Application of Weather Generation to High Frequency and High Resolution Gridded Datasets in Sao Paulo

Abstract

The adjusted stochastic weather generator to disaggregate the monthly climate averages into daily timescale is investigated due to the limitations of Climate Models in representing the daily weather fluctuations. We use weather generator for daily precipitation and maximum and minimum temperature are investigated for Sao Paulo State. We use the adjusted stochastic weather generator to disaggregate the monthly climate averages into daily timescale due to the limitations of Climate Models in representing the daily weather fluctuations. The weather generator WGEN was adjusted considering the statistical distribution of temperature and precipitation from gauges to the present climate. This derived dataset was first applied to investigate the ability of a Regional Climate Model version 4 (RegCM4) in reproduce the spatial variability of those distributions. Then, we developed to generate realizations of daily temperature and precipitation for gridding datasets over Sao Paulo while preserving the spatial and temporal distribution as well as produce gridded dataset of the proprieties that define the precipitation and temperature intensity and frequency. For temperature, we consider topographies to take the interpolation using distance square inverse. Comparison and evaluating generated time series with observations and regional model shows that performance of the weather generator tested in term of the frequency and extreme events reproduced the observed statistical distribution.