Zhao Xiujuan, Xu Jing, Zhang Ziyin, et al. Beijing regional environmental meteorology prediction system and its performance test of PM2.5 concentration. J Appl Meteor Sci, 2016, 27(2): 160-172. DOI:  10.11898/1001-7313.20160204.
Citation: Zhao Xiujuan, Xu Jing, Zhang Ziyin, et al. Beijing regional environmental meteorology prediction system and its performance test of PM2.5 concentration. J Appl Meteor Sci, 2016, 27(2): 160-172. DOI:  10.11898/1001-7313.20160204.

Beijing Regional Environmental Meteorology Prediction System and Its Performance Test of PM2.5 Concentration

DOI: 10.11898/1001-7313.20160204
  • Received Date: 2015-06-17
  • Rev Recd Date: 2016-01-22
  • Publish Date: 2016-03-31
  • Beijing Regional Environmental Meteorology Prediction System (BREMPS) is established by coupling BJ-RUC, WRF-Chem and preferred visibility parameterization scheme. The performance test with observations in 2014 and Asia-Pacific Economic Cooperation (APEC) period shows that BREMPS has a good forecasting ability for two important elements in air quality and haze forecasting in Beijing and surrounding area, PM2.5 concentration and visibility. Correlation coefficients of PM2.5 between forecasted and observed values reaches above 0.6 at most sites, and even reaches above 0.8 at some sites in Beijing. Forecasted values generally underestimate the PM2.5 concentrations with a regional average normalized mean bias of-15%. The forecast performance shows slight decrease after 24 forecasted hours. Comparing with the regional average, the forecast performance is best in Beijing urban area and northern of Hebei. The forecasted PM2.5 concentration agrees well with the observation in Beijing area. Correlation coefficients of PM2.5 concentrations between forecasted and observed values in 48 forecasted hours are about 0.77 in urban area. The normalized mean bias is generally in a range of-26%. The correlation coefficient in rural area is higher than that in urban area. However, the mean bias is also higher in rural area. That is probably attributed to the inaccuracy of the emission information in these areas. The forecast performance is better in spring, autumn and winter, during which the correlation coefficient between forecasted and observed values mostly ranges from 0.7 to 0.9, and the normalized mean bias is within 20%. The forecasted visibility is closer to automatic measurements than artificial observations. Forecasted values are in good agreement with observations during sustained low visibility synoptic processes. For the hourly visibility lower than 10 km, the accuracy of forecast is 77%, which decreases with the reduction of visibility and reaches 40% when the visibility is lower than 2 km. BREMPS shows good forecast performance during the APEC period, when the temporal evolutions of AQI and visibility, and spatial distribution of PM2.5 concentrations are well forecasted, providing strong support for the environmental meteorology forecast service.
  • Fig. 1  Forecast region and the location of monitoring stations

    Fig. 2  Main frame of Beijing regional environmental meteorology prediction system

    Fig. 3  Spatial distribution of correlation coefficient (a) and normalized mean bias (b) between 24 h forecasted and observed daily mean PM2.5 concentration

    Fig. 4  Comparison between observation and 24 h forecast of daily mean PM2.5 concentration in Beijing urban (a) and rural (b) areas

    Fig. 5  Monthly test statistic for observation and forecast of PM2.5 in Beijing urban area

    Fig. 6  Observation and 24 h forecast of daily mean visibility

    (a) Beijing Weather Observatory, (b) Haidian Station

    Fig. 7  Linear fitting between observation and 24 h forecast of daily mean visibility

    (a) Beijing Weather Observatory, (b) Haidian Station

    Fig. 8  Comparison between observed AQI in Beijing area and forecasted AQI at Haidian and Guanyuan stations from 26 Oct to 13 Nov in 2014

    (a) daily AQI, (b) linear regression between the observed and the forecasted AQI at Haidian Station, (c) linear regression between the observed and the forecasted AQI at Guanyuan Station

    Fig. 9  Spatial distributions of observed and forecasted hourly concentrations of PM2.5from 7 Nov to 11 Nov in 2014

    Fig. 10  Comparison of hourly visibility between observation and 24 h forecast at Beijing Weather Observatory from 1 Nov to 13 Nov in 2014

    Table  1  Test statistics for forecasted and observed annual mean daily average PM2.5 concentration

