WeatherProbs {scoring} | R Documentation |
Probabilistic forecasts from the U.S. National Oceanic and Atmospheric Administration, concerning below/near/above average temperatures and below/near/above median precipitation.
data("WeatherProbs")
A data frame with 8976 observations on the following 11 variables.
stn
Station World Meteorological Organization (WMO) number
made
Forecast issuance date
valid
Center of forecast valid period
tblw
Probability of below normal temperatures
tnrm
Probability of near normal temperatures
tabv
Probability of above normal temperatures
tcat
Realized temperature category (1=below, 2=near, 3=above)
pblw
Probability of below median precipitation
pnrm
Probability of near median precipitation
pabv
Probability of above median precipitation
pcat
Realized precipitation category (1=below, 2=near, 3=above)
The forecasts are valid for a period of 6 to 10 days from the date that the forecast was made. The forecasts were supplied every weekday during April, 2009, and they specifically predict the average temperature or total precipitation for the entire valid period.
Data were obtained from http://www.cpc.ncep.noaa.gov/products/archives/short_range/ (see URL in references).
See http://www.cpc.ncep.noaa.gov/products/archives/short_range/README.6-10day.txt for more details on the data.
For an application of similar data (different dates, same source), see:
Wilks, D. S. (in press). The calibration simplex: A generalization of the reliability diagram for 3-category probability forecasts. Weather and Forecasting.
data("WeatherProbs") ## Brier score for temperature forecasts ## (Warning arises because some forecast rows don't sum to 1.) res <- calcscore(tcat ~ tblw + tnrm + tabv, data=WeatherProbs, bounds=c(0,1)) ## Ordered Brier score for temperature forecasts res2 <- calcscore(tcat ~ tblw + tnrm + tabv, data=WeatherProbs, bounds=c(0,1), ordered=TRUE) ## Spherical score for temperature forecasts res3 <- calcscore(tcat ~ tblw + tnrm + tabv, data=WeatherProbs, fam="sph", bounds=c(0,1)) ## Average scores by station avgbrier <- with(WeatherProbs, tapply(res, stn, mean)) avgobrier <- with(WeatherProbs, tapply(res2, stn, mean)) avgsph <- with(WeatherProbs, tapply(res3, stn, mean)) ## Conclusions vary across Brier and ordinal Brier scores plot(avgbrier, avgobrier, pch=20, xlab="Brier", ylab="Ordinal Brier")