【摘要】 利用重庆地区能见度及温、压、湿、风等气象资料和大气颗粒物浓度数据，对重庆能见度特征及其影响因子进行分析，采用神经网络方法建立能见度预报模型，分析比较了引入PM_2.5浓度因子对预报模型的影响效果。发现：重庆地区能见度分布呈现西低东高以及长江沿线较低的分布特征；雾发生时的平均能见度低于降水时能见度也远低于剔除雨、雾后的能见度，表明低能见度受大气中水汽影响更大；雾在冬季比例明显增加，使得平均能见度在冬季明显降低，而6月和10月降水增多是导致这两个月平均能见度出现明显降低的重要原因；能见度日变化呈现单峰型，雾和降水高发时段与平均能见度低值区重叠，是造成夜间能见度低的一个重要原因；大气湿度、温度及颗粒物浓度都是影响能见度的重要因子，当相对湿度小于70%时能见度随PM_2.5增加明显降低，当相对湿度大于70%时PM_2.5对能见度的影响降低；在能见度的客观预报模型中引入PM_2.5浓度因子的预报效果好于不引入该因子的效果，特别是秋冬季的预报效果改善明显。更多还原

【Abstract】 Based on the visibility, temperature, pressure, humidity, wind and atmospheric particulate concentration data in Chongqing, the characteristics and influencing factors of visibility in Chongqing are analyzed. The visibility forecast model is established using the neural network method, and the effects of introducing the PM_2.5 concentration factor on the forecast model are analyzed and compared. The results show that the visibility distribution in Chongqing is low in the west, high in the east, and low along the Yangtze River. The average visibility during fog is lower than that during precipitation and much lower after removing the data of rain and fog days, indicating that low visibility is more affected by water vapour in the atmosphere. The proportion of fog increases significantly in winter, leading to a significant decrease in average visibility in winter. The precipitation increase in June and October is an important reason for the significant decrease in average visibility in these two months. The diurnal variation of visibility shows a single-peak pattern, and the periods of high fog and precipitation overlap with the areas of low average visibility, which is an important reason for the low visibility at night. Atmospheric humidity, temperature and particle concentration are all critical factors affecting visibility. When relative humidity is less than 70%, visibility decreases significantly with the increase of PM_2.5, and when relative humidity is more than 70%, the influence of PM_2.5 on visibility decreases. The prediction effect of introducing the PM_2.5 concentration factor into the visibility objective forecast model is better than that of not introducing the PM_2.5 concentration factor, especially the prediction effect in autumn and winter is significantly improved.更多还原