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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 14
Adv. Sci. Res., 14, 123–129, 2017
https://doi.org/10.5194/asr-14-123-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 14, 123–129, 2017
https://doi.org/10.5194/asr-14-123-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  08 May 2017

08 May 2017

Multimodel probabilistic prediction of 2 m-temperature anomalies on the monthly timescale

Alfonso Ferrone, Daniele Mastrangelo, and Piero Malguzzi Alfonso Ferrone et al.
  • Institute of Atmospheric Sciences and Climate (CNR-ISAC), 40129 Bologna, Italy

Abstract. The 2 m-temperature anomalies from the reforecasts of the CNR-ISAC and ECMWF monthly prediction systems have been combined in a multimodel super-ensemble. Tercile probability predictions obtained from the multimodel have been constructed using direct model outputs (DMO) and model output statistics (MOS), like logistic and nonhomogeneous Gaussian regression, for the 1990–2010 winter seasons. Verification with ERA-Interim reanalyses indicates that logistic regression gives the best results in terms of ranked probability skill scores (RPSS) and reliability diagrams for low–medium forecast probabilities. Also, it is argued that the logistic regression would not yield further improvements if a larger dataset was used.

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The ensemble reforecasts of the CNR-ISAC and ECMWF forecasting systems, both participating to the S2S project, have been combined in a multimodel ensemble. Tercile probability predictions of wintertime 2 m temperature produced through logistic regression outperform the probability estimation based on the direct count of ensemble members, in terms of RPSS and reliability diagrams. Also, it is argued that the logistic regression would not yield further improvements if a larger dataset were used.
The ensemble reforecasts of the CNR-ISAC and ECMWF forecasting systems, both participating to...
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