Initiation of ensemble data assimilation

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

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...

Ensemble-based chemical data assimilation

Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission A. F. Arellano Jr., K. Raeder, J. L. Anderson, P. G. Hess, L. K. Emmons, D. P. Edwards, G. G. Pfister, T. L. Campos, and G. W. Sachse Atmospheric Chemistry Division, Earth and Sun Systems Laboratory, National Center for Atmospheric Research, PO Box 3000, Boulder, Colorado 80307-3000,...

A review of operational methods of variational and ensemble-variational data assimilation: Ensemble-variational data assimilation

Variational and ensemble methods have been developed separately by various research and development groups and each brings its own benefits to data assimilation. In the last decade or so, various ways have been developed to combine these methods, especially with the aims of improving the background-error covariance matrices and of improving efficiency. The field has become confusing, even to ma...

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...