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Probabilistic temperature forecasting with a heteroscedastic autoregressive ensemble postprocessing model

Möller, AnnetteORCIDiD
Groß, Jürgen
DOI: https://doi.org/10.1002/qj.3667
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8854
Möller, Annette; Groß, Jürgen, 2019: Probabilistic temperature forecasting with a heteroscedastic autoregressive ensemble postprocessing model. In: Quarterly Journal of the Royal Meteorological Society, Band 146, 726: 211 - 224, DOI: 10.1002/qj.3667.
 
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  • Abstract
Weather prediction today is performed with numerical weather prediction (NWP) models. These are deterministic simulation models describing the dynamics of the atmosphere, and evolving the current conditions forward in time to obtain a prediction for future atmospheric states. To account for uncertainty in NWP models it has become common practice to employ ensembles of NWP forecasts. However, NWP ensembles often exhibit forecast biases and dispersion errors, thus require statistical postprocessing to improve reliability of the ensemble forecasts. This work proposes an extension of a recently developed postprocessing model utilizing autoregressive information present in the forecast error of the raw ensemble members. The original approach is modified to let the variance parameter depend on the ensemble spread, yielding a two-fold heteroscedastic model. Furthermore, an additional high-resolution forecast is included into the postprocessing model, yielding improved predictive performance. Finally, it is outlined how the autoregressive model can be utilized to postprocess ensemble forecasts with higher forecast horizons, without the necessity of making fundamental changes to the original model. We accompany the new methodology by an implementation within the R package ensAR to make our method available for other researchers working in this area. To illustrate the performance of the heteroscedastic extension of the autoregressive model, and its use for higher forecast horizons we present a case-study for a dataset containing 12 years of temperature forecasts and observations over Germany. The case-study indicates that the autoregressive model yields particularly strong improvements for forecast horizons beyond 24 h.
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  • Geophysik, Extraterrestische Forschung [936]
Subjects:
autoregressive process
ensemble postprocessing
heteroscedastic model
high-resolution forecast
predictive probability distribution
spread-adjusted linear pool
spread-error correlation
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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