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

Hamill, Thomas M.; Snyder, Chris
June 2002
Monthly Weather Review;Jun2002, Vol. 130 Issue 6, p1552
Academic Journal
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 less accurate. This approach is demonstrated using a quasigeostrophic channel model under perfect-model assumptions. The algorithm is applied here to find the supplemental rawinsonde location to add to a regular network of rawinsondes that will reduce analysis errors the most. The observation network is configured in this experiment so there is a data void in the western third of the domain. One-hundred-member ensembles from three data assimilation schemes are tested as input to the target selection procedure, two variants of the standard ensemble Kalman filter and a third perturbed observation (3DVAR) ensemble. The algorithm is shown to find large differences in the expected variance reduction depending on the observation location, the flow of the day, and the ensemble used in the adaptive observation algorithm. When using the two variants of the ensemble Kalman filter, the algorithm defined consistently similar adaptive locations to each other, and assimilation of the adaptive observation typically reduced analysis errors significantly. When the 3DVAR ensemble was used, the algorithm picked very different observation locations and the analyses were not improved as much. The amount of improvement from assimilating a supplemental adaptive observation instead of a fixed observation in the middle of the void depended on...


Related Articles

  • Ensemble Kalman filtering with residual nudging. Luo, Xiaodong; Hoteit, Ibrahim // Tellus: Series A;2012, Vol. 64, p1 

    Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual...

  • A Kalman filter application to a spectral wave model. Pinto, J. P.; Bernardino, M. C.; Silva, A. Pires; Vannitsem, S. // Nonlinear Processes in Geophysics;2005, Vol. 12 Issue 6, p775 

    A sequential time dependent data assimilation scheme based on the Kalman filter is applied to a spectral wave model. Usually, the first guess covariance matrices used in optimal interpolation schemes are exponential spreading functions, which remain constant. In the present work the first guess...

  • Constraint in Attitude Estimation Part II: Unconstrained Estimation. Shuster, Malcolm D. // Journal of the Astronautical Sciences;Jan-Mar2003, Vol. 51 Issue 1, p75 

    The consequences of ignoring the norm constraint in quaternion estimation or the orthogonality constraint in the estimation of the attitude matrix are examined within the framework of batch maximum-likelihood estimation. Unconstrained estimation of the attitude matrix is shown to be a useful...

  • High Maneuvering Target Tracking Using Fuzzy Covariance Presetting. Beheshtipour, Z.; Khaloozadeh, H. // Journal of Applied Sciences;2008, Vol. 8 Issue 20, p3630 

    In this study, a new covariance presetting scheme is presented to overcome some drawbacks of the high maneuvering target tracking problems by using the Fuzzy logic method for evaluating the elements of the covariance matrix presetting. This scheme includes an estimation part that uses a modified...

  • Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter. Luo, Xiaodong; Hoteit, Ibrahim // Monthly Weather Review;Dec2011, Vol. 139 Issue 12, p3938 

    A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter. By design, the H∞...

  • Relation between two common localisation methods for the EnKF. Sakov, Pavel; Bertino, Laurent // Computational Geosciences;Mar2011, Vol. 15 Issue 2, p225 

    This study investigates the relation between two common localisation methods in ensemble Kalman filter (EnKF) systems: covariance localisation and local analysis. Both methods are popular in large-scale applications with the EnKF. The case of local observations with non-correlated errors is...

  • An Ensemble Ocean Data Assimilation System for Seasonal Prediction. Yin, Yonghong; Alves, Oscar; Oke, Peter R. // Monthly Weather Review;Mar2011, Vol. 139 Issue 3, p786 

    A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS, the POAMA Ensemble Ocean Data Assimilation System. PEODAS is an approximate form of an ensemble Kalman filter system. For a...

  • A Study of Coupling Parameter Estimation Implemented by 4D-Var and EnKF with a Simple Coupled System. Han, Guijun; Wu, Xinrong; Zhang, Shaoqing; Liu, Zhengyu; Navon, Ionel Michael; Li, Wei // Advances in Meteorology;8/6/2015, Vol. 2015, p1 

    Coupling parameter estimation (CPE) that uses observations to estimate the parameters in a coupled model through error covariance between variables residing in different media may increase the consistency of estimated parameters in an air-sea coupled system. However, it is very challenging to...

  • An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter. Hajoon Song; Hoteit, Ibrahim; Cornuelle, Bruce D.; Subramanian, Aneesh C. // Monthly Weather Review;Jul2010, Vol. 138 Issue 7, p2825 

    A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics