PRECIPITATION ANOMALY DETECTION METHOD RESEARCH BASED ON DELAUNAY TRIANGULATION AND GEOGRAPHICALLY WEIGHTED REGRESSION
Local precipitation laws were difficult to simulate due to the poor adaptability of current precipitation data quality control methods.Based on the observed summer precipitation data from 35 meteorological stations covering 1985-2014 in Hunan province,a method for detecting spatial outliers of precipitation was developed by combination of Delaunay triangulation and geography weighted regression.In this method,the stable and reasonable geographical proximity relationships between each region was obtained by constructing irregular triangulation,and the adaptive regression bandwidth was obtained according to spatial proximity relationships.Spatial outliers of precipitation were then detected and analyzed according to the statistical distribution characteristics of regression residual,which was calculated by geographically weighted regression method.The results showed that the method could be used to detect spatial outliers of precipitation accurately and effectively.It indicated that the method were meaningful for the secondary treatment of precipitation information and the further exploration of local precipitation laws.
Delaunay triangulationgeographically weighted regressionadaptive bandwidthspatial outliers detection of precipitation
NETL
NSTL
万方数据
维普
