tercilePlot: Tercile plot for visualization of forecast skill of seasonal...

In SantanderMetGroup/visualizeR: Visualizing climate4R data and Communicating Uncertainty in Seasonal Climate Prediction

Tercile plot for visualization of forecast skill of seasonal climate predictions.

Description

Tercile plot for the visualization of forecast skill of seasonal climate predictions. This function is prepared to plot the data sets loaded from the ECOMS User Data Gateway (ECOMS-UDG). See the loadeR.ECOMS R package for more details (http://meteo.unican.es/trac/wiki/udg/ecoms/RPackage).

Usage

tercilePlot(
  hindcast,
  obs,
  forecast = NULL,
  year.target = NULL,
  detrend = FALSE,
  conf.level = 0.95,
  color.pal = c("bw", "reds", "ypb", "tcolor"),
  subtitle = NULL
)

Arguments

hindcast	A multi-member list with the hindcast for verification. See details.
obs	List with the benchmarking observations for forecast verification.
forecast	A multi-member list with the forecasts. Default is NULL.
year.target	Year within the hindcast period considered as forecast. Default is NULL.
detrend	Logical indicating if the data should be linear detrended. Default is FALSE.
conf.level	Confidence level to compute the score significance, by default conf.level=0.95
color.pal	Color palette for the representation of the probabilities. Default to "bw" (black and white). "reds" for a white-red transition, "ypb" for a yellow-pink-blue transition or "tcolor" for a colorbar for each tercile, blue-grey-red for below, normal and above terciles, respectively.
subtitle	String to include a subtitle bellow the title. Default is NULL.

Details

First daily data are averaged to obtain a single seasonal value. For rectangular spatial domains (i.e., for fields), the spatial average is first computed (with a warning) to obtain a unique series for the whole domain. The corresponding terciles for each ensemble member are then computed for the hindcast period. Thus, each particular member and season, are categorized into three categories (above, between or below), according to their respective climatological terciles. Then, a probabilistic forecast is computed year by year by considering the number of members falling within each category. This probability is represented by the colorbar. For instance, probabilities below 1/3 are very low, indicating that a minority of the members falls in the tercile. Conversely, probabilities above 2/3 indicate a high level of member agreement (more than 66% of members falling in the same tercile). The observed terciles (the events that actually occurred) are represented by the white circles. If the forecast object is not NULL, then the probabilities for this season are also ploted next to the hindcast results.

Finally, the ROC Skill Score (ROCSS) is computed for the hindcasts. It is indicated in the secondary (right) Y axis. For each tercile, it provides a quantitative measure of the forecast skill, and it is commonly used to evaluate the performance of probabilistic systems (Joliffe and Stephenson 2003). The value of this score ranges from 1 (perfect forecast system) to -1 (perfectly bad forecast system). A value zero indicates no skill compared with a random prediction. If year.target is not NULL, this year is not included in the computation of the score (operational point of view). Significance of the Area under the ROC curve is highlighted with an *.

In case of multi-member fields or stations, they are spatially averaged to obtain one single time series for each member prior to data analysis, with a warning.

Note

The computation of climatological terciles requires a representative period to obtain meaningful results.

Author(s)

M.D. [e-mail] and J. Fernandez based on the original diagram conceived by A. Cofino (See Diez et al, 2011 for more details).

References

Diez, E., Orfila, B., Frias, M.D., Fernandez, J., Cofino, A.S., Gutierrez, J.M., 2011. Downscaling ECMWF seasonal precipitation forecasts in Europe using the RCA model. Tellus A 63, 757-762. doi:10.1111/j.1600-0870.2011.00523.x

Jolliffe, I. T. and Stephenson, D. B. 2003. Forecast Verification: A Practitioner's Guide in Atmospheri Science, Wiley, NY

See Also

Other visualization functions: bubblePlot(), cascadePlot(), climagram(), reliabilityCategories(), spreadPlot(), tercileBarplot()

Examples

## Not run: 
data(tas.cfs)
data(tas.cfs.operative.2016)
data(tas.ncep)
require(transformeR)
# Select spatial domain
tas.ncep2 <- subsetGrid(tas.ncep, lonLim = c(-80, -35), latLim = c(-12, 12))
tas.cfs2 <- subsetGrid(tas.cfs, lonLim = c(-80, -35), latLim = c(-12, 12))
tas.cfs.operative2.2016 <- subsetGrid(tas.cfs.operative.2016, 
                           lonLim = c(-80, -35), latLim = c(-12, 12))
# Tercile plot
tercilePlot(hindcast = tas.cfs2, obs = tas.ncep2, forecast = tas.cfs.operative2.2016)

## End(Not run)

SantanderMetGroup/visualizeR documentation built on Oct. 28, 2023, 6:11 a.m.