Ensemble Square Root Filters*

Michael K. Tippett, Jeffrey L. Anderson, Craig H. Bishop, Thomas M. Hamill, Jeffrey S. Whitaker
<span title="">2003</span> <i title="American Meteorological Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xq4uy4sfzndvvhfl2jn3avlqey" style="color: black;">Monthly Weather Review</a> </i> &nbsp;
Ensemble data assimilation methods assimilate observations using state-space estimation methods and low-rank representations of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics. This transformation may be preformed stochastically by treating observations as random variables, or deterministically by requiring that the updated analysis perturbations satisfy the Kalman filter
more &raquo; ... nalysis error covariance equation. Deterministic analysis ensemble updates are implementations of Kalman square-root filters. The nonuniqueness of the deterministic transformation used in square-root Kalman filters provides a framework to compare three recently proposed ensemble data assimilation methods.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1175/1520-0493(2003)131&lt;1485:esrf&gt;2.0.co;2">doi:10.1175/1520-0493(2003)131&lt;1485:esrf&gt;2.0.co;2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gh4ch6i6c5du5ffhuat5jn5a4e">fatcat:gh4ch6i6c5du5ffhuat5jn5a4e</a> </span>
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