reshuffle_members: Reshuffle ensemble members using template

In katerobsau/depPPR: deppR - Dependence restoration of statistical post-processed weather forecasts in R

Reshuffle ensemble members using template

Description

This function reshuffles the members of a post-processed ensemble forecast using a template to restore a meaningful dependence structure.

Usage

reshuffle_members(forecast, template)

Arguments

forecast	is a matrix where the columns correspond to members with the unrealistic dependence structure
template	is a matrix where the columns correspond to the template for dependence structure

Details

For ensemble members that have been sampled from a post-processed forecast distribution the members need to be reshuffled to restore a meaningful dependence structure in space, time or both.

This function uses a template to reshuffle the sampled ensemble members. There are many option for template selection. In the Schaake shuffle and its variants the template uses past observations. In Empirical Coupla Coupling, the dependence template is inherited from the raw ensemble forecast.

Value

a matrix where the members have been reshuffled according to the dependence template

Author(s)

Kate Saunders and Kirien Whan

References

Clark, Martyn, et al. "The Schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields." Journal of Hydrometeorology 5.1 (2004): 243-262.

Schefzik, Roman, Thordis L. Thorarinsdottir, and Tilmann Gneiting. "Uncertainty quantification in complex simulation models using ensemble copula coupling." Statistical science 28.4 (2013): 616-640.

Hu, Yiming, et al. "A stratified sampling approach for improved sampling from a calibrated ensemble forecast distribution." Journal of Hydrometeorology 17.9 (2016): 2405-2417.

Examples

d <- 3  # number of dimensions (space x time)
m <- 5  # number of members (sampled from the post-process forecast distribution)

set.seed(1)
eg_forecast = matrix(rnorm(d*m), nrow = d)

eg_template = matrix(rnorm(d*m), nrow = d)

forecast_with_dependence = reshuffle_members(eg_forecast, eg_template)

# Checking the forecast was shuffled correctly using the template
order(eg_forecast[1,])
order(eg_template[1,])
order(forecast_with_dependence[1,])

# Checking the forecast still contains the same members after shuffling
sort(eg_forecast[1,])
sort(forecast_with_dependence[1,])

katerobsau/depPPR documentation built on July 2, 2023, 10:48 p.m.