HR: 16:15h
AN: A14A-04    [Abstracts]
TI: Ensemble forecasts and seasonal precipitation tercile probabilities
AU: * Tippett, M K
EM: tippett@iri.columbia.edu
AF: International Research Institute for Climate Prediction, The Earth Institute at Columbia University, Lamont Campus / 61 Route 9W, Palisades, NY 10964 United States
AU: Barnston, A G
EM: tonyb@iri.columbia.edu
AF: International Research Institute for Climate Prediction, The Earth Institute at Columbia University, Lamont Campus / 61 Route 9W, Palisades, NY 10964 United States
AU: Robertson, A W
EM: awr@iri.columbia.edu
AF: International Research Institute for Climate Prediction, The Earth Institute at Columbia University, Lamont Campus / 61 Route 9W, Palisades, NY 10964 United States
AB: Seasonal forecasts of precipitation and near-surface temperature are often issued in terms of expected probabilities of tercile categories. A simple method of estimating precipitation tercile probabilities from seasonal ensemble forecasts is to use relative ensemble frequencies, that is, to count the number of ensemble members in each category. Such a nonparametric estimate is affected by sampling error, especially for modest sized ensembles like those used in seasonal forecasting. Previous work has shown the benefit of estimating tercile probabilities from parametric distributions fit to ensemble output, though variables like precipitation with nonnormal distributions provide challenges. Here we compare parametric and nonparametric methods of estimating tercile probabilities to an empirical approach using a generalized linear model. The empirical approach allows us to quantify the ensemble information necessary to determine tercile probabilities, and in particular, to determine the relative importance of ensemble mean and spread on tercile probabilities. Results are obtained using idealized models and output from general circulation atmospheric models.
DE: 3354 Precipitation (1854)
SC: Atmospheric Sciences [A]
MN: 2005 Joint Assembly