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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, 85–88, 2017
https://doi.org/10.5194/asr-14-85-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 14, 85–88, 2017
https://doi.org/10.5194/asr-14-85-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  18 Apr 2017

18 Apr 2017

CNR-ISAC 2 m temperature monthly forecasts: a first probabilistic evaluation

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

Abstract. The 2 m temperature probabilistic forecasts collected, on a weekly basis, in about one year of CNR-ISAC monthly forecasting activity are evaluated in this work. RPSS and reliability diagrams are computed on a tercile classification of forecast and observed temperatures. The RPSS, averaged over all the available cases, shows that the system has a residual predictive skill beyond week 2 on some peculiar regions. Reliability diagrams show that, in general, the probability forecasts of above-normal observed temperature are more reliable than below-normal temperature. Although the results are based on a limited period, they can represent a reference for similar works based on other subseasonal forecasting systems.

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Short summary
The probabilistic evaluation of 2 m temperature forecast of about one year of monthly ensemble forecasts, issued on a monthly basis, is provided. The ranked probability skill score, averaged over all the available cases, shows that the system has a residual predictive skill beyond week 2 on some peculiar regions. Reliability diagrams show that, in general, the probability forecasts of above-normal observed temperature are more reliable than below-normal temperature.
The probabilistic evaluation of 2 m temperature forecast of about one year of monthly ensemble...
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