基于大气环流特征量的河北省冬小麦白粉病预报模型

利用1990-2010年逐年的河北省冬小麦白粉病和国家气候中心74项大气环流特征量指数（即大气环流指数）逐月值资料,通过Pearson相关分析和逐步回归分析方法,筛选与白粉病发生显著相关的大气环流因子,建立河北省冬小麦白粉病受害面积的年前、春季预报模型.首先根据白粉病发生面积与大气环流指数关系,建立白粉病发生面积预报模型（模型Ⅰ和模型Ⅱ）,并基于贝叶斯分类规则,利用白粉病面积预报结果建立白粉病发生程度的预报模型；另外,基于贝叶斯分类规则直接利用关键大气环流指数建立白粉病发生程度预报模型（模型Ⅲ和模型Ⅳ）；最后通过预报结果与实际发生情况的对比对模型效果进行检验.结果表明,白粉病发生面积和发生程度两种模型均可在发病前1a的12月底和当年4月底进行预报；对1990-2010年的历史回代拟合和2011-2013年的外延预报结果显示,模型Ⅰ和模型Ⅱ预报的小麦白粉病发生面积（1990-2013年）与实际发生面积基本一致,年前和春季病害发生程度预报模型（模型Ⅲ和模型Ⅳ）的历史回代拟合准确率分别为81.0％和90.5％,2011-2013年外延预报误差最大的仅1个等级.说明大气环流特征量对小麦白粉病有较强的气候指示效应,可为小麦白粉病的长期预测提供参考依据.

Prediction of Winter Wheat Powdery Mildew in Hebei Province Based on Atmospheric Circulation Characteristics

Based on annual winter wheat powdery mildew （WPM） data from 1990 to 2010, and 74 monthly atmospheric circulation characteristics data from National Climate Center of China, the key circulation characteristic indices （Atmospheric Circulation Indices, ACI ）, which significantly correlated with WPM area in Hebei province, were determined through Pearson correlation analysis and step-wise regression analysis. Based on these key factors, the WPM area prediction model for Hebei province was established. At first, Model I and Model II were established based on the relationship between WPM area and ACI, then Model III and Model IV were established by Bayesian classification. All these models were validated by comparing predicted WPM value and actual WPM records. The results showed that both prediction models of WPM area and WPM level were available in the end of previous December and in the end of April. The predicted WPM area was consistent with the real records ,both for the historical data during 1990 -2010 and the real-time records during 2011 -2013. The accuracy of the WPM level prediction model was 81.0% and 90. 5% respectively at the end of previous December and the end of April. The maximum error of the WPM levelprediction model in real time prediction for 2011 - circulation characteristics were significant indicators powdery mildew in long term 2013 was one level. to WPM and could The results indicated that provide basic to predict the atmospheric to winter wheat