| 摘要: |
| 利用1980-2017年逐日雾观测资料、NCEP/NCAR 1°×1°再分析资料，采用统计学、天气学、累积频率等方法分析了衢州市雾日的时空分布特征及雾预报方法。结果表明：（1）衢州市雾的空间分布呈北多南少、山区明显多于平原的特征；雾出现时间主要集中在冬春季，83.2 %的雾出现在23:00～次日9:00，峰值在6时；（2）容易导致衢州出现区域性雾的地面天气类型有四类：冷锋前暖区型、大陆高压型、入海高压后部型和低槽型；500 hPa高空环流形势有三种：低槽型、高压脊型和纬向气流型；（3）雾发生前一天对流层低层小风、高湿并伴有逆温，其中低槽型雾逆温层接地，其他三种类型逆温层抬离地面；（4）用累积频率法定量给出了雾出现的相对湿度、风速、逆温的阈值与消空指标。（5）经过两年使用，该预报模型预报准确率达71.2%，具有较强的预报价值。 |
| 关键词: 分布特征；天气学分型；累积频率；预报指标 |
| DOI： |
| 投稿时间：2019-02-02修订日期：2019-07-18 |
| 基金项目:浙江省气象局预报员专项项目2018YBY05和衢州市气象局一般项目2018QZ08共同资助 |
|
| Research on mechanism analysis and forecasting method |
| MAO Cheng-yan,PAN Xin,GONG Li-qing,LI Hao-wen |
| (Quzhou Meteorological Bureau;Guangzhou Meteorological Observatory) |
| Abstract: |
| Based on the daily meteorological fog data and NCEP/NCAR FNL 1°x1° reanalysis data from 1980 to 2017, the spatial-temporal distribution characteristics of fog in Quzhou city of Zhejiang Province are analyzed by statistical method, synoptic?method and cumulative?frequency?method. (1). The results show that occurrence frequency of fog are higher in mountainous area than that in Plain area, and northern areas experience more fog days than the southern areas; fog mainly occurs in?winter and spring，and 83.2 % of fog occurs from 23:00 to 9:00 the next day, reaching the peak at 6:00. (2). It turns out that four types of surface synoptic situation can lead to regional fog in Quzhou , which are the warming zone in front of the cold front, continental high, high pressure aft-part over sea, low trough, and three types of synoptic situation at 500 hPa, can also lead to regional fog, which are zonal flow, low trough, high pressure ridge. (3). Usually the lower troposphere exhibit the characteristic of high humidity and slight?breeze the day before fog occurs, and there is an inversion layer close to the ground when low trough dominates, while the inversion layers are away from the surface layer during the other three types of situation. (4). For the comprehensive analysis of meteorological elements in foggy days, the thresholds and negative indexes of relative humidity, wind speed, temperature inversion are determined by the cumulative frequency method. (5). After two years of application, the forecast model has an accuracy of 71.2%, with good prospects in operational weather forecast. |
| Key words: Distribution features; Synoptic situation; Cumulative frequency; Prediction standards |
