Stochastic multi-site generation of daily weather data |
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Authors: | Malika Khalili François Brissette Robert Leconte |
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Institution: | (1) Department of Civil Engineering and Applied Mechanics, McGill University, 817, Sherbrooke Street West, Montreal, QC, H3A 2K6, Canada;(2) école de technologie supérieure, Quebec University, 1100, Notre-Dame Street West, Montreal, QC, H3C 1K3, Canada |
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Abstract: | Spatial autocorrelation is a correlation between the values of a single variable, considering their geographical locations.
This concept has successfully been used for multi-site generation of daily precipitation data (Khalili et al. in J Hydrometeorol
8(3):396–412, 2007). This paper presents an extension of this approach. It aims firstly to obtain an accurate reproduction
of the spatial intermittence property in synthetic precipitation amounts, and then to extend the multi-site approach to the
generation of daily maximum temperature, minimum temperature and solar radiation data. Monthly spatial exponential functions
have been developed for each weather station according to the spatial dependence of the occurrence processes over the watershed,
in order to fulfill the spatial intermittence condition in the synthetic time series of precipitation amounts. As was the
case for the precipitation processes, the multi-site generation of daily maximum temperature, minimum temperature and solar
radiation data is realized using spatially autocorrelated random numbers. These random numbers are incorporated into the weakly
stationary generating process, as with the Richardson weather generator, and with no modifications made. Suitable spatial
autocorrelations of random numbers allow the reproduction of the observed daily spatial autocorrelations and monthly interstation
correlations. The Peribonca River Basin watershed is used to test the performance of the proposed approaches. Results indicate
that the spatial exponential functions succeeded in reproducing an accurate spatial intermittence in the synthetic precipitation
amounts. The multi-site generation approach was successfully applied for the weather data, which were adequately generated,
while maintaining efficient daily spatial autocorrelations and monthly interstation correlations. |
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Keywords: | Weather generator Multi-site Precipitation Temperature Solar radiation |
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