Article ID Journal Published Year Pages File Type
10156628 Physica D: Nonlinear Phenomena 2018 25 Pages PDF
Abstract
Artificial ensemble inflation is a common technique in ensemble data assimilation, whereby the ensemble covariance is periodically increased in order to prevent deviation of the ensemble from the observations and possible ensemble collapse. This manuscript introduces a new form of covariance inflation for ensemble data assimilation based upon shadowing ideas from dynamical systems theory. We present results from a low order nonlinear chaotic system that support using shadowing inflation, demonstrating that shadowing inflation is more robust to parameter tuning than standard multiplicative covariance inflation, often leading to longer forecast shadowing times.
Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
Authors
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