斜剖面图的设计及在PM2.5中期潜势预报中的应用

毛卓成; 瞿元昊; 许建明; 余钟奇; 施春红; 杨丹丹

PDF(3910 KB)

中国环境科学 ›› 2020, Vol. 40 ›› Issue (8) : 3312-3321.

大气污染与控制

斜剖面图的设计及在PM_2.5中期潜势预报中的应用

毛卓成^1,2, 瞿元昊^1,2, 许建明^1,2, 余钟奇^1,2, 施春红³, 杨丹丹^1,2

作者信息 +

Design of oblique section and its application to PM_2.5 mid-term potential forecast

MAO Zhuo-cheng^1,2, QU Yuan-hao^1,2, XU Jian-ming^1,2, YU Zhong-qi^1,2, SHI Chun-hong³, YANG Dan-dan^1,2

Author information +

文章历史 +

摘要

本文将强降水潜势预报的“配料法”原理引入到环境预报中，选取ECMWF数值预报产品中的700hPa相对湿度、1000hPa风向风速和850hPa温度作为气象要素“配料”，沿占比最多的冷空气输送路径穿过华北重污染区和上海，设计制作了未来10d气象要素“配料”时空斜剖面图，并具体定义了业务中常见的典型污染过程（冷空气输送、干静稳和湿静稳累积）和清洁过程（冷空气清洁、湿沉降清洁）共5种图形的识别特征.该斜剖面图由于融合了单站时序图和单时次空间面图的优点，无需一张张翻阅天气形势图，显著缩短了看图分析时间；斜剖面图还建立了气象要素图形特征、污染气象条件和PM_2.5污染潜势三者的对应关系，通过图形特征可以快速掌握区域内天气形势演变情况，识别出上述典型污染和清洁过程，最终得出PM_2.5中期污染潜势，便于在业务中应用和推广.一次典型污染个例应用显示，斜剖面图和污染实况的图形型态特征非常相似，斜剖面图中静稳累积和冷空气输送过程图形特征辨识度高，区域和单站提前5d的预报结论和实况较为一致；进一步评估显示，2018年冬季期间预报时效5d以内的TS评分维持在0.32~0.50区间，未来10d的预报准确率维持在74.4%~86.7%区间.

Abstract

“Ingredients-based forecast methodology” in intensive precipitation potential forecast was adapted to environmental meteorological forecast. The ingredient-based oblique section for the next 10days was produced with RH at 700 hPa, wind direction and wind speed at 1000 hPa and temperature at 850 hPa from ECMWF numerical forecast products as ingredients. The oblique section was designed to be along the predominant cold air transport pathways which passed through the heavily polluted area in North China and Shanghai. 5recognition features were defined specifically for cold air transport type, dry stagnant type, wet stagnant type (3polluted types), cold air clean type and wet deposition clean type (2clean types). This oblique section combined the advantages of a single station’s time series diagrams and spatial graphs at a single time, thus significantly reduced the time of chart analysis. The oblique section also produced the relationship among meteorological pattern characteristics, pollution meteorological conditions and PM_2.5 pollution potentials. The evolution of synoptic patterns was easily recognized as typical polluted type or clean type, which led to the PM_2.5 mid-term potential forecast. Thus, this ingredient-based oblique section could be applied and promoted to operational environmental forecast. The model was applied to a typical pollution case. The characteristics of oblique section and actual pollution observation of this case were very similar. The stagnant pattern and cold air transport pattern were both identified. The 5-day forecast for the entire region and single stations were consistent with the observations. Further assessment showed that the TS score within 5 days of the forecast duration in the winter of 2018 was maintained in the range of 0.32~0.50, and the forecast accuracy for the next 10days was maintained in the range of 74.4%~86.7%.

导出引用

毛卓成, 瞿元昊, 许建明, 余钟奇, 施春红, 杨丹丹. 斜剖面图的设计及在PM_2.5中期潜势预报中的应用[J]. 中国环境科学. 2020, 40(8): 3312-3321

MAO Zhuo-cheng, QU Yuan-hao, XU Jian-ming, YU Zhong-qi, SHI Chun-hong, YANG Dan-dan. Design of oblique section and its application to PM_2.5 mid-term potential forecast[J]. China Environmental Science. 2020, 40(8): 3312-3321

