ESTIMATION OF RAINFALL PROBABILITY, THROUGH THE USE OF NON PARAMETRIC STATISTICAL TECHNIQUES, APPLIED TO NUMERICAL SIMULATIONS OF WRF. A CASE OF STUDY

Authors

  • Lissette Guzmán Rodríguez Departamento de Física, Universidade Federal de Santa Maria, Santa Maria, RS
  • Vagner Anabor Departamento de Física, Universidade Federal de Santa Maria, Santa Maria, RS
  • Franciano Scremin Puhales Departamento de Física, Universidade Federal de Santa Maria, Santa Maria, RS
  • Everson Dal Piva Departamento de Física, Universidade Federal de Santa Maria, Santa Maria, RS

DOI:

https://doi.org/10.5902/2179460X20193

Keywords:

KDE. Probabilistic forecast. Heavy rainfall.

Abstract

In this paper was  used the  kernel density estimation (KDE),  a nonparametric method to estimate the probability density function of a random variable, to obtain a probabilistic  precipitation forecast, from an ensemble prediction with the  WRF model. The nine members of the prediction were obtained by varying the convective parameterization of the model, for a heavy precipitation event in southern Brazil. Evaluating the results, the estimated probabilities  obtained for periods of 3 and 24 hours, and various thresholds of precipitation, were compared with the estimated precipitation of the TRMM, without showing a clear morphological correspondence between them. For  accumulated in 24 hours, it was possible to compare the specific values of the observations of INMET, finding better coherence between the observations and the predicted probabilities. Skill scores were calculated from contingency tables,  for different ranks of probabilities, and the forecast of heavy rain had higher proportion correct in all ranks of probabilities, and forecasted precipitation with probability of 75%, for any threshold, did not produce false alarms. Furthermore, the precipitation of lower intensity with marginal probability was over-forecasted, showing also higher index of false alarms.

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Published

2016-07-20

How to Cite

Rodríguez, L. G., Anabor, V., Puhales, F. S., & Piva, E. D. (2016). ESTIMATION OF RAINFALL PROBABILITY, THROUGH THE USE OF NON PARAMETRIC STATISTICAL TECHNIQUES, APPLIED TO NUMERICAL SIMULATIONS OF WRF. A CASE OF STUDY. Ciência E Natura, 38, 491–497. https://doi.org/10.5902/2179460X20193