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The Fog/Haze Medium-range Forecast Experiments Based on Dynamic Statistic Method
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摘要: 利用欧洲中期天气预报中心（ECMWF）数值预报产品和动态统计预报方法，对北京、天津、石家庄等14个京津冀重点城市雾霾与空气污染进行定量化的中期预报试验，包括对首要污染物PM2.5浓度和能见度的逐时定量化预报及雾霾现象的客观化判断，并对2015年10月1日-2016年11月10日试验预报效果进行了检验评估。检验结果显示：该方法对北京及周边城市未来10 d逐时和逐日能见度、PM2.5浓度及雾霾现象的预报值与观测值之间具有显著正相关系数、较高的误差减少量和TS评分等，表明基于ECMWF数值预报产品和动态统计预报方法的京津冀雾霾污染中期定量化预报技术整体上具有较高的可靠性、稳定性与预报技巧性。此外，检验指标还显示出该动态统计预报方法对能见度的预报效果要略优于PM2.5浓度预报，同时对霾的预报准确率高于对雾的预报。个例分析显示，该动态统计预报方法能提前5~6 d预报出北京地区典型持续性雾霾污染的发展过程，对持续性雾霾的提前预报预警具有较好的参考意义。Abstract: Beijing and eastern China have frequently suffered from severe fog/haze days in recent years, which are characterized by high particle mass concentration and low visibility. Severe haze/fog pollution, especially the persisted fog/haze days (i.e., in January 2013 and November, December of 2015) greatly threaten human health and traffic safety. These phenomena stimulate great interest in studying the fog/haze pollutions in Beijing or even eastern China. The fog/haze pollution is in general attributed to two aspects:Pollutants emission to the lower atmosphere from fossil fuel combustion, construction and others, and unfavorable meteorological diffusion conditions. Air quality or the occurrence of fog/haze pollution are strongly influenced by meteorology. Meteorological factors not only have essential impacts on the accumulation or diffusion, spread and regional transport of air pollutants, but also have important impacts on the formation of secondary aerosol which are generated by the complicated physical and chemical reactions. Particularly, weather conditions play an essential role in the daily variability of air pollutant concentrations.Based on the dynamic statistic forecasting method and the high-resolution weather forecast fields derived from European Centre for Medium-Range Weather Forecasts (ECMWF), the fog/haze medium-range forecast system is designed to provide objective and quantitative PM2.5 and visibility forecasts for cities in Beijing-Tianjin-Hebei and its adjacent regions by predicting 1 to 10 days in advance. A forecasting experiment is performed during the period from 1 October 2015 to 10 November 2016. Results show that the predicted PM2.5 concentrations and visibilities based on the method for 14 cities (Beijing, Tianjin, Shijiazhuang and others) and different leading times (namely 1 to 10 days in advance) are well consistent with the observed. All correlation coefficients between them are significant at 0.01 level. And most of the reduction errors (RE) between them are larger than 0.2. Most of TS values are confined in 0.1 to 0.3, and the mean of all TS values are close to 0.2 for visibility, PM2.5 grades and fog/haze phenomena. Moreover, several case analyses suggest that the method can predict the change trends of the continuous fog/haze process about 5-6 days in advance. Generally, the method can approximately predict the hourly variability of the PM2.5 concentration and visibility and the change trends of the process of fog/haze and heavy pollution in Beijing-Tianjin-Hebei and its adjacent regions on the medium-range time scale. The high reliability and stability of the forecasting test suggest that the objective and quantitative predictions produced by the method can be used with high reference value for the medium-range forecast of fog/haze and air quality in Beijing and surrounding cities.
