Comparison of Success Rate of Numerical Weather Prediction Models with Forecasting System of Convective Precipitation

  • David ŠaurEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 464)


The aim of this article is to compare a success rate of a chosen numerical weather prediction (NWP) models with a forecasting system of convective precipitation based on an analysis of ten historical weather events over the territory of the Zlin Region for the year 2015. This paper is based on a previous article “Evaluation of the accuracy of numerical weather prediction models”. The first chapter is a theoretical framework describing the current forecasting systems of convective precipitation, which are selected NWP models and forecasting system of convective precipitation. This chapter describes the principle of creating predictions and selection of individual NWP models. Furthermore, they are provided with basic information about the prediction of convective precipitation. The second chapter outlines the principles of the methods used for evaluating the success rate of forecast precipitation. In the discussion, results of these methods on selected historical weather situations are published. Finally, the work contains an overview of the most accurate NWP models in comparison with the forecasting system of convective precipitation. This refined predictive information of convective precipitation may be especially useful for the crisis management authorities for preventive measures against the occurrence of flash floods.


Numerical weather prediction models Flash floods Crisis management Convective precipitation 



This article was supported by the Department of Security Engineering under internal grant IGA/FAI/2016/023 “Optimization the System of Convective Precipitation Forecast for an Increase of its Success Rate”.


  1. 1.
    Saur, D.: Evaluation of the accuracy of numerical weather prediction models. In: Advances in Intelligent Systems and Computing. 4th Computer Science On-line Conference 2015 (CSOC 2015), pp. 181–193. Springer (2015). ISBN: 978-3-319-18476-0.
  2. 2.
    Saur, D., Lukas L.: Computational models of weather forecasts as a support tool for crisis management. In: Proceedings of the contributory 7th International Scientific Conference Safe Slovakia and the European Union. Kosice, Slovakia: College Security Management in Košice, p. 10. ISBN: 978-80-89282-88-3.
  3. 3.
    Johnson, A., Wang X., Regonda S., Wu L., Lee H., Seo D.: Verification and calibration of neighborhood and object-based probabilistic precipitation forecasts from a multimodel convection-allowing ensemble: 2. streamflow verification. Month. Weather Rev. 140(9), 3054−3077 (2012). doi: 10.1175/MWR-D-11-00356.1. ISSN: 0027-0644.
  4. 4.
    Clark, A. J., Gallus W.A, Weisman M. L.: Neighborhood-based verification of precipitation forecasts from convection-allowing NCAR WRF model simulations and the operational NAM: 2. Streamflow verification. Weather and Forecasting. 25(5), 1495–1509 (2010). doi: 10.1175/2010WAF2222404.1. ISSN: 0882-8156.
  5. 5.
    Davis, Ch., Brown, B., Bullock, R.: Object-based verification of precipitation forecasts. part ii: application to convective rain systems. Mon. Weather Rev. 134(7), 1785−1795 (2006). doi: 10.1175/MWR3146.1. ISSN: 0027-0644.
  6. 6.
    Rezacova D., Sokol Z., Pesice P.: A radar-based verification of precipitation forecast for local convective storms: application to Convective Rain Systems. Atmos. Res. 83(2–4), 211−224 (2007). doi: 10.1016/j.atmosres.2005.08.011. ISSN: 01698095
  7. 7.
    Rezacova D., Zacharov P., Sokol Z.: Uncertainty in the area-related QPF for heavy convective precipitation: application to Convective Rain Systems. Atmos. Res. 93(1–3), 238−246 (2009). doi: 10.1016/j.atmosres.2008.12.005. ISSN: 01698095.
  8. 8.
  9. 9.
    Salek, M.: Weather forecasts, current trends in meteorology.
  10. 10.
  11. 11.
    Zacharov, P., Rezacova D.: Comparison of efficiency of diagnostic and prognostic characteristic the convection environment. Meteorol. Bull. (2005). ISSN: 0026–1173.$FILE/MZ%202005_3.pdf#page=3
  12. 12.
    Zacharov P.: Diagnostic and prognostic precursors of convection. Diploma thesis, Prague: Faculty of Mathematics and Physics UK, KMOP. 61 p (2004).
  13. 13.
  14. 14.
    Critical Success Index (CSI) or Threat Score (TS), and Equitable Threat Score (ETS).
  15. 15.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

Personalised recommendations