Adaptive Kalman filtering for post-processing ensemble numerical weather predictions

Adaptive Kalman filtering for post-processing ensemble numerical weather predictions AbstractForecasts from numerical weather prediction models suffer from systematic and non-systematic errors, which originate from various sources such as model error and sub-grid variability. Statistical post-processing techniques can partly remove such errors.Adaptive MOS techniques based on Kalman filters (here called AMOS), are used to sequentially post-process the forecasts, by continuously updating the correction parameters as new ground observations become available. These techniques, originally proposed for deterministic forecasts, are valuable when long training data sets do not exist.Here, we introduce a new adaptive post-processing technique for ensemble predictions (called AEMOS). The proposed method implements a Kalman filtering approach that fully exploits the information content of the ensemble for updating the parameters of the post-processing equation.We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 10-meter wind speed and 2-meter temperature from 18 ground-based automatic weather stations, comparing them with the corresponding COSMO-LEPS ensemble forecasts.We show that the proposed adaptive method outperforms the AMOS method, while it shows comparable results to the Member-by-Member batch post-processing approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Adaptive Kalman filtering for post-processing ensemble numerical weather predictions

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
D.O.I.
10.1175/MWR-D-17-0084.1
Publisher site
See Article on Publisher Site

Abstract

AbstractForecasts from numerical weather prediction models suffer from systematic and non-systematic errors, which originate from various sources such as model error and sub-grid variability. Statistical post-processing techniques can partly remove such errors.Adaptive MOS techniques based on Kalman filters (here called AMOS), are used to sequentially post-process the forecasts, by continuously updating the correction parameters as new ground observations become available. These techniques, originally proposed for deterministic forecasts, are valuable when long training data sets do not exist.Here, we introduce a new adaptive post-processing technique for ensemble predictions (called AEMOS). The proposed method implements a Kalman filtering approach that fully exploits the information content of the ensemble for updating the parameters of the post-processing equation.We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 10-meter wind speed and 2-meter temperature from 18 ground-based automatic weather stations, comparing them with the corresponding COSMO-LEPS ensemble forecasts.We show that the proposed adaptive method outperforms the AMOS method, while it shows comparable results to the Member-by-Member batch post-processing approach.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Oct 2, 2017

References

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