| Title: | Ensemble Postprocessing Data Sets |
|---|---|
| Description: | Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided. |
| Authors: | Jakob Messner [aut, cre] |
| Maintainer: | Jakob Messner <[email protected]> |
| License: | GPL-2 | GPL-3 |
| Version: | 1.0-0 |
| Built: | 2024-11-13 03:57:55 UTC |
| Source: | https://github.com/cran/ensemblepp |
Accumulated 18-30 hour precipitation ensemble forecasts and corresponding observations at Innsbruck. The dataset includes GEFS reforecasts (Hamill et al. 2013) and observations from SYNOP station Innsbruck Airport (11120) from 2000-01-02 to 2016-01-01.
data("temp")data("temp")
A data frame with 2749 rows. The first column (rain) are 12-hour
accumulated precipitation observations. Columns 2-12 (rainfc)
are 18-30 hour accumulated precipitation forecasts from the individual
ensemble members.
Observations: http://www.ogimet.com/synops.phtml.en
Reforecasts: http://www.esrl.noaa.gov/psd/forecasts/reforecast2/
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.
## Diagnostic plots similar to Figure 8 in Vannitsem et al. ## ## load and prepare data data("rain") rain <- sqrt(rain) rain$ensmean <- apply(rain[,2:12], 1, mean) rain$enssd <- apply(rain[,2:12], 1, sd) ## Scatterplot of precipitation by ensemble mean plot(rain~ensmean, rain, col = gray(0.2, alpha = 0.4), main = "Scatterplot") abline(0, 1, lty = 2) ## Verification rank histogram rank <- apply(rain[,1:12], 1, rank)[1,] hist(rank, breaks = 0:12 + 0.5, main = "Verification Rank Histogram") ## Spread skill relationship sdcat <- cut(rain$enssd, quantile(rain$enssd, seq(0, 1, 0.2))) boxplot(abs(rain-ensmean)~sdcat, rain, ylab = "absolute error", xlab = "ensemble standard deviation", main = "Spread-Skill") ## Histogram hist(rain$rain, xlab = "square root of precipitation", main = "Histogram")## Diagnostic plots similar to Figure 8 in Vannitsem et al. ## ## load and prepare data data("rain") rain <- sqrt(rain) rain$ensmean <- apply(rain[,2:12], 1, mean) rain$enssd <- apply(rain[,2:12], 1, sd) ## Scatterplot of precipitation by ensemble mean plot(rain~ensmean, rain, col = gray(0.2, alpha = 0.4), main = "Scatterplot") abline(0, 1, lty = 2) ## Verification rank histogram rank <- apply(rain[,1:12], 1, rank)[1,] hist(rank, breaks = 0:12 + 0.5, main = "Verification Rank Histogram") ## Spread skill relationship sdcat <- cut(rain$enssd, quantile(rain$enssd, seq(0, 1, 0.2))) boxplot(abs(rain-ensmean)~sdcat, rain, ylab = "absolute error", xlab = "ensemble standard deviation", main = "Spread-Skill") ## Histogram hist(rain$rain, xlab = "square root of precipitation", main = "Histogram")
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.
data("temp")data("temp")
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.
Observations: http://www.ogimet.com/synops.phtml.en
Reforecasts: http://www.esrl.noaa.gov/psd/forecasts/reforecast2/
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.
## 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$temp, xlab = "minimum temperature", main = "Histogram")## Diagnostic plots similar to Figure 1 and 3 in Vannitsem et al. ## ## load and prepare data data("temp") temp$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")