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中国农学通报 ›› 2016, Vol. 32 ›› Issue (35): 171-177.doi: 10.11924/j.issn.1000-6850.casb16060027

所属专题: 农业气象

• 资源 环境 生态 土壤 气象 • 上一篇    下一篇

马鞍山大雾气候特征分析与预报方法

魏葳,付敏,陈晓伟,吴韶华   

  1. 安徽省马鞍山市气象局,安徽省马鞍山市气象局,安徽省马鞍山市气象局,安徽省马鞍山市气象局
  • 收稿日期:2016-06-06 修回日期:2016-11-27 接受日期:2016-09-06 出版日期:2016-12-26 发布日期:2016-12-26
  • 通讯作者: 魏葳
  • 基金资助:
    安徽省气象局预报员专项“基于细网格数值预报产品的大雾预报方法”(KY201307)。

Climatic Characteristic Analysis and Forecasting Method of Fog in Ma ’anshan

吴韶华   

  • Received:2016-06-06 Revised:2016-11-27 Accepted:2016-09-06 Online:2016-12-26 Published:2016-12-26

摘要: 为了提高马鞍山地区大雾预报准确率,分析1961—2010 年近50 年来马鞍山地区大雾的气候特征,并建立基于配料法的大雾预报方法。结果表明:马鞍山市雾日集中在11 月—次年1 月,为“冬半年雾日多于夏半年”型。潮湿的空气(相对湿度为90%~100%)、微弱的风速(风速≤3 m/s)和适宜的气温(≤20℃)均有利于大雾的形成。925 hPa 和1000 hPa 是否有逆温层,对于雾的形成与维持极其重要。马鞍山大雾地面形势具体分为四类:弱高压型、入海高压后部型、冷锋前暖区型、地面倒槽型。选取水汽条件、冷却条件、层结条件以及风力条件作为大雾预报的基本“配料”,建立马鞍山大雾预报方法。利用EC细网格资料(2011 年9 月—2013 年12 月)对该方法验证的漏报和空报分析:TS 评分42.86%,漏报率6.9%,空报率55.74%。

关键词: 金花菌, 金花菌, 不同原料, 接种发酵, 品质

Abstract: In order to improve the fog forecast accuracy in Ma’anshan, the authors analyzed the climatic characteristics of heavy fog in Ma’anshan from 1961 to 2010 and established a fog prediction method based on the method of ingredients. The results showed that: the fog days of Ma’anshan focused on November to January of the following year, the fog days in winter were more than that in summer; the humid air (relative humidity was 90% to 100%), the weak wind (wind speed was equal or less than 3 m/s) and suitable temperature (equal or less than 20℃) were all beneficial to the formation of fog; whether there was an inversion layer in 925 hPa and 1000 hPa was beneficial to the thermosphere and was extremely important for the formation and maintenance of fog; the ground situation of heavy fog in Ma’anshan was divided into four categories: weak high pressure type, the back type of the high-pressure sea-going, cold front warm area type, ground reverse grooved type. The authors chose the water vapor condition, cooling condition, stratification condition and wind conditions as the basic ingredients of fog forecast and established the prediction method of Ma’anshan. Using the data of EC fine grid (September 2011-December 2013) to analyze the failure report and empty report for this validation method: TS score was 42.86%, non report rate was 6.9% and the empty report rate was 55.74%.