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中国农学通报 ›› 2014, Vol. 30 ›› Issue (1): 93-97.doi: 10.11924/j.issn.1000-6850.2013-1955

• 林学 园艺 园林 • 上一篇    下一篇

西藏林区森林火险等级中短期预报方法

林志强 罗布次仁 罗文红 德庆卓嘎   

  • 收稿日期:2013-07-17 修回日期:2013-08-11 出版日期:2014-01-05 发布日期:2014-01-05
  • 基金资助:
    公益事业行业专项“中国气象局2013年业务建设项目(GYHY201106005);西藏自治区气象局科研课题“省级公共气象服务业务系统建设(第2期)”(XZQX201106)。

Short and Mid-term Prediction Method of Forest Fire Weather Danger Classification Over Tibetan Forest

  • Received:2013-07-17 Revised:2013-08-11 Online:2014-01-05 Published:2014-01-05

摘要: 利用2009/2010、2010/2011和2011/2012西藏林区防火期(11月—翌年4月)气象观测资料和T639数值预报资料,基于人工神经网络BP算法,建立了西藏林区森林火险等级1~7天预报模型,历史拟合率超过85%;通过对2012/2013防火期间的森林火险等级试报检验结果表明,前3天的平均绝对误差不超过0.5级,7天的平均绝对误差不超过0.6级;与直接利用数值预报模式气象要素预报结果相比,有效地纠正了数值模式要素预报的系统偏差,表明模型预报效果良好。该模型的建立提高了对西藏高原森林火险等级的预报准确性,为森林火险防御和消防调度提供了参考。

关键词: 长沙市, 长沙市

Abstract: Using the 2009/2010, 2010/2011 and 2011/2012 Tibet forest fire prevention period (from November to next April) meteorological observations and T639 numerical forecast data, based on artificial neural network BP algorithm (BPM), the forest fire danger rating forest model in 7 days in Tibet was established. The model history matching rate was more than 85%, through the 2012/2013 fire prevention period fire danger rating test report results showed that: the average absolute error in 3 days was less than 0.5, and the average absolute error in 7 days was less than 0.6; comparing with using numerical weather prediction model, the BPM effectively correct prediction of system deviation factor. It indicated that: the BPM had a good prediction results. The model improved on the Tibetan forest fire danger rating prediction to improve forest fire prevention.