摘要
应用集对分析(SPA)这一关于不确定性问题的数学理论和方法,基于概率统计分析,从SPA的同、异、反分析出发,用联系度公式导出解决预报中合理地使用预报因子的方法,实现预报模型因子结构的动态优化,增强模型预报机制的合理性,达到提高模型预报能力的目的。在以往对沙尘暴研究成果的基础上,以强风、热力和沙源三大影响因子为着眼点,结合2001—2003年的沙尘暴天气个例,对沙尘暴天气进行了分类,分别选取预报因子,建立基于SPA的沙尘暴预报模型。根据数值预报产品输出结果,于2004年春季进行了短期(24h)预报试用,结果表明,这一方法具有较好的效果。
The paper, on the basis of formation and dynamics diagnosis, has developed a synoptic concept model about sandstorms＇ happening. According to this model, combined with the theory of the uncertaintyset pair analysis, the paper has designed the sandstorm short-range forecasting method. The forecast factors in the synoptic concept model have been selected strictly by means of the characteristics of forecasted objects, the physical significance of the forecasting factors, the experiences in forecasting sandstorm weather, and some technical ways. Generally speaking, all the selected forecast factors have better forecast abilities, but the better abilities of these factors aren＇t always unchanged. Sometimes, the better ability of one factor may play down. Thus, the error is generated in forecast results. Set Pair Analysis （SPA） is a systemic theory and method used in diagnosing non-authenticity. Using the theory and method, this paper makes a judgment of status and analysis of same-difference-reverse about the factors that will be used to forecast the sandstorm weather. In analyzing, the method weakens effect of those factors with badness abilities in the sandstorm forecast model, while those with better abilities in the model will contribute greatly to forecast it. As a result, optimizing the structure of factors can be realized in the forecast model. The rationality of forecast mechanism in the model can be strengthened. The applications for 24 h sandstorm forecasting in spring of 2004 interpreting and using from the numerical forecast products indicate that the method has better effect.
出处
《中国沙漠》
CSCD
北大核心
2006年第2期268-272,共5页
Journal of Desert Research
基金
陕西省气象局科研基金项目(2004m-8)资助
关键词
集对分析
不确定性
联系度
沙尘暴预报
Set Pair Analysis （SPA）
uncertainty
relation
sand storm forecasting