中图分类号： X513

参考文献

[1] 佟彦超.中国重点城市空气污染预报及其进展[J].中国环境监测, 2006,22(2):69-71. Tong Y C. Air quality forecast and development in major cities of China[J]. environmental monitoring in china, 2006,22(2):69-71.
[2] 白晓平,李红,张启明,等.人工神经网络在空气污染预报中的研究进展[J].科技导报, 2006,24(12):77-81. Bai X P, Li H, Zhang Q M, et al. Progress of research on artificial neural network in air pollution prediction[J]. Science & Technology Review, 2006,24(12):77-81.
[3] 丁冰,陈健,王彬,等.城市环境PM_2.5空间分布监测方法研究进展[J].地球与环境,2016,44(1):130-138. Ding B, Chen J, Wang B, et al. A review on monitoring methods for spatial distribution of urban PM_2.5[J]. Earth and Environment, 2016, 44(1):130-138.
[4] 常清,杨复沫,李兴华,等.北京冬季雾霾天气下颗粒物及其化学组分的粒径分布特征研究[J].环境科学学报, 2015,35(2):363-370. Chang Q, Yang F M, Li X H, et al. Characteristics of mass and chemical species size distributions of particulate matter during haze pollution in the winter in Beijing[J]. Acta Scientiae Circumstantiae, 2015,35(2):363-370.
[5] 牟莹莹,楼晟荣,陈长虹,等.利用SPAMS研究上海秋季气溶胶污染过程中颗粒物的老化与混合状态[J].环境科学, 2013,34(6):2071-2080. Mu Y Y, Lou S R, Chen C H, et al. Aging and mixing state of particulate matter during aerosol pollution episode in autumn Shanghai using a single particle aerosol mass spectrometer (SPAMS)[J]. Environmental Science, 2013,34(6):2071-2080.
[6] 潘鹄,耿福海,陈勇航,等.利用微脉冲激光雷达分析上海地区一次灰霾过程[J].环境科学学报, 2010,30(11):2164-2173. Pan H, Geng F H, Chen Y H, et al. Analysis of a haze event by micro-pulse light laser detection and ranging measurements in Shanghai[J]. Acta Scientiae Circumstantiae, 2010,30(11):2164-2173.
[7] 许亚宣,李小敏,于华通,等.邯郸市大气复合污染特征的监测研究[J].环境科学学报, 2015,35(9):2710-2722. Xu Y X, Li X M, Yu H T, et al. Characteristics of air pollution complex in Handan[J]. Acta Scientiae Circumstantiae, 2015,35(9):2710-2722.
[8] 陈永林,谢炳庚,杨勇.全国主要城市群空气质量空间分布及影响因素分析[J].干旱区资源与环境, 2015,29(11):99-103. Chen Y L, Xie B G, Yang Y. Distribution of the urban air quality in the major city clusters in China and the influencing factors[J]. Journal of Arid Land Resources and Environment, 2015,29(11):99-103.
[9] 郑小波,罗宇翔,赵天良,等.中国气溶胶分布的地理学和气候学特征[J].地理科学, 2012,32(3):265-272. Zheng X B, Luo Y X, Zhao T L, et al. Geographical and climatological characterization of aerosol distribution in China[J]. Scientia Geographica Sinica, 2012,32(3):265-272.
[10] 王振波,方创琳,许光,等.2014年中国城市PM_2.5浓度的时空变化规律[J].地理学报, 2015,70(11):1720-1734. Wang Z B, Fang C L, Xu G, et al. Spatial-temporal characteristics of the PM_2.5 in China in 2014[J]. Acta Geographica Sinica, 2015,70(11):1720-1734.
[11] 徐鹏,郝庆菊,吉东生,等.重庆市北碚大气中PM_2.5、NO_x、SO₂和O₃浓度变化特征研究[J].环境科学学报, 2016,36(5):1539-1547. Xu P, Hao Q J, Ji D S, et al. Characteristics of atmospheric PM_2.5, NO_x, SO₂ and O₃ in Beibei district, Chongqing[J]. Acta Scientiae Circumstantiae, 2016,36(5):1539-1547.
[12] 张连科,鲁尚发,焦坤灵,等.包头城区冬春大气颗粒物污染特征及其与气象条件关系[J].大气与环境光学学报, 2017,12(6):401-410. Zhang L K, Lu S F, Jiao K L, et al. Pollution characteristics of particulate matter in urban districts of Baotou and their relationships with meteorological conditions[J]. Journal of Atmospheric and Environmental Optics, 2017,12(6):401-410.
