Open AccessComparison of ensemble‐MOS methods in the Lorenz '96 setting
Author(s) -
Wilks D. S.
Publication year - 2006
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1017/s1350482706002192
Subject(s) - quantile , variance (accounting) , statistics , ensemble forecasting , ensemble learning , kernel density estimation , econometrics , computer science , statistical ensemble , suite , mathematics , artificial intelligence , canonical ensemble , monte carlo method , geography , accounting , archaeology , estimator , business
A suite of methods that have been proposed for statistical post‐processing of ensemble forecasts based on historical verification data (i.e. ensemble‐MOS methods) are compared with each other, and with direct probability estimates using ensemble relative frequencies, in the idealised Lorenz '96 setting. The three most promising methods are logistic regressions predicting probabilities associated with selected quantiles, ensemble dressing (a kernel density estimation approach), and linear regressions with non‐constant prediction errors that depend on the ensemble variance. Copyright © 2006 John Wiley & Sons, Ltd.