The Fog/Haze Medium-range Forecast Experiments Based on Dynamic Statistic Method

Zhang Ziyin; Zhao Xiujuan; Xiong Yajun; Ma Xiaohui

doi:10.11898/1001-7313.20180106

Vol.29, NO.1, 2018

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Article Navigation > Journal of Applied Meteorological Science > 2018 > 29(1): 57-69

Zhang Ziyin, Zhao Xiujuan, Xiong Yajun, et al. The fog/haze medium-range forecast experiments based on dynamic statistic method. J Appl Meteor Sci, 2018, 29(1): 57-69. DOI: 10.11898/1001-7313.20180106.

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Zhang Ziyin, Zhao Xiujuan, Xiong Yajun, et al. The fog/haze medium-range forecast experiments based on dynamic statistic method. J Appl Meteor Sci, 2018, 29(1): 57-69. DOI: 10.11898/1001-7313.20180106.

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The Fog/Haze Medium-range Forecast Experiments Based on Dynamic Statistic Method

DOI: 10.11898/1001-7313.20180106

1.
Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089
2.
Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing 100089

Received Date: 2017-05-15
Rev Recd Date: 2017-10-17
Publish Date: 2018-01-31

Abstract

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 PM_2.5 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 PM_2.5 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, PM_2.5 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 PM_2.5 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.
- fog/haze;
- PM_2.5;
- medium-mange forecast;
- ECMWF;
- dynamic statistic method

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References(27)

Figures and Tables

Fig. 1 Correlation coefficients of the predicted visibility and the observed visibility(a) with those of PM_2.5(b) in different leading times for Beijing and the mean of 14 cities

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Fig. 2 The mean bias of the predicted visibility and the observed visibility(a) with that of PM_2.5(b) in different leading times for Beijing and the mean of 14 cities

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Fig. 3 The root mean square error of the predicted and observed visibility(a) with that of PM_2.5(b) in different leading times for Beijing and the mean of 14 cities

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Fig. 4 The predicted error reduction of visibility(a) and PM_2.5(b) in different leading times for Beijing and the mean of 14 cities

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Fig. 5 TS of the visibility prediction in different leading times for Beijing(a) and the mean of 14 cities(b)

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Fig. 6 TS of PM_2.5 prediction in different leading times for Beijing(a) and the mean of 14 cities(b)

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Fig. 7 The observed and the predicted PM_2.5(a) with visibility(b) during a fog/haze process in Oct 2015

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Fig. 8 The observed and the predicted PM_2.5(a) with visibility(b) during a fog/haze process in Nov 2015

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Fig. 9 The observed and the predicted PM_2.5(a) with visibility(b) during a fog/haze process in Nov 2016

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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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Table 2 Correlation between the predicted visibility and the observed visibility in different leading times

城市	预报时效
城市	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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Table 3 TS of fog and haze predictions in different leading times

预报时效/h	霾		雾
预报时效/h	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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