湖南省低能见度天气时空分布特征及预报订正方法研究

利用2011~2020年国家基本站观测资料，研究了湖南省低能见度天气时空分布特征，并结合低能见度与地面气象要素的关系获取预报订正阈值，在此基础上，结合概率匹配法对EC能见度预报进行订正研究。结果表明:（1）低能见度区域主要位于湖南中北部和通道县、衡阳市附近，并且从秋季开始整个低能见度区域有明显向南扩大的趋势；低能见度时数出现最多的是冬季，其次是春季和秋季，分布范围最广的也是冬季，集中于湖南中东部。（2）低能见度日变化呈单峰型，主要集中在20时~次日09时。（3）≤1 km低能见度主要出现在地面风速<2 m/s、地面相对湿度基本高于90%、地面温度低于20℃及24 h变压<2 hPa的气象环境。（4）采用概率密度匹配结合要素的预报订正方法优于仅使用概率匹配的订正方法，可以很好地对湖南大部分低能见度天气预报进行有效订正，订正后TS评分显著提高。 

Study on Temporal and Spatial Distribution Characteristics and Forecast Correction Method of Low Visibility Weather in Hunan Province

Based on the observation data of National Basic Weather Stations in Hunan from 2011 to 2020, the temporal and spatial distribution characteristics of low visibility weather in Hunan are studied. The ECMWF model visibility forecast is corrected by using probability density function matching method based on the correction threshold that is obtained by combining the relationship between low visibility and surface meteorological elements. The analysis shows that: (1) The area with low-visibility was mainly located in the central and northern part of Hunan, Tongdao County and Hengyang City. The whole low-visibility area had a obvious tendency to move southwardly. Low visibility occurred most frequently in winter, followed by spring and autumn, and was most widely distributed in winter, concentrated in central and eastern Hunan. (2) The diurnal variation of low visibility was single peak, mainly concentrated from 20:00 to 09:00. (3) The low visibility less than or equal to 1km mainly appeared in the meteorological environment where wind speed was less than 2 m/s, the relative humidity was basically above 90%, the surface temperature was lower than 20°C and the 24-hour pressure change was lower than 2hPa. (4) The visibilty forecast in most low-visibility areas could be corrected by using correction methods that combined probability density function with meteorological factors, which was better than the probability matching correction scheme. After correction, TS scores were significantly improved.