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Since
we use the Morss and Emanuel (2001) simulation system, we
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know
the “truth” and analysis/forecast errors.
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We
compare the three methods showing
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the
total analysis error for a simulated year over the whole domain,
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and
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the
background error superimposed with the analysis increments.
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The
3D-Var parameters have been carefully tuned to get the best
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results.
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Note
that, because it is based on a statistically optimized background
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error
covariance, 3D-Var cannot capture the “errors of the day”.
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The
Local Bred Vector Kalman Filtering (Ott et al, 2002) do capture the
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errors
of the day. However, the bred vectors may “inbreed” to much
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and
miss some growing directions.
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In
order to avoid this, we add random perturbations (as if they were
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observational
errors) to the bred vectors.
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