Improved multisite stochastic weather generation with applications to historical data in South Korea

In this paper, we propose an improved multisite weather generation with applications to the historical data in South Korea. The proposed method improve the algorithm of Wilks (1998, 1999) by automatically selecting an optimal model that represents precipitation amounts and by providing a procedure to obtain a symmetric positive definite estimate for the covariance matrix. The proposed method is computationally fast, and hence, it can be feasible to handle a massive data. We apply the proposed method to the precipitation and temperature data collected 170 stations in South Korea for the period 1976–2005 which are given by the Korea Meteorological Administration (KMA). Results of the proposed method demonstrate the promising performance in terms of spatial correlation and long-term variation as compared with those of the multisite method of Wilks (1998) and the single-site weather generator.