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Entropy‐based member selection in a GCM ensemble forecasting
Author(s) -
Tapiador F. J.,
Gallardo C.
Publication year - 2006
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2005gl024888
Subject(s) - sensitivity (control systems) , nonlinear system , ensemble forecasting , gcm transcription factors , computer science , entropy (arrow of time) , climate model , mathematics , general circulation model , climate change , artificial intelligence , geology , physics , oceanography , quantum mechanics , electronic engineering , engineering
We present a method for choosing a member in a global circulation model (GCM) ensemble forecasting. We show that the r 2 correlation between members and independent data is related to the flux of information entropy contained into the forecasts. This allows to classify the members in terms of goodness‐of‐fit. To validate the method we use several ECMWF‐ensemble forecast from the DEMETER project, and the ERA‐40 database. We compare ERA‐40 actual estimates with our entropy‐based choice of the DEMETER members in each of the 180 days, obtaining consistent results. Our hypothesis is based on energy‐balance, maximum entropy production considerations so that the method might only be applicable to global simulations.