[13] 董群,赵普生,陈一娜.降雨对不同粒径气溶胶粒子碰撞清除能力[J].环境科学, 2016,37(10):3686-3692. Dong Q, Zhao P S, Chen Y N. Impact of collision removal of rainfall on aerosol particles of different sizes[J]. environmental science, 2016,37(10):3686-3692.
[14] 陈多宏,何俊杰,张国华,等.不同天气类型广东大气超级站细粒子污染特征初步研究[J].地球化学, 2014,43(3):217-223. Chen D H, He J J, Zhang G H, et al. A preliminary study on the characteristics of fine particle pollution characteristics for different weather type in the Guangdong atmospheric supersite[J]. Geochimica, 2014,43(3):217-223.
[15] 赵敬国,王式功,张天宇,等.兰州市大气重污染气象成因分析[J].环境科学学报, 2015,35(5):1547-1555. Zhao J G, Wang S G, Zhang T Y, et al. The analysis of meteorological factors causing heavy air pollution in Lanzhou[J]. Acta Scientiae Circumstantiae, 2015,35(5):1547-1555.
[16] 王茜,吴剑斌,林燕芬.CMAQ模式及其修正技术在上海市PM_2.5预报中的应用检验[J].环境科学学报, 2015,35(6):1651-1656. Wang Q, Wu J B, Lin Y F. Implementation of a dynamic linear regression method on the CMAQ forecast of PM_2.5 in Shanghai[J]. Acta Scientiae Circumstantiae, 2015,35(6):1651-1656.
[17] 安静宇,李莉,黄成,等.2013年1月中国东部地区重污染过程中上海市细颗粒物的来源追踪模拟研究[J].环境科学学报, 2014, 34(10):2635-2644. An J Y, Li L, Huang C, et al. Source apportionment of the fine particulate matter in Shanghai during the heavy haze episode in eastern China in January 2013[J]. Acta Scientiae Circumstantiae, 2014, 34(10):2635-2644.
[18] 陈亦君,尤佳红,束炯,等.基于WRF-RTIM的上海地区霾预报MOS方法研究[J].环境科学学报, 2014,34(3):574-581. Chen Y J, You J H, Shu J, et al. A WRF-RTIM-based model output statistics method for haze event prediction in Shanghai[J]. Acta Scientiae Circumstantiae, 2014,34(3):574-581.
[19] 李莉,陈长虹,黄成,等.长江三角洲地区大气O₃和PM₁₀的区域污染特征模拟[J].环境科学, 2008,29(1):237-245. Li L, Chen C H, Huang C, et al. Regional air pollution characteristics simulation of O₃ and PM₁₀ over Yangtze River Delta region[J]. Environmental Science, 2008,29(1):237-245.
[20] 何金海,郭品文,银燕.大气科学概论[M].北京:气象出版社, 2012. He J H, Guo P W, Yin Y. Introduction to atmospheric science[M]. Beijing:China Meteorological Press, 2012.
[21] 张自银,赵秀娟,熊亚军,等.基于动态统计预报方法的京津冀雾霾中期预报试验[J].应用气象学报, 2018,29(1):57-69. Zhang Z Y, Zhao X J, Xiong Y J, et al. The fog/haze medium-range forecast experiments based on dynamic statistic method[J]. Quarterly Journal of Applied Meteorology, 2018,29(1):57-69.
[22] 瞿元昊,许建明, Brasseur G,等.利用多模式最优集成方法预报上海PM_2.5[J].环境科学学报, 2018,38(9):3449-3456. Qu Y H, Xu J M, Brasseur G, et al. Application of OCF on PM_2.5 forecast in Shanghai[J]. Acta Scientiae Circumstantiae, 2018,38(9):3449-3456.
[23] 潘留杰,张宏芳,朱伟军,等.ECMWF模式对东北半球气象要素场预报能力的检验[J].气候与环境研究, 2013,18(1):111-123. Pan L J, Zhang H F, Zhu W J, et al. Forecast performance verification of the ECMWF model over the northeast hemisphere[J]. Climatic and Environmental Research, 2013,18(1):111-123.
[24] 杨兴川,赵文吉,熊秋林,等.2016年京津冀地区PM_2.5时空分布特征及其与气象因素的关系[J].生态环境学报, 2017,26(10):1747-1754. Yang X C, Zhao W J, Xiong Q L, et al. Spatio-temporal distribution of PM_2.5 in Beijing-Tianjin-Hebei (BTH) Area in 2016 and Its relationship with meteorological factors[J]. Ecology and Environmnet, 2017, 26(10):1747-1754.
