关键词: 白背飞虱, 迁入量, 大气环流特征量, BP神经网络, 短期预报模型

Abstract: The reanalyzed meteorological grid data from NCEP （National Center for Environment Predicting in USA） in 1979-2011 and the pentad lighting catches of Sogatella furcifera（ white backed plant hopper，WBPH） in the rice growing regions of the middle and lower reaches of the Yangtze river were collected to discuss the influence of the atmospheric circulation characteristic variables on the immigrations of WBPH. After the analysis of the correlations between the immigration amount of WBPH and the main atmospheric circulation characteristic variables， a method of Back Propagation Neural Network was used to establish the shortterm forecast models of WBPHs occurrence grades during the beginning periods of immigration， the peak periods of migration northward， the peak periods of migration southward and the ending periods of immigration for 13 plant protection stations in the middle and lower reaches of the Yangtze river. The results showed as follows: （1） there were most significant correlations between the immigration amount of WBPH and the area index of the western Pacific Subtropical High （IA）， the western ridge point index of the western Pacific Subtropical High （IW）， the westerly strength index （IEARW） and the East Asia major trough index （H500） on the isobaric surface of 500hPa in the preceding pentad respectively. Among these correlations， the correlation between the immigration amount of WBPH and IEARW was negative and the correlations between the immigration amount of WBPH and IA， IW and H500 were positive. The correlation between the immigration amount of WBPH and IW was 0.397 as the least value but the correlation coefficients between it and IA， IEARW and H500 were larger than 0.78. （2） There were significant positive correlations between the immigration amount of WBPH and the geopotential height （hgt）， vertical velocity （omega）， zonal wind speed （uwnd） and meridional wind speed （vwnd） on the isobaric surface of 850hPa in the preceding pentad respectively. Among these correlations， the correlation coefficient between WBPHs immigration amount and hgt was 0.354 as the least value but the correlation coefficients between it and omega， uwnd and vwnd were larger than 0.8. （3） The atmospheric circulation characteristic variables of significant correlation between them and the immigration amount of WBPH were selected as the predictors. After the WBPHs immigration amounts were divided into 5 occurrence grades， 4 forecasting models of WBPHs occurrence grades during the beginning periods of immigration， the peak periods of migration northward， the peak periods of migration southward and the ending periods of immigration based on the method of Back Propagation Neural Network were established and the extension examination accuracy rates of these models stabled above 80%. Therefore， these models were proved to be available for the shortterm forecast of WBPH pendant occurrence grades in the middle and lower reaches of the Yangtze river. The results were positive significant to reveal the influence rules of meteorological factors on WBPHs immigration， to predict the WBPHs occurrence degrees， and to prevent or control the WBPH harmness timely and effectively.

Key words: Sogatella furcifera（Horvth）, Immigrating amount, Characteristic variable of atmospheric circulation, Back Propagation neural network, Shortterm forecast model