摘要
武汉作为中部地区高湿度代表城市,大气污染严重,霾天气多发,但有关该地区大气能见度与PM_(2.5)浓度及相对湿度(RH)的定量关系尚不明确。利用2014年9月—2015年3月武汉地区逐时能见度、相对湿度及颗粒物质量浓度观测数据,研究分析了武汉大气能见度与PM_(2.5)浓度及相对湿度的关系,并进行能见度非线性预报初探,得到以下结论:武汉霾时数发生比例高,霾的发生和加重是能见度降低的主要原因;能见度降低伴随大量细粒子产生和累积,这是武汉大气能见度恶化的重要诱因。细颗粒物浓度与相对湿度共同影响和制约大气能见度变化,高湿高浓度时能见度显著下降,湿情景下(RH≥40%),能见度恶化主要是由湿度增高诱使细颗粒物粒径吸湿增长导致其散射效率增大造成的。当RH>90%时,能见度随湿度升高成线性递减,相对湿度每升高1%,武汉平均能见度降低0.568km。而干情景下(RH<40%),能见度迅速降低的关键因素是PM_(2.5)质量浓度升高。在城市大气细粒子污染背景下,能见度与相对湿度成非线性关系,这主要与PM_(2.5)对能见度的影响及吸湿性颗粒物的散射效率变化有关。PM_(2.5)浓度与能见度成幂函数非线性关系,80%≤RH<90%湿度区段下相关性最强。PM_(2.5)浓度对能见度的影响敏感阈值是随着湿度升高而减小的,干情景下能见度10km对应的PM_(2.5)浓度阈值为70μg/m^3,湿情景下该阈值为18—55μg/m^3。当PM_(2.5)质量浓度低于约40μg/m^3时,继续降低PM_(2.5)可显著提高武汉大气能见度。预报试验表明,基于神经网络方法建立大气能见度非线性预报模型是可行的,预报能见度相关系数为0.86,均方根误差为1.9km,能见度≤10km的TS评分为0.92。网络模型具有较高预报性能,对霾的判别有较高准确性,为衔接区域环境气象数值预报模式,建立大气能见度精细化动力统计模型提供参考依据。
Hourly observations of visibility,relative humidity(RH),and particulate mass concentration in Wuhan for the period from September 2014 to March 2015 have been analyzed in this study to investigate the relationship among these variables.Nonlinear prediction of visibility in Wuhan is explored preliminarily.It is found that the frequent occurrence of haze in Wuhan is largely responsible for the severe reduction in visibility.The formation and accumulation of fine particulates are two important factors inducing haze and low visibility.Both the RH and the particulate mass concentration affect the variation of atmospheric visibility.High RH and large fine particulate mass concentration can significantly reduce the atmospheric visibility.Under wet conditions(RH ≥40%),the visibility deteriorates because the hygroscopic growth of the fine particulate can efficiently enhance light absorption and scattering.When the RHis higher than 90%,the visibility decreases linearly with the increase in RH.Averagely,the visibility decreases by 0.568 km as the RH increases by 1%.Under dry conditions(RH40%),the increase in the PM_(2.5)concentration becomes a critical factor for the rapid decrease in visibility.In urban areas where fine particulates in the atmosphere are primary pollutants,the visibility has a nonlinear relationship with RH.This is partly attributed to the influence of PM_(2.5)on the visibility and partly attributed to light scattering effects of hygroscopic particles.Results also indicate that there exists a nonlinear relationship between the PM_(2.5)concentration and the visibility,which can be described by a power function.The correlation between the PM_(2.5)concentration and the visibility is most significant when the RH is less than90% but larger than 80%.The sensitivity threshold of PM_(2.5)concentration for the atmospheric visibility decreases with increasing RH.Under dry conditions,the visibility of 10 km corresponds to a PM_(2.5) concentration threshold of 70μg/m^3,whereas the value is 18-55μg/m^3 under wet conditions.Decreases in the PM_(2.5)concentration can lead to significant improvement in visibility when the PM_(2.5) concentration is less than 40μg/m^3.In addition,results of preliminary experimentshave shown that the visibility prediction model,which is developed based on the neural network method,performs well in prediction of visibility in Wuhan.The correlation coefficient between observations and predictions can be up to 0.86,and the Root Mean Square Error(RMSE)is 1.9km.The TS score is 0.92 for the visibility that is less than 10 km.These results indicate that the model has a crucial skill for haze prediction.
出处
《气象学报》
CAS
CSCD
北大核心
2016年第2期189-199,共11页
Acta Meteorologica Sinica
基金
湖北省气象局科技发展基金项目(2015Y04)
2014年湖北省财政业务建设项目
2015年湖北省山洪地质灾害防治气象保障工程