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Modeling and forecasting climate variables using a physical‐statistical approach
Author(s) -
Campbell Edward P.,
Palmer Mark J.
Publication year - 2010
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2009jd012030
Subject(s) - climate model , empirical modelling , data assimilation , climatology , downscaling , computer science , process (computing) , econometrics , climate change , meteorology , environmental science , mathematics , geography , precipitation , geology , simulation , oceanography , operating system
In climatology it is common for studies to use either process models derived from physical principles or empirical models, which are rarely combined in any formal way. In part, this is because it is difficult to develop process models for climate variables such as monthly or seasonal rainfall that may be thought of as outputs from complex physical processes. Models for these so‐called climate outputs therefore typically use empirical methods, often incorporating modeled data as predictors. Our application is concerned with using simplified models of the El Niño‐Southern Oscillation to drive forecasts of climate outputs such as monthly rainfall in southeast Australia. We develop a method to couple an empirical model with a process model in a sequential formulation familiar in data assimilation. This allows us to model climate outputs directly, and it offers potential for building new seasonal forecasting approaches drawing on the strengths of both empirical and physical modeling. It is also easy to update the model as more data become available.

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