TITLE

An Ensemble Multiscale Filter for Large Nonlinear Data Assimilation Problems

AUTHOR(S)
Yuhua Zhou; McLaughlin, Dennis; Entekhabi, Dara; Ng, Gene-Hua Crystal
PUB. DATE
February 2008
SOURCE
Monthly Weather Review;Feb2008, Vol. 136 Issue 2, p678
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Operational data assimilation problems tend to be very large, both in terms of the number of unknowns to be estimated and the number of measurements to be processed. This poses significant computational challenges, especially for ensemble methods, which are critically dependent on the number of replicates used to derive sample covariances and other statistics. Most efforts to deal with the related problems of computational effort and sampling error in ensemble estimation have focused on spatial localization. The ensemble multiscale Kalman filter described here offers an alternative approach that effectively replaces, at each update time, the prior (or background) sample covariance with a multiscale tree. The tree is composed of nodes distributed over a relatively small number of discrete scales. Global correlations between variables at different locations are described in terms of local relationships between nodes at adjacent scales (parents and children). The Kalman updating process can be carried out very efficiently on such a tree, especially if the update calculations exploit the tree’s parallel structure. In fact, the resulting savings in effort far exceeds the additional work required to construct the tree. The tree-identification process offers possibilities for introducing localization in scale, which can be used instead of or in addition to localization in space. The multiscale filter is able to continually adapt to changing problem scales through associated changes in the tree structure. This is illustrated with a large (106) unknown turbulent fluid flow example that generates dynamic features that span a wide range of time and space scales. This filter is able to track changing features over long distances without any spatial localization, using a moderate ensemble size of 54. The computational savings provided by the multiscale approach, combined with opportunities for hybrid localization over both space and scale, offer significant practical benefits for large data assimilation applications.
ACCESSION #
31295613

 

Related Articles

  • Resilience of Hybrid Ensemble/3DVAR Analysis Schemes to Model Error and Ensemble Covariance Error. Etherton, Brian J.; Bishop, Craig H. // Monthly Weather Review;May2004, Vol. 132 Issue 5, p1065 

    Previous idealized numerical experiments have shown that a straightforward augmentation of an isotropic error correlation matrix with an ensemble-based error correlation matrix yields an improved data assimilation scheme under certain conditions. Those conditions are (a) the forecast model is...

  • Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions. Delle Monache, Luca; Nipen, Thomas; Liu, Yubao; Roux, Gregory; Stull, Roland // Monthly Weather Review;Nov2011, Vol. 139 Issue 11, p3554 

    Two new postprocessing methods are proposed to reduce numerical weather prediction's systematic and random errors. The first method consists of running a postprocessing algorithm inspired by the Kalman filter (KF) through an ordered set of analog forecasts rather than a sequence of forecasts in...

  • On the Propagation of Information and the Use of Localization in Ensemble Kalman Filtering. Yoon, Young-noh; Ott, Edward; Szunyogh, Istvan // Journal of the Atmospheric Sciences;Dec2010, Vol. 67 Issue 12, p3823 

    Several localized versions of the ensemble Kalman filter have been proposed. Although tests applying such schemes have proven them to be extremely promising, a full basic understanding of the rationale and limitations of localization is currently lacking. It is one of the goals of this paper to...

  • A new localization implementation scheme for ensemble data assimilation of non-local observations. ZHU, JIANG; ZHENG, FEI; LI, XICHEN // Tellus: Series A;Mar2011, Vol. 63 Issue 2, p244 

    Localization technique is commonly used in ensemble data assimilation of small-size ensemble members. It effectively eliminates the spurious correlations of the background and increases the rank of the system. However, one disadvantage in current localization schemes is that it is difficult to...

  • A Comparison of Multiscale GSI-Based EnKF and 3DVar Data Assimilation Using Radar and Conventional Observations for Midlatitude Convective-Scale Precipitation Forecasts. Johnson, Aaron; Wang, Xuguang; Carley, Jacob R.; Wicker, Louis J.; Karstens, Christopher // Monthly Weather Review;Aug2015, Vol. 143 Issue 8, p3087 

    A GSI-based data assimilation (DA) system, including three-dimensional variational assimilation (3DVar) and ensemble Kalman filter (EnKF), is extended to the multiscale assimilation of both meso- and synoptic-scale observation networks and convective-scale radar reflectivity and velocity...

  • An Ensemble Kalman Smoother for Nonlinear Dynamics. Evensen, Geir; van Leeuwen, Peter Jan // Monthly Weather Review;Jun2000, Vol. 128 Issue 6, p1852 

    It is formally proved that the general smoother for nonlinear dynamics can be formulated as a sequential method, that is, observations can be assimilated sequentially during a forward integration. The general filter can be derived from the smoother and it is shown that the general smoother and...

  • Ensemble-Based Sensitivity Analysis Applied to African Easterly Waves. Torn, Ryan D. // Weather & Forecasting;Feb2010, Vol. 25 Issue 1, p61 

    An ensemble Kalman filter (EnKF) coupled to the Advanced Research version of the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts of a strong African easterly wave (AEW) during the African Monsoon Multidisciplinary Analysis field campaign. Ensemble...

  • Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging. Fraley, Chris; Raftery, Adrian E.; Gneiting, Tilmann // Monthly Weather Review;Jan2010, Vol. 138 Issue 1, p190 

    Bayesian model averaging (BMA) is a statistical postprocessing technique that generates calibrated and sharp predictive probability density functions (PDFs) from forecast ensembles. It represents the predictive PDF as a weighted average of PDFs centered on the bias-corrected ensemble members,...

  • A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography. Hoteit, I.; Pham, D.-T.; Triantafyllou, G.; Korres, G. // Monthly Weather Review;Jan2008, Vol. 136 Issue 1, p317 

    This paper introduces a new approximate solution of the optimal nonlinear filter suitable for nonlinear oceanic and atmospheric data assimilation problems. The method is based on a local linearization in a low-rank kernel representation of the state’s probability density function. In the...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

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

Try another library?
Sign out of this library

Other Topics