Improved Spatio-temporal Kringing and its Application to Regional Precipitation Prediction

Abstract

Precipitation is one of the most important elements in meteorological data. However, due to the limitation of resource conditions, the number of meteorological stations is limited, and interpolation is required to obtain the precipitation data in the observation area and other locations. Kriging interpolation whose core is to obtain the best variogram model is widely used in the prediction of regional precipitation. However, it is difficult to find perfect estimating model, and numerous approachs are utilized to handle this problem. In order to gain better parameters and model, an improved spatiotemporal Kriging interpolation method is proposed in this paper. The chaotic ant-lion algorithm (CALO) is employed to seek suitable parameters of the variogram both in the space domain and the time domain. This evolutionary algorithm whose performance has been validated in the literatures is not vulnerable to search the global solution. The experiment is conducted in terms of the fitting effect and interpolation effect, error analysis to demonstrate the superior performance of the proposed method, compared to other fitting methods such as Least square method. Several optimization algorithms are used to constitute the contrast experiment. The experimental results show that the proposed method prevails among other approachs as far as the precision, calculation cost and effectiveness.