    区域 浓度/(μg·m-3) 相关系数 归一化平均偏差/% 平均误差/(μg·m-3)
    观测 24 h 48 h 72 h 24 h 48 h 72 h 24 h 48 h 72 h 24 h 48 h 72 h
    北京城区 88.0 78.9 69.7 65.2 0.77 0.77 0.69 -10.3 -20.8 -25.9 -9.0 -18.2 -22.7
    北京郊区 76.7 49.2 45.8 43.1 0.83 0.79 0.75 -35.8 -40.1 -43.7 -27.4 -30.6 -33.4
    天津 85.4 59.0 49.8 45.7 0.69 0.64 0.56 -30.9 -41.7 -46.5 -26.4 -35.6 -39.7
    石家庄 132.5 91.6 81.5 77.0 0.68 0.62 0.53 -30.9 -38.5 -41.8 -40.9 -51.0 -55.4
    河北中南部 117.5 82.5 71.3 67.8 0.73 0.66 0.57 -29.8 -39.3 -42.3 -35.0 -46.2 -49.7
    河北北部 55.6 44.5 39.5 37.5 0.77 0.76 0.73 -20.0 -28.9 -32.4 -11.1 -16.1 -18.0
    整个区域 74.3 51.8 45.2 43.0 0.73 0.69 0.65 -30.2 -39.2 -42.1 -22.5 -29.1 -31.3
    DownLoad: Download CSV

    Table  2  Classification statistics of forecasted visibility at Beijing Weather Observatory

