tercileBarplot: Tercile bar plot for visualization of forecast skill of...

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

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

Description

Tercile bar plot for visualization of the skill of seasonal climate predictions for a particular year. 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

tercileBarplot(
  hindcast,
  obs,
  forecast = NULL,
  year.target = NULL,
  detrend = FALSE,
  conf.level = 0.95,
  score.threshold = NULL,
  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
score.threshold	Threshold to remark high positive score values in the figure.
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. The probability for the the forecast or selected year is represented by the bars. The 1/3 probability is plotted by a grey line. For instance, probabilities below this line 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).

Finally, the ROC Skill Score (ROCSS) is indicated at the bottom part of the bar plot 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). Zero indicates no skill compared with a random prediction. The selected year is not included in the computation of the score (operational point of view). Negative values are written in red while high positive values are in blue. The threshold to highlight high positive values can be modified with the score.threshold argument. 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. Frias [e-mail] and J. Fernandez

References

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(), tercilePlot()

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 bar plot
tercileBarplot(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.