Discriminant Genetic Algorithm Extended (DGAE) model for seasonal sand and dust storm prediction

作     者：YANG YuanQin WANG JiZhi HOU Qing LI Yi ZHOU ChunHong 

作者机构：Key Laboratory Jor Atmospheric Chemistry Center for Atmospheric Watch and Services Chinese Academy of Meteorological Sciences China Meteorological Administration Beijing 100081 China 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2011年第54卷第1期

页      面：10-18页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 070601[理学-气象学] 0706[理学-大气科学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by National S & T Support Program (Grant No. 2008BAC40B02) National Basic Research Program of China (Grant Nos. 2006CB403703 and 2006CB403701) Basic Research Fund under Chinese Academy of Meteorological Sciences (Grant Nos. 2009Y002, 2009Y001) 

主　　题：sand and dust storms seasonal prediction methodology Discriminant Genetic Algorithm Extended (DGAE) model 

摘      要：Here we use a Discriminant Genetic Algorithm Extended (DGAE) model to diagnose and predict seasonal sand and dust storm (SDS) activities occurring in Northeast Asia. The study employed the regular meteorological data, including surface data, upper air data, and NCEP reanalysis data, collected from 1980–2006. The regional, seasonal, and annual differences of 3-D atmospheric circulation structures and SDS activities in the context of spatial and temporal distributions were given. Genetic algorithms were introduced with the further extension of promoting SDS seasonal predication from multi-level resolution. Genetic probability was used as a substitute for posterior probability of multi-level discriminants, to show the dual characteristics of crossover inheritance and mutation and to build a non-linear adaptability function in line with extended genetic algorithms. This has unveiled the spatial distribution of the maximum adaptability, allowing the forecast field to be defined by the population with the largest probability, and made discriminant genetic extension possible. In addition, the effort has led to the establishment of a regional model for predicting seasonal SDS activities in East Asia. The model was tested to predict the spring SDS activities occurring in North China from 2007 to 2009. The experimental forecast resulted in highly discriminant intensity ratings and regional distributions of SDS activities, which are a meaningful reference for seasonal SDS predictions in the future.