As an unsupervised classification method,Self-Organizing Map(SOM) has been increasingly used in climatology and meteorology.But one thing bothering for the method is that it can not give the time varying amplitude of SOM patterns.To solve this problem,an easy but effective pattern amplitude algorithm is proposed based on the fact that pattern amplitude at certain time is correlated with the degree of similarity between the SOM pattern and the corresponding input element field of this time.The amplitude of typical patterns reflects the strength of these patterns,and it is related to two factors.One is the degree of the similarity between the SOM pattern and the input element field,the greater the degree of similarity,the stronger the typical pattern.The other one is the population value of the input element field,bigger element value indicate stronger typical pattern.Taking into account these two factors,the definition of the pattern amplitude at certain time is the correlation coefficient between the input field vector and SOM weight vector multiplied by the ratio of the total absolute value of the two vectors.The above method is applied to the study of the sea surface height anomaly(SSHA) fields of the South China Sea from 1993 to 2008.Four spatial distribution patterns of SSHA interannual variation are extracted by SOM from the monthly anomaly SSHA fields.Pattern 1 is dominated by a cyclonic circulation over the whole basin,and pattern 4 almost mirrors pattern 1;pattern 2 is dominated by a cyclonic circulation in the west of the basin with a higher SSHA along the eastern boundary of the basin,and pattern 3 almost mirrors pattern 2.The time series of the amplitude of the four patterns are calculated with the proposed algorithm.In order to verify the reliability of the results,the pattern type time series is reconfirmed with the pattern of the largest amplitude and compared with those resulted from SOM.Totally 62.5% pattern numbers during the whole time series are consistent with the original pattern numbers resulted from SOM,the other 37.5% cases are inconsistent mainly because they are the transitions of different patterns and therefore have no typical pattern features.Delayed correlation analysis of the SOM pattern amplitude time series with Nio 3 index shows that when Nio 3 index leads pattern 2 and pattern 3 by two months,the correlation coefficients between them are the strongest,which can reach-0.633 and 0.632 respectively with a 95% confidence level,indicating that the two mirror patterns are closely related with the La Nia and El Nio phenomenon,while pattern 1 and pattern 4 have no obvious delayed correlation with Nio 3 index.According to the satellite altimeter data from 1993 to 2003,the pattern of the South China Sea SSHA fields is lagged by Nio 3.4 index for 2 months.The above results show that the pattern amplitude calculated by the algorithm is reasonable.
The optimal extraction conditions of jujube fruit wine using jujube as raw material were investigated.The optimal conditions were obtained by response surface analysis as followed: the water content 5.5 folds of materials,the enzyme 0.105ml/kg,the extraction pH value 3.3,baking temperature 120℃,baking time 40min,hydrolysis temperature 40℃ and hydrolysis time 4h.Under these conditions,the reducing sugar was 87.22g/L.
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