Assimilating Satellite Observations with a Local Ensemble Kalman Filter
Assimilating Satellite Observations with a Local Ensemble Kalman Filter
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Date
2007-04-25
Authors
Fertig, Elana Judith
Advisor
Hunt, Brian R
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Abstract
Numerical weather prediction relies on data
assimilation to estimate the current state of the atmosphere.
Generally speaking, data assimilation methods combine information from
observations and from a prior forecast state, taking into account
their respective uncertainties. Ensemble-based data
assimilation schemes estimate the forecast uncertainty with the sample
covariance from an ensemble of forecasts. While these schemes have
been shown to successfully assimilate conventional observations of
model state variables, they have only recently begun to
assimilate satellite observations. This dissertation explores some of
the complications that arise when ensemble-based schemes
assimilate satellite observations.
Although ensemble data assimilation schemes often assume that
observations are taken at the time of assimilation, satellite
observations are available almost continuously between consecutive
assimilation times. In Chapter 2, we formulate a
``four-dimensional'' extension to ensemble-based schemes that is
analogous to the operationally used scheme 4D-VAR. Using perfect
model experiments with the Lorenz-96 model, we find that the
four-dimensional ensemble scheme can perform comparably to 4D-VAR.
Many ensemble data assimilation schemes utilize spatial
localization so that a small ensemble can capture the unstable degrees
of freedom in the model state. These local ensemble-based schemes
typically allow the analysis at a given location to depend only on
observations near that location. Meanwhile, the location of satellite
observations cannot be pinpointed in the same manner as conventional
observations. In Chapter 3, we propose a technique to update the
state at a given location by assimilating satellite radiance
observations that are strongly correlated to the model state there.
For satellite retrievals, we propose incorporating the observation
error covariance matrix and selecting the retrievals that have errors
correlated to observations near the location to be updated. Our
selection techniques improve the analysis obtained when assimilating
simulated satellite observations with a seven-layer primitive equation
model, the SPEEDY model.
Finally, satellite radiance observations are subject to
state-dependent, systematic errors due to errors in the radiative
transfer model used as the observation operator. In Chapter 4 we
propose applying state-space augmentation to ensemble based
assimilation schemes to estimate satellite radiance biases during the
data assimilation procedure. Our approach successfully corrects such
systematic errors in simulated biased satellite observations with the
SPEEDY model.