Approach based on the concept of model error

2011 ◽  
pp. 165-184
Keyword(s):  
Model Error

Reduced model error analysis “application to synchronous machines"

1994 ◽  
Vol 4 (10) ◽  
pp. 1999-2012 ◽  
Author(s):  
Nabil Derbel ◽  
Mohamed B.A. Kamoun ◽  
Michel Poloujadoff
Keyword(s):  
Error Analysis ◽  
Model Error ◽  
Synchronous Machines ◽  
Reduced Model ◽  
Analysis Application

Quantifying the Predictive Skill in Long-Range Forecasting. Part II: Model Error in Coarse-Grained Markov Models with Application to Ocean-Circulation Regimes

2012 ◽  
Vol 25 (6) ◽  
pp. 1814-1826 ◽  
Author(s):  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Long Range ◽  
Ocean Circulation ◽  
Climate Models ◽  
Markov Models ◽  
Low Frequency ◽  
Model Error ◽  
Ocean Model ◽  
Coarse Grained ◽  
Predictive Skill ◽  
Relaxation To Equilibrium

Abstract An information-theoretic framework is developed to assess the predictive skill and model error in imperfect climate models for long-range forecasting. Here, of key importance is a climate equilibrium consistency test for detecting false predictive skill, as well as an analogous criterion describing model error during relaxation to equilibrium. Climate equilibrium consistency enforces the requirement that long-range forecasting models should reproduce the climatology of prediction observables with high fidelity. If a model meets both climate consistency and the analogous criterion describing model error during relaxation to equilibrium, then relative entropy can be used as an unbiased superensemble measure of the model’s skill in long-range coarse-grained forecasts. As an application, the authors investigate the error in modeling regime transitions in a 1.5-layer ocean model as a Markov process and identify models that are strongly persistent but their predictive skill is false. The general techniques developed here are also useful for estimating predictive skill with model error for Markov models of low-frequency atmospheric regimes.

scholarly journals Parameter Estimation Using Ensemble-Based Data Assimilation in the Presence of Model Error

2015 ◽  
Vol 143 (5) ◽  
pp. 1568-1582 ◽  
Author(s):  
Juan Ruiz ◽  
Manuel Pulido
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Model Error ◽  
Forecast Skill ◽  
Moist Convection ◽  
Treatment Techniques ◽  
Correction Technique ◽  
Error Treatment ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved.

Sign in / Sign up

Export Citation Format

Share Document