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Evaluation of probabilistic precipitation forecast determined from WRF forecasted amounts

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Abstract

This paper examines the connection between the probability of precipitation and forecast amounts from Weather Research and Forecasting (WRF) model runs over Central and West Africa. A one season period (June–September 2010) was used to investigate the quantitative precipitation forecast–probability relationship. The predictive capability of this relationship was then tested on an independent sample of data (June–September 2011); 2010 and 2011 were wet and dry years, respectively. The results show that rainfall is less likely to occur in those areas where the model indicates no precipitation than it is elsewhere in the domain. Rainfall is more likely to occur in those regions where precipitation is predicted, especially where the predicted precipitation amounts are largest. The probabilities of rainfall forecasts based on this relationship are found to possess skill as measured by relative operating characteristic curves, reliability diagrams, and Brier skill scores. Skillful forecasts from the technique exist throughout 24-h periods for which WRF output was available. The results suggest that this forecasting tool might assist forecasters throughout the season in a wide variety of weather events and not only in areas of difficult-to-forecast convective systems.

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Acknowledgments

WRF simulations were done on a workstation provided by Dr Serge Janicot of LOCEAN (Paris), in the framework of the PICREVAT project, funded by the French government. WRF was provided by the University Corporation for Atmospheric Research website (for more information see http://www.mmm.ucar.edu/wrf/users/download/get_source.html). GPCP data were obtained from the NOAA website http://www.esrl.noaa.gov. TRMM data wereprovided online by NASA at http://mirador.gsfc.nasa.gov.

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Authors and Affiliations

  1. Laboratory for Environmental Modelling and Atmospheric Physics, Department of Physics, University of Yaounde 1, Yaounde, Cameroon

    Roméo S. Tanessong, P. Moudi Igri, Derbetini A. Vondou & F. Mkankam Kamga

  2. Department of Physics, Faculty of Sciences, University of Douala, Douala, Cameroon

    P. H. Kamsu Tamo

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  1. Roméo S. Tanessong
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  2. P. Moudi Igri
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  3. Derbetini A. Vondou
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  4. P. H. Kamsu Tamo
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  5. F. Mkankam Kamga
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Correspondence to Roméo S. Tanessong.

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Tanessong, R.S., Igri, P.M., Vondou, D.A. et al. Evaluation of probabilistic precipitation forecast determined from WRF forecasted amounts. Theor Appl Climatol 116, 649–659 (2014). https://doi.org/10.1007/s00704-013-0965-2

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  • DOI: https://doi.org/10.1007/s00704-013-0965-2

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