Articles | Volume 14
https://doi.org/10.5194/asr-14-123-2017
https://doi.org/10.5194/asr-14-123-2017
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

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Latest update: 28 Mar 2024
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Short summary
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.