Article InfoStochastic weather generators used in meteorological and hydrological studies are mostly statistical models that produce random numbers resembling the observed data on which they have been fitted. They can generate weather sequences that statistically resemble the real observed data. In this study we developed a single-site daily rainfall generator for the simulations of rainfall occurrences and amounts in northwestern of Benin. A first-order two-state Markov chain was used to determine the occurrence of daily precipitation. The rainfall amounts on wet days were generated by using the oneparameter Exponential distribution and the two-parameter Gamma distribution. The Markov model was successful in simulating the rainfall occurrences and the Exponential distribution as the more reliability in preserving most of the important daily characteristics of the historical rainfall amounts. The test of the two distributions on data that are not taken into account in the elaboration of the model has shown that the exponential distribution reproduces well the statistics of the daily precipitation than the gamma distribution

This paper analyses for the first time in tropical area, the relationship between lightning and DSD (Drop Size Distribution) parameters on rainy events that occurred during the monsoon period. The Lightning data used are collected by the LINET (Lightning Detection Network) while the DSD data were recorded by a distrometer. The correlation was computed within five circles of radius varying between  to  with a step of . These consecutive areas are centered on the position of the disdrometer. By taking into account only the convective spectra and remove out of the data the cases where there is rain without any lightning and vice versa, all data was computed with a time scale of one minute during each of the rainy events .The results showed that the exponential and polynomial laws fit better our data than the power and linear laws. The highest correlation coefficients are obtained within a radius of about 20 km around the distrometer location. The correlation between the parameter  and  is the most stable with a correlation coefficient equal to .

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