||Ochotta, T., Gebhardt, C., Saupe, D., Wergen, W.
||Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods
||In data assimilation for numerical weather prediction, measurements of
various observation systems are combined with background data to define
initial states for the forecasts. Current and future observation systems,
in particular satellite instruments, produce large amounts of
measurements with high spatial and temporal density. Such data sets
significantly increase the computational costs of the assimilation and,
moreover, can violate the assumption of spatially independent
observation errors. To ameliorate these problems, we propose two greedy
thinning algorithms, which reduce the number of assimilated observations
while retaining the essential information content of the data.
In the first method the number of points in the output set is increased
iteratively. We use a clustering method with a distance metric that
combines spatial distance with difference in observation values.
In a second scheme we iteratively estimate the redundancy of the current
observation set and remove the most redundant data points.
We evaluated the proposed methods with respect to a geometric error
measure and compared them with a uniform sampling scheme.
We obtain good representations of the original data
with thinnings retaining only a small portion of observations.
We also evaluated our thinnings of ATOVS satellite data using
the assimilation system of the Deutsche Wetterdienst.
Impact of the thinning on the analysed fields and on the subsequent
forecasts is discussed.