Adaptive Spatially-varying Variance Inflation in an Ensemble Filter

Details Adaptive Spatially-varying Variance Inflation in an Ensemble Filter Adaptive Spatially-varying Variance Inflation in an Ensemble Filter

May 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007agusm.a24b..03r&link_type=abstract

American Geophysical Union, Spring Meeting 2007, abstract #A24B-03

Physics

0300 Atmospheric Composition And Structure, 0343 Planetary Atmospheres (5210, 5405, 5704), 0350 Pressure, Density, And Temperature, 0394 Instruments And Techniques

Scientific paper

Ensemble filters are subject to errors arising from model deficiencies, representativeness error, and sampling error. In general, these errorslead to a systematic underestimate of the ensemble variance. This in turn can lead to reduced assimilation accuracy, insufficient spread for forecasts, and filter divergence in the worst case. Simple heuristic algorithms like inflation have been used to ameliorate this problem. However, it can be difficult to select appropriate inflation magnitudes. Even worse, if the spatial density of observations is not uniform, the inflation required in heavily observed regions can lead to filter divergence in sparsely observed regions. A hierarchical Bayesian algorithm that uses observations to produce a spatially- and temporally-varying inflation field has been developed to address this problem. The algorithm is implemented in the Data Assimilation Research Testbed and has been applied to a wide variety of global and regional prediction models. In this talk, results will be shown for assimilations using a global climate model (NCAR's Community Atmospheric Model) and the standard set of operational NWP observations. One month ensemble assimilations with and without adaptive inflation are compared and contrasted. The algorithm is successful in producing larger inflation in regions where dense observations make this necessary. A particular challenge occurs in areas where different observation types may have slightly different bias relative to the model.

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