Data Assimilation Meetings at Reading

Date Meeting type Speakers
5 June 2015
Meteorology 1L61
Invited speaker Jeff Anderson (NCAR)
Automated Design of Localization for Ensemble Kalman Filter (pdf)
High performance ensemble Kalman filters for geophysical applications have required empirical methods to correct for errors in ensemble covariances. The two most common approaches are inflation to control errors in the variance and localization to control errors in correlation. Adaptive methods that automatically estimate appropriate values of inflation as an integral part of the ensemble filter process have been used for a number of years and work well for most applications. Here, an alternative to localization is described in which errors in the ensemble correlations are assumed to be due to sampling errors from the use of small ensembles. An explicit estimate of the probability distribution function (PDF) for the correlation between an observation and a state variable is produced as part of the filtering algorithm. The maximum likelihood value of this correlation distribution is then used in the ensemble filter instead of the ensemble sample correlation. Results are shown for applications of this correlation error reduction method in observing system simulation experiments. The method is able to produce ensemble analyses with significantly smaller root mean square error than can be achieved with carefully tuned traditional localization functions in some cases. When combined with adaptive inflation, this method may be able to produce good assimilations for many types of geophysical problems without tuning.

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