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ensembleBMA (version 5.1.8)

prcpDJdata: Precipitation Data

Description

A subset of daily 48 hour forecasts of 24 hour accumulated precipitation over the US Pacific Northwest region from December 2002 to January 2003 based on a 9 member version of the University of Washington mesoscale ensemble (Grimit and Mass 2002; Eckel and Mass 2005). Precipitation amounts are quantized to hundredths of an inch.
Note that forecasts are not available for some of the interim dates.

Arguments

Format

A data frame with 175 rows and 15 columns:
CENT,AVN,CMCG,ETA,GASP,JMA,NGAPS,TCWB,UKMO forecasts from the 9 members of the ensemble (numeric).
observation the observed accumulated precipitation (numeric).
date the date of each forecast/observation, format YYYYMMDDHH (categorical).
station weather station identifier (categorical).
latitude the latitude of each weather station (numeric).
longitude the longitude of each weather station (numeric).
elevation the elevation of each weather station (numeric).

Details

This dataset is a small subset of the data used in Sloughter et al. (2006), provided for the purposes of testing. Typically forecasting would be performed on much larger datasets.

References

E. P. Grimit and C. F. Mass, Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest, Weather and Forecasting 17:192--205, 2002.

F. A. Eckel and C. F. Mass, Effective mesoscale, short-range ensemble forecasting, Weather and Forecasting 20:328--350, 2005.

J. M. Sloughter, A. E. Raftery, T. Gneiting and C. Fraley, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review 135:3309--3320, 2007.

C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter, ensembleBMA: An R Package for Probabilistic Forecasting using Ensembles and Bayesian Model Averaging, Technical Report No. 516R, Department of Statistics, University of Washington, 2007 (revised 2010).

Examples

Run this code
if (FALSE)  # R check

 data(prcpDJdata)
 data(prcpFit)

 prcpForc <- quantileForecast( prcpFit, prcpDJdata, date = "20030113",
                               quantiles = c( .1, .5, .9))

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