| 湖州市大雾天气的成因分析及预报研究 |
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| 引用本文： | 李法然,周之栩,陈卫锋,朱红,陈世春.湖州市大雾天气的成因分析及预报研究[J].应用气象学报,2005,16(6):794-803. |
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| 作者姓名： | 李法然 周之栩 陈卫锋 朱红 陈世春 |
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| 作者单位： | 浙江省湖州市气象台 湖州 313000 |
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| 摘 要： | 提出了一个大雾预报的天气学模型，为了建立客观预报模式，必须把相应环流背景信息进行量化处理。该模式应用多种物理量来描述大雾发生前天气形势的变化，既便于量化又很容易实现预报的客观化和自动化。给出了物理量转换、组合计算方法，定向输送量概念对背景场的动态量化十分有效。应用BP神经网络建立大雾预报模型，选取的预报因子、预报指标可以较完整地描述形成大雾的整个背景场，包含的信息量大，业务应用效果明显。用于建模的神经网络设计和参数化方案对其他预报系统建设有借鉴意义。
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| 关 键 词： | 大雾气候分布 物理量处理 神经网络 预报自动化 |
| 收稿时间： | 2004-10-22 |
| 修稿时间： | 2005-08-16 |
The Physical Mechanisms Analyses and Forecast Research About the Heavy Fog in Huzhou |
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| Li Faran,Zhou Zhixu,Chen Weifeng,Zhu Hong,Chen Shichun.The Physical Mechanisms Analyses and Forecast Research About the Heavy Fog in Huzhou[J].Quarterly Journal of Applied Meteorology,2005,16(6):794-803. |
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| Authors: | Li Faran Zhou Zhixu Chen Weifeng Zhu Hong Chen Shichun |
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| Affiliation: | Huzhou Meteorological Observatory, Zhejiang Province, Huzhou 313000 |
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| Abstract: | A model is developed to prognosticate the heavy fog.For creating a objective forecasting-heavyfog model,the circulation background information must be transformed into numeral format.Ignored the fog classification and corresponding synoptic patterns,it uses various physical variations to describe the circulation background on the formation mechanism of the heavy fog which is valuable in not only easily quantifying,but also realizing the(forecast's) objectivity and automatity.The algorithm of the variatious conversion and their combinations presented.The concept and its formula of the directional transporting are very effective to dynamical quantifying of relevant background field.In the fog-forecasting model,based on BP neural networks technique,the forecast factors and forecast index can better describe the whole background,in which developed the heavy fog,it consists of numerous information and has effective application.The design of the neural networks and the parameterization scheme would be a good reference to the construction of the other forecast systems. |
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| Keywords: | Climate distribution of heavy fog Processing of physical variables Neural networks Forecast automization |
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