Representing model error in ensemble data assimilation

Model error estimation in ensemble data assimilation

A new methodology is proposed to estimate and account for systematic model error in linear filtering as well as in nonlinear ensemble based filtering. Our results extend the work of Dee and Todling (2000) on constant bias errors to time-varying model errors. In contrast to existing methodologies, the new filter can also deal with the case where no dynamical model for the systematic error is ava...

Impacts of Model Error and Ensemble Initiation on Mesoscale Data Assimilation with an Ensemble Kalman Filter

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

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 parame...

Accounting for Model Errors in Ensemble Data Assimilation

This study addresses the issue of model errors with the ensemble Kalman filter. Observations generated from the NCEP–NCAR reanalysis fields are assimilated into a low-resolution AGCM. Without an effort to account for model errors, the performance of the local ensemble transform Kalman filter (LETKF) is seriously degraded when compared with the perfect-model scenario. Several methods to account ...

Ensemble-based atmospheric data assimilation

Ensemble-based data assimilation techniques are being explored as possible alternatives to current operational analysis techniques such as threeor four-dimensional variational assimilation. Ensemble-based assimilation techniques utilise an ensemble of parallel data assimilation and forecast cycles. The background-error covariances are estimated using the forecast ensemble and are used to produc...