白永清,祁海霞,刘琳,陈城,林春泽,李武阶. 2016. 武汉大气能见度与PM2.5浓度及相对湿度关系的非线性分析及能见度预报[J]. 气象学报, 74(2):189-199, doi:10.11676/qxxb2016.013
武汉大气能见度与PM2.5浓度及相对湿度关系的非线性分析及能见度预报
Study on the nonlinear relationship among the visibility, PM2.5 concentration and relative humidity in Wuhan and the visibility prediction
投稿时间:2015-08-11  修订日期:2015-12-30
DOI:10.11676/qxxb2016.013
中文关键词:  武汉  能见度  PM2.5  相对湿度  非线性
英文关键词:Wuhan  Visibility  PM2.5  Relative humidity  Nonlinear relationship
基金项目: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。
作者单位
白永清 中国气象局武汉暴雨研究所 暴雨监测预警湖北省重点实验室, 武汉, 430205
武汉中心气象台, 武汉, 430074 
祁海霞 武汉中心气象台, 武汉, 430074 
刘琳 中国气象局武汉暴雨研究所 暴雨监测预警湖北省重点实验室, 武汉, 430205 
陈城 湖北省气象信息与技术保障中心, 武汉, 430074 
林春泽 中国气象局武汉暴雨研究所 暴雨监测预警湖北省重点实验室, 武汉, 430205 
李武阶 中国气象局武汉暴雨研究所 暴雨监测预警湖北省重点实验室, 武汉, 430205 
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中文摘要:
      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PM2.5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PM2.5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0.568 km。而干情景下(RH<40%),能见度迅速降低的关键因素是PM2.5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PM2.5对能见度的影响及吸湿性颗粒物的散射效率变化有关。PM2.5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。PM2.5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10 km对应的PM2.5浓度阈值为70 μg/m3,湿情景下该阈值为18—55 μg/m3。当PM2.5质量浓度低于约40 μg/m3时,继续降低PM2.5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1.9 km,能见度≤10 km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。
英文摘要:
      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 RH is 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 (RH<40%), the increase in the PM2.5 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 PM2.5 on the visibility and partly attributed to light scattering effects of hygroscopic particles. Results also indicate that there exists a nonlinear relationship between the PM2.5 concentration and the visibility,which can be described by a power function. The correlation between the PM2.5 concentration and the visibility is most significant when the RH is less than 90% but larger than 80%. The sensitivity threshold of PM2.5 concentration for the atmospheric visibility decreases with increasing RH. Under dry conditions, the visibility of 10 km corresponds to a PM2.5 concentration threshold of 70 μg/m3, whereas the value is 18-55 μg/m3 under wet conditions. Decreases in the PM2.5 concentration can lead to significant improvement in visibility when the PM2.5 concentration is less than 40 μg/m3. 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 1.9 km. 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.
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