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Long-term prediction models for China national wheat powdery mildew based on large scale factors.

YU Cai-xia1, HUO Zhi-guo1,2**, HUANG Da-peng3, JIANG Yan4, WU Li1   

  1. (1Chinese Academy of Meteorological Sciences, Beijing 100081, China; 2Collaborative Innovation Center of Meteorological Disaster Forecast, EarlyWarning and Assessment, Nanjing University of Information Science & Technology, Nanjing 210044, China; 3National Climate Center, Beijing 100081, China; 4 Department of Emergency Response, Disaster Mitigation and Public Services, China Meteorological Administration, Beijing 100081, China)
  • Online:2015-03-10 Published:2015-03-10

Abstract: Based on China national wheat powdery mildew occurrence area data from 1971 to 2008, the monthly data of atmospheric circulation characteristics from 1970 to 2008 and the north Pacific sea surface temperature (SST) monthly data from 1970 to 2008, using the method of factor puffing and spatial topological analysis, in combination with atmospheric circulation factors and SST factors, the factors that have stable and significant effects on wheat powdery mildew were selected by means of optimization process, correlation analysis, stability test, and factor independence test, the atmospheric circulation and the north Pacific SST regression forecasting models of the wheat powdery mildew occurrence area rate were constructed, respectively. With data from 2009 to 2010, the models were verified. The accuracy rate of forecast about the atmospheric circulation model with the  historical data reached 81.6% with the predicting accuracy of 100%, and its accuracy rate of prediction was 82.5% as a whole. The accuracy rate of forecast about the north Pacific SST model with the  historical data reached 78.95% with the prediction accuracy of 100%, and its accuracy rate of predicting was 80% as a whole. The evaluation precision of the atmospheric circulation model is slightly higher than that of the north Pacific SST model.

Key words: large aperture scintillometer, water and heat fluxes, farmland, footprint climatology