calCCR: Inflation calibration method for seasonal forecasts (a.k.a....

In SantanderMetGroup/calibratoR: An R package for statistical calibration of seasonal climate forecasts

Inflation calibration method for seasonal forecasts (a.k.a. climate conserving recalibration)

Description

This function implements the EMOS method introduced in Doblas-Reyes et al. 2005 and recently applied in Torralba et al. 2017 (method 2) to produce reliable operational seasonal forecasts of wind speed. After Weigel et al. 2009, this method is sometimes referred to as climate conserving recalibration (CCR).

Usage

calCCR(
  fcst.grid,
  obs.grid,
  crossval = TRUE,
  apply.to = c("all", "sig"),
  alpha = 0.1
)

Arguments

fcst.grid	climate4R grid. Forecasts to be calibrated (typically on a monthly/seasonal basis). At the moment, only gridded data are supported.
obs.grid	climate4R grid. Reference observations the forecasts are calibrated towards (typically on a monthly/seasonal basis).
crossval	Logical. TRUE (default) for leave-one-out cross-validation. FALSE for not cross-validation.
apply.to	Character. If "all" is selected, all forecasts are calibrated. Alternatively, if "sig" is selected, the calibration is only applied in those points where the correlation between the ensemble mean and the observations is statistically significant.
alpha	Significance (0.1 by default) of the ensemble mean correlation (i.e. "alpha = 0.05" would correspond to a 95% confidence level). Only works if "apply.to = sig".

Value

climate4R grid. Calibrated forecasts.

Note

Ensemble Model Output Statistics (EMOS) methods use the correspondence between the ensemble mean and the observations in the calibration process.

Author(s)

R. Manzanas and V. Torralba.

References

• Doblas-Reyes, F.J., R. Hagedorn, and T.N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting-II. Calibration and combination. Tellus, 57A, 234–252, doi:10.3402/tellusa.v57i3.14658

• Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel, and M. Davis, 2017: Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources. J. Appl. Meteor. Climatol., 56, 1231–1247, https://doi.org/10.1175/JAMC-D-16-0204.1

• Weigel, A.P., M.A. Liniger, and C. Appenzeller, 2009: Seasonal ensemble forecasts: Are recalibrated single models better than multimodels? Mon. Wea. Rev., 137, 1460-1479, https://doi.org/10.1175/2008MWR2773.1

See Also

Other calibration: calLM(), calMVA(), calNGR(), calRPC()

Examples

{
## loading seasonal forecasts (CFS) and observations (NCEP) of boreal winter temperature over Iberia
require(climate4R.datasets)
data("CFS_Iberia_tas"); fcst = CFS_Iberia_tas
data("NCEP_Iberia_tas"); obs = NCEP_Iberia_tas
## passing from daily data to seasonal averages
fcst = aggregateGrid(fcst, aggr.y = list(FUN = "mean", na.rm = TRUE))
obs = aggregateGrid(obs, aggr.y = list(FUN = "mean", na.rm = TRUE))
## interpolating forecasts to the observations' resolution
fcst = interpGrid(fcst, new.coordinates = getGrid(obs))
## applying calibration
fcst.cal = calCCR(fcst, obs, crossval = TRUE, apply.to = "all")
## plotting climatologies
library(visualizeR)
spatialPlot(makeMultiGrid(climatology(obs),
                          climatology(fcst, by.member = FALSE),
                          climatology(fcst.cal, by.member = FALSE)),
           backdrop.theme = "coastline",
           layout = c(3, 1),
           names.attr = c("NCEP", "CFS (raw)", "CFS (calibrated)"))
}

SantanderMetGroup/calibratoR documentation built on July 8, 2023, 2:49 p.m.