基于粒子群改进的自适应核密度估计算法的江苏省地面气温分析

考虑到当前气温分析方法对于季度、月度及日夜时间尺度下的数据分析不够充分的问题，提出一种基于粒子群算法改进的自适应核密度估计算法(PA-KDE)，利用1961~2008年江苏省12个站点的定时气温资料研究该方法在季度、月度及日夜时间尺度下的灵敏度。按区域与季节对试验进行分析的结果表明：PA-KDE算法在季度、月度、日夜时间尺度下具有更高的灵敏度；同时可以用来更全面的分析气温的时空变化特征以及不同影响因子的效果。可见在讨论气温时空变化以及影响特征时要关注更多时间尺度、区域、季节下的影响。

Analysis of Surface Air Temperature Observations in Jiangsu Province Based on Adaptive Kernel Density Estimation Algorithm Improved by Particle Swarm Optimization

The problem had been considered that the current temperature analysis methods were insufficiently for data analysis at quarterly, monthly, daily and nightly time scales, thus an adaptive kernel density estimation algorithm improved by particle swarm optimization was proposed. The timed temperature of 12 stations in Jiangsu Province from 1961 to 2008 were selected as observations to verify the sensitivity of the method at the quarterly, monthly, daily and nightly time scales. The result analyzed by region and season shows that: the improved algorithm has higher sensitivity at these time scales, and the characteristics of spatiotemporal variation and the effects of different influencing factors can be more comprehensively analyzed simultaneously. Thus the results suggest that attention should be paid to the effects at different time scales, regions and seasons when the characteristics of spatiotemporal variation and different influencing factors are discussed.