations, test times, lead times, members]. This is usually generated from the RAnEn::generateAnalogs.
target.forecasts	A three- or four- dimensional array for test forecasts that analogs are generated for. The dimensions can either be [stations, test times, lead times] or [stations, test times, lead times, 1]. The forecasts should be aligned with the anlaogs for the first three dimensions.
historical.forecasts	The historical forecast search repository with the dimensions [parameters, stations, times, lead times]. This is usually the Forecasts used in RAnEn::generateAnalogs.
similarity.time.index	Similarity time index for each analog members. To have this for your analogs, you need to set config$save_similarity_time_index = T before you run RAnEn::generateAnalogs.
similarity.station.index	Similarity station index for each analog members. To have this for your analogs, you need to set config$save_similarity_station_index = T and use SSE for analog generation.
regression.forecasts	The forecast values used to calculate the slope of a linear regression line. These forecasts must correspond to observations for regression.
regression.observations	The observation values used to calculate the slope of a linear regression line These observations must correspond to forecasts for regression.
forecast.id	A forecasts parameter index used by the historical.forecasts to specify which forecast parameter to use.
activation.func	An activation function to signify whether a particular ensemble should be bias corrected. This function should takes a single argument and return a TRUE or FALSE. The single argument of the function will be an analog ensemble, or a numeric vector. For example, activation.func = function(members) {if (mean(members) > 15) {return(T)} else {return(F)}} will only correct ensembles that have an average over 15.
show.progress	Whether to show a progress bar
return.more	Whether to return more information
group.func	How to collapse the analog ensemble to a single value to calculate the amount of correction. In the paper and by default, this is mean.
...	Additional variables passed to group.func

References

Alessandrini, Stefano, Simone Sperati, and Luca Delle Monache. "Improving the analog ensemble wind speed forecasts for rare events." Monthly Weather Review 147.7 (2019): 2677-2692.