Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods

Ochotta, Tilo; Gebhardt, Christoph; Saupe, Dietmar; Wergen, Werner

Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods

Dateien

Ochotta_240780.pdfGröße: 522.12 KBDownloads: 445

Datum

2005

Autor:innen

Ochotta, Tilo

Gebhardt, Christoph

Saupe, Dietmar

Wergen, Werner

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Quarterly Journal of the Royal Meteorological Society. 2005, 131(613), pp. 3427-3437. ISSN 0035-9009. eISSN 1477-870X. Available under: doi: 10.1256/qj.05.94

Zusammenfassung

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 numbers of measurements with high spatial and temporal density. Such datasets 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 evaluate the proposed methods with respect to a geometric error measure and compare 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 evaluate our thinnings of ATOVS satellite data using the assimilation system of the Deutscher Wetterdienst. Impact of the thinning on the analysed fields and on the subsequent forecasts is discussed.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

Clustering, numerical weather prediction, redundant satellite data

Zitieren

ISO 690

OCHOTTA, Tilo, Christoph GEBHARDT, Dietmar SAUPE, Werner WERGEN, 2005. Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods. In: Quarterly Journal of the Royal Meteorological Society. 2005, 131(613), pp. 3427-3437. ISSN 0035-9009. eISSN 1477-870X. Available under: doi: 10.1256/qj.05.94

BibTex

@article{Ochotta2005Adapt-24078,
  year={2005},
  doi={10.1256/qj.05.94},
  title={Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods},
  number={613},
  volume={131},
  issn={0035-9009},
  journal={Quarterly Journal of the Royal Meteorological Society},
  pages={3427--3437},
  author={Ochotta, Tilo and Gebhardt, Christoph and Saupe, Dietmar and Wergen, Werner}
}

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Universitätsbibliographie

Ja