Spatially adaptive post-processing of ensemble forecasts for temperature
We propose a statistical post-processing method that yields locally calibrated probabilistic forecasts of temperature, based on the output of an ensemble prediction system. It represents the mean of the predictive distributions as a sum of short-term averages of local temperatures and ensemble predi...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2014
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| In: |
Journal of the Royal Statistical Society. Series C, Applied statistics
Year: 2013, Volume: 63, Issue: 3, Pages: 405-422 |
| ISSN: | 1467-9876 |
| DOI: | 10.1111/rssc.12040 |
| Online Access: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1111/rssc.12040 Verlag, lizenzpflichtig, Volltext: https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12040 
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| Author Notes: | Michael Scheuerer and Luca Büermann |
| Summary: | We propose a statistical post-processing method that yields locally calibrated probabilistic forecasts of temperature, based on the output of an ensemble prediction system. It represents the mean of the predictive distributions as a sum of short-term averages of local temperatures and ensemble prediction system driven terms. For the spatial interpolation of temperature averages and local forecast uncertainty parameters we use an intrinsic Gaussian random-field model with a location-dependent nugget effect that accounts for small-scale variability. Applied to the COSMO-DE ensemble, our method yields locally calibrated and sharp probabilistic forecasts and compares favourably with other approaches. |
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| Item Description: | First published: 30 September 2013 Gesehen am 18.09.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1467-9876 |
| DOI: | 10.1111/rssc.12040 |