    分级/km 小时样本量 准确率/% 日样本量 准确率/%
    观测 预报 观测 预报
    [10,30) 1333 1073 80.5 200 179 89.5
    [0,10) 1424 1098 77.1 165 111 67.3
    [0,5) 903 562 62.2 89 50 56.2
    [0,2) 307 125 40.7 24 2 8.3
    [2,5) 597 229 38.4 66 29 43.9
    [5,10) 522 174 33.3 77 29 37.7
    DownLoad: Download CSV
  • [1]
    Shao M, Tang X, Zhang Y, et al.City clusters in China:Air and surface water pollution.Frontiers in Ecology & the Environment, 2006, 4:353-361. doi:  10.1890/1540-9295(2006)004%5b0353:CCICAA%5d2.0.CO%3b2/full
    [2]
    Zhang Q, Streets D G, Carmichael G R, et al.Asian emissions in 2006 for the NASA INTEX-B mission.Atmos Chem Phys, 2009, 9:5131-5153. doi:  10.5194/acp-9-5131-2009
    [3]
    徐祥德, 丁国安, 卞林根.北京城市大气环境污染机理与调控原理.应用气象学报, 2006, 17(6):815-828. doi:  10.11898/1001-7313.20060618
    [4]
    张远航.大气复合污染是灰霾内因.环境, 2008, 7:32-33. http://www.cnki.com.cn/Article/CJFDTOTAL-HQYT200807017.htm
    [5]
    黄健, 吴兑, 黄敏辉.1954—2004年珠江三角洲大气能见度变化趋势.应用气象学报, 2008, 19(1):61-70. doi:  10.11898/1001-7313.20080111
    [6]
    靳军莉, 颜鹏, 马志强, 等.北京及周边地区2013年1—3月PM2.5变化特征.应用气象学报, 2014, 25(6):690-700. doi:  10.11898/1001-7313.20140605
    [7]
    Wang L T, Wei Z, Yang J, et al.The 2013 severe haze over the southern Hebei, China:Model evaluation, source apportionment, and policy implications.Atmos Chem Phys, 2014, 14:3151-3173. doi:  10.5194/acp-14-3151-2014
    [8]
    Dennis R L, Byun D W, Novak J H, et al.The next generation of integrated air quality modeling:EPA's Models-3.Atmospheric Environment, 1996, 30(12):1925-1938. doi:  10.1016/1352-2310(95)00174-3
    [9]
    ENVIRON.User's Guide to the Comprehensive Air Quality Modeling System with Extensions (CAMx), Version 4.4, 2002:10-44.
    [10]
    Wu Q Z, Shi A, Li Y, et al.Air quality forecast of PM10 in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) system:emission and improvement.Geosci Model Dev, 2014, 7:2243-2259. doi:  10.5194/gmd-7-2243-2014
    [11]
    邓涛, 吴兑, 邓雪娇, 等.珠三角空气质量暨光化学烟雾数值预报系统.环境科学与技术, 2013, 36(4):62-68. http://www.cnki.com.cn/Article/CJFDTOTAL-FJKS201304015.htm
    [12]
    王自发, 谢付莹, 王喜全, 等.嵌套网格空气质量预报模式系统的发展与应用.大气科学, 2006, 30(5):778-790. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200605006.htm
    [13]
    Wang T J, Jiang F, Deng J J, et al.Urban air quality and regional haze weather forecast for Yangtze River Delta region.Atmos Environ, 2012, 58:70-83. doi:  10.1016/j.atmosenv.2012.01.014
    [14]
    Grell G A, Peckham S E, Schmitz R, et al.Fully coupled "online" chemistry within the WRF model:Description and application.Atmos Environ, 2005, 39:6957-6975. doi:  10.1016/j.atmosenv.2005.04.027
    [15]
    陈敏, 范水勇, 郑祚芳, 等.基于BJ-RUC系统的临近探空及其对强对流发生潜势预报的指示性能初探.气象学报, 2011, 69(1):181-194. doi:  10.11676/qxxb2011.016
    [16]
    魏东, 尤凤春, 杨波, 等.北京快速更新循环预报系统 (BJ-RUC) 要素预报质量评估.气象, 2011, 37(12):1489-1497. doi:  10.7519/j.issn.1000-0526.2011.12.003
    [17]
    刘梦娟, 陈敏.BJ-RUC系统对北京夏季边界层的预报性能评估.应用气象学报, 2014, 25(2):212-221. doi:  10.11898/1001-7313.20140211
    [18]
    闵晶晶.BJ-RUC系统模式地面气象要素预报效果评估.应用气象学报, 2014, 25(3):265-273. doi:  10.11898/1001-7313.20140302
    [19]
    Zaveri R A, Peters L K.A new lumped structure photochemical mechanism for large-scale applications.J Geophys Res, 1999, 104:30387-30415. doi:  10.1029/1999JD900876
    [20]
    Wild O, Zhu X, Prather M J.Fast-J:Accurate simulation of inand below-cloud photolysis in tropospheric chemical model.J Atmos Chem, 2000, 37:245-282. doi:  10.1023/A:1006415919030
    [21]
    Zaveri R A, Easter R C, Fast J D, et al.Model for simulating aerosol interactions and chemistry (MOSAIC).J Geophys Res, 2008, 113, D13204.doi: 10.1029/2007JD008782.
    [22]
    Bohren C F, Huffman D R.Absorption and Scattering of Light by Small Particles.John Wiley, 1983:477-482. doi:  10.1002/9783527618156
    [23]
    Petters M D, Kreidenweis S M.A single parameter representation of hygroscopic growth and cloud condensation nucleus activity.Atmos Chem Phys, 2007, 7:1961-1971, doi: 10.5194/acp-7-1961-2007.
    [24]
    Malm W C, Day D E, Carrico C M, et al.Intercomparison and closure calculations using measurements of aerosol species and optical properties during the Yosemite aerosol characterization study.J Geophys Res, 2005:110, D14302, 10.1029/2004JD005494. https://www.researchgate.net/profile/Bret_Schichtel/publication/228614183_Intercomparison_and_closure_calculations_using_measurements_of_aerosol_species_and_optical_properties_during_the_Yosemite_Aerosol_Characterization_Study/links/0c96051841ac8f2ff3000000.pdf?origin=publication_list
    [25]
    Malm W C, Hand J L.An examination of the physical and optical properties of aerosols collected in the IMPROVE program.Atmos Environ, 2007, 41:3407-3427. doi:  10.1016/j.atmosenv.2006.12.012
    [26]
    Chen J, Zhao C S, Ma N, et al.A parameterization of low visibilities for hazy days in the North China Plain.Atmos Chem Phys, 2012, 12:4935-4950. doi:  10.5194/acp-12-4935-2012
    [27]
    张强, Zbigniew Klimont, David Streets, 等.中国人为源颗粒物排放模型及2001年排放清单估算.自然科学进展, 2006, 16(2):223-231. http://www.cnki.com.cn/Article/CJFDTOTAL-ZKJZ200602016.htm
    [28]
    王丽涛, 张强, 郝吉明, 等.中国大陆CO人为源排放清单.环境科学学报, 2005, 25(12):1580-1585. doi:  10.3321/j.issn:0253-2468.2005.12.002
    [29]
    翟世贤, 安兴琴, 刘俊, 等.不同时刻污染减排对北京市PM2.5浓度的影响.中国环境科学, 2014, 34(6):1369-1379. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGHJ201406002.htm
    [30]
    Wu Q, Wang Z, Gbaguidi A, et al.A numerical study ofcontributions to air pollution in Beijing during CAREBeijing-2006.Atmos Chem Phys, 2011, 11(12):5997-6011. doi:  10.5194/acp-11-5997-2011
    [31]
    Zhao P S, Dong F, He D, et al.Characteristics of concentrations and chemical compositions for PM2.5 in the region of Beijing, Tianjin, and Hebei, China.Atmos Chem Phys, 2013, 13:4631-4644. doi:  10.5194/acp-13-4631-2013
    [32]
    司鹏, 高润祥.天津雾和霾自动观测与人工观测的对比评估.应用气象学报, 2015, 26(2):240-246. doi:  10.11898/1001-7313.20150212
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    • Received : 2015-06-17
    • Accepted : 2016-01-22
    • Published : 2016-03-31

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