资源环境科技发展态势分析平台

In climatology, there is a clear need for more reliable data, especially in regions where no meteorological stations exist. Different statistical methods as well as regional climate models are usually used for covering areas with limited data. However, important biases between real and simulated climate parameters are observed, especially with respect to extremes. The present study introduces a new statistical method that combines triangular irregular networks and copulas for the simulation of extreme maximum and minimum temperatures. According to the new method, a studied region can be divided into triangles, and a data series can be simulated for every unknown x-point in each triangle. The simulation of new data series is based on both the distances between the unknown point and the triangle vertices and the observational data available at each vertex. The statistical evaluation of this new method was successful and further demonstrated that the size of the triangle as well as the climatic characteristics of the stations at the triangle vertices can significantly affect the final results. The present investigation proposes the triangular irregular network-copula method for the simulation of extreme temperatures, as the projections provided by this method are shown to approach the observed extremes values for individual meteorological stations.