ensembleBMA (version 2.1)

prcpFit: BMA Model Fit to Precipitation Data

Description

The default ensembleBMAgamma0 model fit to the precipitation data set from http://www.stat.washington.edu/MURI, which gives daily daily 48 hour forecasts of 24 hour accumulated precipitation over the US Pacific Northwest region from December 12, 2002 through March 31, 2005 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.

format

A list with the following arguments: dateTable{ A named vector in which the names are the dates and the entries are the number of observations for each date. } trainingRule{ The training rule used to compute the model fits. } prob0coefs{ The coefficients in the logistic regression for probability of zero precipitation. } biasCoefs{ The coefficients in the linear regression for bias correction. } varCoefs{ The variance coefficients of the models. } weights{ The BMA weights for the models. } transformation{ An R function giving transformation of the data used in the BMA fitting. } inverseTransformation{ An R function giving the inverse of transformation. }

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. 516, Department of Statistics, University of Washington, August 2007.

Examples

data(prcpFit)

  bmaModelParameters(prcpFit, date = "20030113")

  data(prcpGrid) 

  prcpGridData <- ensembleData(forecasts = prcpGrid[,1:9], 
                               latitude = prcpGrid[,"latitude"],
                               longitude = prcpGrid[,"longitude"])

# probability of precipitation
  1 - cdfBMA( prcpFit, prcpGridData, value = 0)

# probability of precipitation above 0.25 in
  1 - cdfBMA( prcpFit, prcpGridData, date = "20030113", value = 25)