Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations
نویسندگان
چکیده
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 vary both in space and in time. Here, these covariances are generated using an ensemble Kalman filter assimilation scheme. A variant is also developed that can estimate the analysis improvement in data assimilation schemes where background error statistics are
منابع مشابه
A New Adaptive Extended Kalman Filter for a Class of Nonlinear Systems
This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the n...
متن کاملEnsemble-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...
متن کاملAdaptive ensemble Kalman filtering of nonlinear systems
A necessary ingredient of an ensemble Kalman filter is covariance inflation [1], used to control filter divergence and compensate for model error. There is an ongoing search for inflation tunings that can be learned adaptively. Early in the development of Kalman filtering, Mehra [2] enabled adaptivity in the context of linear dynamics with white noise model errors by showing how to estimate the...
متن کاملTitle of Document : CARBON CYCLE DATA ASSIMILATION USING A COUPLED ATMOSPHERE - VEGETATION MODEL AND THE LOCAL ENSEMBLE TRANSFORM KALMAN FILTER
Title of Document: CARBON CYCLE DATA ASSIMILATION USING A COUPLED ATMOSPHEREVEGETATION MODEL AND THE LOCAL ENSEMBLE TRANSFORM KALMAN FILTER Ji Sun Kang, Doctor of Philosophy, 2009 Directed By: Professor Eugenia Kalnay Department of Atmospheric and Oceanic Science We develop and test new methodologies to best estimate CO2 fluxes on the Earth’s surface by assimilating observations of atmospheric ...
متن کاملA Hybrid Ensemble Kalman Filter / 3d-variational Analysis Scheme
Given the chaotic nature of the atmosphere, ensemble forecasting is increasingly being embraced as an approach for providing probabilistic weather forecasts. The best method for determining a set of initial conditions for ensemble forecasts is still being debated. We have found that there are appealing characteristics to ensembles of forecasts generated by the perturbed observation (PO) method ...
متن کامل