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气象:2007,33(12):3-8
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基于Logistic回归的区域地质灾害综合气象预警模型
(1.国家气象中心, 北京 100081;2.中国气象科学研究院)
Regional Integrated Meteorological Forecasting and Warning Model for Geological Hazards Based on Logistic Regression
(1.National Meteorological Centre, Beijing 100081;2.Chinese Academy of Meteorological Sciences)
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投稿时间:2007-08-31    修订日期:2007-10-17
中文摘要: 应用Logistic回归拟合地质灾害发生概率,建立了区域地质灾害综合气象预警模型,实 现了降雨引发地质灾害发生概率的动态预报。降雨观测和数值降雨预报作为模型的动态输入 ,利用信息量模型集成地学因子得到的总信息量作为模型的静态预报因子。预警模型在栅格 点上预报降雨引发地质灾害的发生概率,并以等级形式发布预警。用2004年实际观测对预报 进行检验,结果表明当年80%的灾害都达到了预警发布的标准。2006年汛期的业务运行对台 风碧利斯引发的地质灾害做出了较准确的预报预警。说明模型能满足预警业务的要求, 是提高地质灾害气象预警水平的有效途径。
Abstract:Regional Integrated Meteorological Forecasting and Warning Model for G eological Hazards is constructed based on Logistic regression of the probabiliti es of geological hazard occurrences. Dynamic forecasting of the probability of r ainfall induced geological hazard occurrence is thus realized. Rainfall observa t ions and numerical rainfall forecasts are used as dynamical inputs of the model. Total information produced by information model that integrates factors of geos ciences is used as the static predictor of the model. The model forecasts probab ilities of geological hazard occurrences on a grid, and releases warning message s in the form of a 5 level hierarchy. Validation of the model with observationa l data for the year 2004 shows that 80% of the geolog ical hazards of the year hav e been identified as Warning Level 3 or above. The operation of the model during the rainy season of the year 2006 has made forecasts and warnings with high acc uracy for geological hazards triggered by Typhoon Bilis. All these show that the model can satisfy the requirements of warning operations, and is an effective w ay to improve the meteorological warnings for geological hazards.
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基金项目:中国气象局气象新技术推广基金(CMATG2004M05)资助
引用文本:
徐晶,张国平,张芳华,齐丹,2007.基于Logistic回归的区域地质灾害综合气象预警模型[J].气象,33(12):3-8.
Xu Jing,Zhang Guoping,Zhang Fanghua,Qi Dan,2007.Regional Integrated Meteorological Forecasting and Warning Model for Geological Hazards Based on Logistic Regression[J].Meteor Mon,33(12):3-8.