| Author(s) | Ochotta, T., Gebhardt, C., Saupe, D., Wergen, W. |
| Title | Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods |
| Abstract | 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. |
| Download | OcGeSaWe05.pdf |