Parametric Postprocessing of Dual-Resolution Precipitation Forecasts

Marianna Szabó aFaculty of Informatics, University of Debrecen, Debrecen, Hungary
bDoctoral School of Informatics, University of Debrecen, Debrecen, Hungary

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Estíbaliz Gascón cEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Sándor Baran aFaculty of Informatics, University of Debrecen, Debrecen, Hungary

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https://orcid.org/0000-0003-1035-004X
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Abstract

All major weather centers issue ensemble forecasts, which differ both in ensemble size and spatial resolution, even while covering the same domain. These parameters directly determine both the forecast skill of the prediction and the computation cost. In the past few years, the plans for upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) from a single forecast with 9-km resolution and a 51-member ensemble with 18-km resolution induced an extensive study of the forecast skill of both raw and postprocessed dual-resolution predictions comprising ensemble members of different horizontal resolutions. We investigate the predictive performance of the censored shifted gamma (CSG) ensemble model output statistic (EMOS) approach for statistical postprocessing with the help of dual-resolution, 24-h, precipitation accumulation ensemble forecasts over Europe with various forecast horizons. We consider the operational 50-member ECMWF ensemble as of high resolution and extend it with a low-resolution (29-km grid), 200-member experimental forecast. The investigated dual-resolution combinations consist of subsets of these two forecast ensembles with equal computational cost, which is equivalent to the cost of the operational ensemble. Our case study verifies that, compared with the raw ensemble combinations, EMOS postprocessing results in a significant improvement in forecast skill and that skill is statistically indistinguishable between any of the analyzed mixtures of dual-resolution combinations. Furthermore, the semilocally trained CSG EMOS provides an efficient alternative to the state-of-the-art quantile mapping without requiring additional historical data.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sándor Baran, baran.sandor@inf.unideb.hu

Abstract

All major weather centers issue ensemble forecasts, which differ both in ensemble size and spatial resolution, even while covering the same domain. These parameters directly determine both the forecast skill of the prediction and the computation cost. In the past few years, the plans for upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) from a single forecast with 9-km resolution and a 51-member ensemble with 18-km resolution induced an extensive study of the forecast skill of both raw and postprocessed dual-resolution predictions comprising ensemble members of different horizontal resolutions. We investigate the predictive performance of the censored shifted gamma (CSG) ensemble model output statistic (EMOS) approach for statistical postprocessing with the help of dual-resolution, 24-h, precipitation accumulation ensemble forecasts over Europe with various forecast horizons. We consider the operational 50-member ECMWF ensemble as of high resolution and extend it with a low-resolution (29-km grid), 200-member experimental forecast. The investigated dual-resolution combinations consist of subsets of these two forecast ensembles with equal computational cost, which is equivalent to the cost of the operational ensemble. Our case study verifies that, compared with the raw ensemble combinations, EMOS postprocessing results in a significant improvement in forecast skill and that skill is statistically indistinguishable between any of the analyzed mixtures of dual-resolution combinations. Furthermore, the semilocally trained CSG EMOS provides an efficient alternative to the state-of-the-art quantile mapping without requiring additional historical data.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sándor Baran, baran.sandor@inf.unideb.hu
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