temp {ensemblepp}

Minimum Temperature Observations and Forecasts for Innsbruck

Description

18-30 hour minimum temperature ensemble forecasts and corresponding observations at Innsbruck. The dataset includes GEFS reforecasts (Hamill et al. 2013) and observations from the SYNOP station Innsbruck Airport (11120) from 2000-01-02 to 2016-01-01.

Usage

data("temp")

Format

A data frame with 2749 rows. The first column (temp) are 12-hour minimum temperature observations. Columns 2-12 (tempfc) are 18-30 hour minimum temperature forecasts from the individual ensemble members.

Source

Observations: http://www.ogimet.com/synops.phtml.en

Reforecasts: http://www.esrl.noaa.gov/psd/forecasts/reforecast2/

References

Hamill TM, Bates GT, Whitaker JS, Murray DR, Fiorino M, Galarneau Jr TJ, Zhu Y, Lapenta W (2013). NOAA's Second-Generation Global Medium-Range Ensemble Reforecast Data Set. Bulletin of the American Meteorological Society, 94(10), 1553-1565.

Vannitsem S, Wilks DS, Messner JW (2017). Statistical Postprocessing of Ensemble Forecasts, Elsevier, to appear.

Examples

## Diagnostic plots similar to Figure 1 and 3 in Vannitsem et al. ##

## load and prepare data
data("temp")

temp$ensmean <- apply(temp[,2:12], 1, mean)
temp$enssd <- apply(temp[,2:12], 1, sd)

## Scatterplot of minimum temperature observation by ensemble mean
plot(temp~ensmean, temp, main = "Scatterplot")
abline(0, 1, lty = 2)

## Verification rank histogram
rank <- apply(temp[,1:12], 1, rank)[1,]
hist(rank, breaks = 0:12 + 0.5, main = "Verification Rank Histogram")

## Spread skill relationship
sdcat <- cut(temp$enssd, breaks = quantile(temp$enssd, seq(0, 1, 0.2)))
boxplot(abs(temp-ensmean)~sdcat, temp, ylab = "absolute error",
xlab = "ensemble standard deviation", main = "Spread-Skill")

## Histogram
hist(temp$temp, xlab = "minimum temperature", main = "Histogram")