Using lagged covariances in data assimilation

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...

Modeling Retrieval Error Covariances for Global Data Assimilation

Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations

A method for determining adaptive observation locations is demonstrated. This method is based on optimal estimation (Kalman filter) theory; it determines the observation location that will maximize the expected improvement, which can be measured in terms of the expected reduction in analysis or forecast variance. This technique requires an accurate model for background error statistics that var...

Suboptimal Schemes for Atmospheric Data Assimilation Based on the Kalman Filter

This work is directed toward approximating the evolution of forecast error covariances for data assimilation. We study the performance of di erent algorithms based on simpli cation of the standard Kalman lter (KF). These are suboptimal schemes (SOS's) when compared to the KF, which is optimal for linear problems with known statistics. The SOS's considered here are several versions of optimal in...

Estimating Forecast Error Covariances for Strongly Coupled Atmosphere–Ocean 4D-Var Data Assimilation

Strongly coupled data assimilation emulates the real-world pairing of the atmosphere andocean by solving the assimilation problem in terms of a single combined atmosphere–ocean state. A significant challenge in strongly coupled variational atmosphere–ocean data assimilation is a priori specification of the cross covariances between the errors in the atmosphere and ocean model forecasts. These c...