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\begin{document}

\title{A New Copula-Bayesian Post-Processing Method for NMME Precipitation
Forecasts: Extreme and Non-Extreme Values}



\author[1]{Farhad Yazdandoost}%
\author[1]{Mina Zakipour}%
\author[1]{Ardalan Izadi}%
\affil[1]{K N Toosi University of Technology}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{abstract}
In this study, an effective post-processing approach has been examined
to improve skill of NMME precipitation forecasts. This method is based
on the existence of a correlation between the historical raw forecast
and observational data. In this respect, the Copula-Bayesian approach
was used along with the Normal Kernel Density marginal distribution,
kernel Copula function, and a novel approach to select final improved
forecast data amongst the existing candidates on the calculated
Conditional Probability Distribution Functions (CPDF). In this approach,
called the Double Copula method, four input variables are effective for
determining the improved NMME data. These are 1) the likelihood of an
improved forecast (as a probable observation) for a given raw forecast
(CPDFf) 2) the likelihood of raw forecast for the corresponding improved
forecast (CPDFo) 3) the probability of occurrence of raw and 4) the
probability of occurrence of improved forecast data (as PDF). The
evaluation of the proposed method for improving the precipitation
forecast by the NMME model has been performed in Karoon basin, Iran.
Here, the data of 1982-2010 for the calibration period (hindcast) and
2011-2018 (forecast) to validate the results have been used. The results
show that the improved forecast data is more reliable due to several
achievements namely; 1) higher spatial and temporal accuracy and
consistency are observed, 2) extreme values of precipitation are better
detected, and finally, 3) during different length of time, the involved
uncertainties have been reduced significantly in comparison with raw
data.%
\end{abstract}%



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