Editorial Type:
Article
Article Type:
Research Article

Simultaneous Ensemble Postprocessing for Multiple Lead Times with Standardized Anomalies

Markus Dabernig Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria

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Georg J. Mayr Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria

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Jakob W. Messner Institute of Atmospheric and Cryospheric Sciences, and Department of Statistics, University of Innsbruck, Innsbruck, Austria

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Achim Zeileis Department of Statistics, University of Innsbruck, Innsbruck, Austria

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Print Publication:
01 Jul 2017
DOI:
https://doi.org/10.1175/MWR-D-16-0413.1
Page(s):
2523–2531
Restricted access

Abstract

Separate statistical models are typically fit for each forecasting lead time to postprocess numerical weather prediction (NWP) ensemble forecasts. Using standardized anomalies of both NWP values and observations eliminates most of the lead-time-specific characteristics so that several lead times can be forecast simultaneously. Standardized anomalies are formed by subtracting a climatological mean and dividing by the climatological standard deviation. Simultaneously postprocessing forecasts between +12 and +120 h increases forecast coherence between lead times, yields a temporal resolution as high as the observation interval (e.g., up to 10 min), and speeds up computation times while achieving a forecast skill comparable to the conventional method.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society.

Corresponding author: Markus Dabernig, markus.dabernig@uibk.ac.at

Abstract

Separate statistical models are typically fit for each forecasting lead time to postprocess numerical weather prediction (NWP) ensemble forecasts. Using standardized anomalies of both NWP values and observations eliminates most of the lead-time-specific characteristics so that several lead times can be forecast simultaneously. Standardized anomalies are formed by subtracting a climatological mean and dividing by the climatological standard deviation. Simultaneously postprocessing forecasts between +12 and +120 h increases forecast coherence between lead times, yields a temporal resolution as high as the observation interval (e.g., up to 10 min), and speeds up computation times while achieving a forecast skill comparable to the conventional method.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society.

Corresponding author: Markus Dabernig, markus.dabernig@uibk.ac.at
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