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A Study of the Correlativity Between Weather Stations and the Improvement of the Objective Analysis Scheme of MM4 Model
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摘要: 采用多元统计学中的主成分分析法，通过对MM4模式计算范围内169个测站的风、压、湿和温度的相关分析，确定了89个关键站，另外80个非关键站可由这89个关键站通过多元回归的方法求得。用这种方法，整个计算区域内的统计信息损失量大约超过2%。文章选取1992年7月24日到25日的一次暴雨过程，用关键站的相关最大的站，对缺测的站使用多元回归的方法补值，然后再用MM4模式进行数值预报试验，发现客观分析场和预报场的准确度都有提高。Abstract: Using the principal component analysis in multivariate statistical analysis, the correlations between 169 weather stations are discussed in the computation domain of the MM4 forecast model. The result shows that there are 89 key stations which play an important role in the forecasting, and the information of the other stations could be obtained from them by multivariate linear regression. It also shows that the loss of the statistical information in the whole domain is less than 2%. A storm rainfall event on 24—25, July 1992 is selected to test the regression method which is used to supplement the missing data. It is found that the objective analysis field and forecast field are improved
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