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Key words:
- fog/haze;
- PM2.5;
- medium-mange forecast;
- ECMWF;
- dynamic statistic method
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图 1 逐日能见度(a)、PM2.5浓度(b)预报与观测的相关系数
Fig. 1 Correlation coefficients of the predicted visibility and the observed visibility(a) with those of PM2.5(b) in different leading times for Beijing and the mean of 14 cities
图 2 逐日能见度(a)、PM2.5浓度(b)预报与观测的平均偏差
Fig. 2 The mean bias of the predicted visibility and the observed visibility(a) with that of PM2.5(b) in different leading times for Beijing and the mean of 14 cities
图 3 逐日能见度(a)、PM2.5浓度(b)预报与观测的均方根误差
Fig. 3 The root mean square error of the predicted and observed visibility(a) with that of PM2.5(b) in different leading times for Beijing and the mean of 14 cities
图 4 逐日能见度(a)、PM2.5浓度(b)预报与观测的误差减少量
Fig. 4 The predicted error reduction of visibility(a) and PM2.5(b) in different leading times for Beijing and the mean of 14 cities
图 5 北京(a)及14个城市平均(b)的能见度预报TS评分
Fig. 5 TS of the visibility prediction in different leading times for Beijing(a) and the mean of 14 cities(b)
图 6 北京(a)及14个城市平均(b)的PM2.5浓度预报TS评分
Fig. 6 TS of PM2.5 prediction in different leading times for Beijing(a) and the mean of 14 cities(b)
图 7 2015年10月中旬雾霾过程北京城区平均PM2.5浓度(a)和能见度(b)的观测值和预报值对比
Fig. 7 The observed and the predicted PM2.5(a) with visibility(b) during a fog/haze process in Oct 2015
图 8 2015年11月上旬雾霾过程北京城区平均PM2.5浓度(a)和能见度(b)的观测值和预报值对比
Fig. 8 The observed and the predicted PM2.5(a) with visibility(b) during a fog/haze process in Nov 2015
图 9 2016年11月上旬雾霾过程北京城区平均PM2.5浓度(a)和能见度(b)的观测值和预报值对比
Fig. 9 The observed and the predicted PM2.5(a) with visibility(b) during a fog/haze process in Nov 2016
表 1 14个城市信息
Table 1 Information of 14 cities
序号 城市 纬度范围 经度范围 1 北京 39.71°~40.11°N 116.20°~116.60°E 2 天津 38.93°~39.33°N 117.00°~117.40°E 3 石家庄 37.85°~38.25°N 114.31°~114.71°E 4 保定 38.66°~39.06°N 115.28°~115.68°E 5 济南 36.46°~36.86°N 116.81°~117.21°E 6 太原 37.63°~38.03°N 112.35°~112.75°E 7 大同 39.87°~40.27°N 113.11°~113.51°E 8 呼和浩特 40.61°~41.01°N 111.50°~111.90°E 9 张家口 40.59°~40.99°N 114.69°~115.09°E 10 唐山 39.44°~39.84°N 117.96°~118.36°E 11 沧州 38.10°~38.50°N 116.66°~117.06°E 12 衡水 37.54°~37.94°N 115.48°~115.88°E 13 邢台 36.86°~37.26°N 114.30°~114.70°E 14 邯郸 36.40°~36.80°N 114.29°~114.69°E 
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表 2 不同预报时效的逐日能见度预报值与观测值的相关系数
Table 2 Correlation between the predicted visibility and the observed visibility in different leading times
城市 预报时效 24 h 72 h 120 h 168 h 216 h 北京 0.75 0.73 0.71 0.57 0.47 天津 0.71 0.70 0.68 0.55 0.39 石家庄 0.67 0.61 0.58 0.53 0.41 保定 0.71 0.71 0.64 0.59 0.38 济南 0.68 0.57 0.59 0.47 0.49 太原 0.58 0.59 0.54 0.47 0.38 大同 0.72 0.68 0.64 0.57 0.46 呼和浩特 0.66 0.66 0.59 0.55 0.39 张家口 0.63 0.63 0.52 0.47 0.40 唐山 0.71 0.70 0.69 0.56 0.41 沧州 0.66 0.61 0.45 0.31 0.27 衡水 0.69 0.61 0.53 0.43 0.35 邢台 0.65 0.62 0.56 0.56 0.47 邯郸 0.64 0.53 0.44 0.32 0.32 注：相关系数均达到0.01显著性水平。
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表 3 不同预报时效的逐时雾和霾现象的TS评分
Table 3 TS of fog and haze predictions in different leading times
预报时效/h 霾 雾 北京 14个城市平均 北京 14个城市平均 24 0.30 0.20 0.14 0.17 48 0.26 0.18 0.10 0.14 72 0.28 0.20 0.12 0.13 96 0.30 0.21 0.16 0.13 120 0.30 0.21 0.20 0.14 144 0.21 0.19 0.16 0.15 168 0.24 0.20 0.15 0.13 192 0.22 0.19 0.17 0.11 216 0.18 0.17 0.07 0.07 240 0.19 0.17 0.04 0.07
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