[25] 苗蕾,廖晓农,王迎春.基于长时间序列的北京PM_2.5浓度日变化及气象条件影响分析[J].环境科学, 2016,37(8):2836-2846. Miao L, Liao X N, Wang Y C. Diurnal variation of PM_2.5 Mass concentration in Beijing and influence of meteorological factors based on long term date[J]. Environmental Science, 2016,37(8):2836-2846.
[26] 李会霞,史兴民.西安市PM_2.5时空分布特征及气象成因[J].生态环境学报, 2016,25(2):266-271. Li H X, Shi X M. Temporal and spatial distribution, meteorological factors of PM_2.5 in Xi'an City[J]. Ecology and Environmnet, 2016, 25(2):266-271.
[27] 岳岩裕,周悦,王晓玲,等.武汉市霾天气过程冷锋和降水对PM_2.5输送和清除效应分析[J].环境科学学报, 2018,38(12):4612-4619. Yue Y Y, Zhou Y, Wang X L, et al. Analysis on transport/purification effect by cold front and precipitation during haze day in Wuhan[J]. Acta Scientiae Circumstantiae, 2018,38(12):4612-4619.
[28] 周述学,王兴,弓中强,等.长江三角洲西部地区PM_2.5输送轨迹分类研究[J].气象学报, 2017,75(6):996-1010. Zhou S X, Wang X, Gong Z Q, et al. Transport patterns of PM_2.5 in the western Yangtze River Delta district,China[J]. Acta Meteorologica Sinica, 2017,75(6):996-1010.
[29] 王燕丽,薛文博,雷宇,等.京津冀区域PM_2.5污染相互输送特征[J].环境科学, 2017,38(12):4897-4904. Wang Y L, Xue W B, Lei Y, et al. Regional transport matrix study of PM_2.5 in Jingjinji Region, 2015[J]. Environmental Science, 2017, 38(12):4897-4904.
[30] 俞小鼎.基于构成要素的预报方法--配料法[J].气象, 2011, 37(8):913-918. Yu X D. Ingredients based forecasting methodology[J]. Meteorological Monthly, 2011,37(8):913-918.
[31] Doswell III C A, Brooks H E, Maddox R A. Flash flood forecasting:An ingredients-based methodology[J]. Wea Forecasting, 1996,11(4):560-581.
[32] 陶诗言,张小玲,张顺利.长江流域梅雨锋暴雨灾害研究[M].北京:气象出版社, 2004. Tao S Y, Zhang X L, Zhang S L. Study on the rainstorm disaster of Meiyu front in the Yangtze River Basin[M]. Beijing:China Meteorological Press, 2004.
[33] 李俊,李武阶,廖移山.基于"配料"的梅雨锋强降水预报方法[J].气象, 2006,32(9):3-8. Li J, Li W J, Liao Y S. An ingredients-based methodology of forecasting heavy rain of Meiyu front[J]. Meteorological Monthly, 2006,32(9):3-8.
[34] 张萍萍,龙利民,张宁,等."分型配料法"在湖北省暴雨预报中的应用研究[J].热带气象学报, 2012,28(5):771-776. Zhang P P, Long L M, Zhang N, et al. Applications of typing ingredients-based methodology to heavy rainfall forecast in Hubei province[J]. Journal of Tropical Meteorology, 2012,28(5):771-776.
[35] 梅鹏蔚.稳定气象条件对天津市环境空气质量的影响[J].城市环境与城市生态, 2006,19(4):37-39. Mei P W. Influence of steady weather conditions to air quality in Tianjin[J].Urban Environment & Urban Ecology, 2006,19(4):37-39.
[36] 邹振操,邓院昌,邓治平.不同初始场对WRF模式风场模拟效果的影响分析[J].可再生能源, 2015,33(7):1048-1053. Zou Z C, Deng Y C, Deng Z P. Effect of different initial fields on wind field simulation with WRF model[J]. Renewable Energy, 2015, 33(7):1048-1053.
[37] 蔡子颖,韩素芹,邱晓滨,等.基于255m气象塔天津地区污染天气高空风特征研究[J].环境科学学报, 2018,38(9):3406-3413. Cai Z Y, Han S Q, Qiu X B, et al. The characteristics of high-altitude wind about the analysis of pollution weather by 255 meters meteorological tower in Tianjin[J]. Acta Scientiae Circumstantiae, 2018,38(9):3406-3413.
[38] 冯业荣.广州单站要素及特征曲线显示系统简介[J].广东气象, 2001,(1):13-15. Feng Y R. A Brief look at a visual display system on single-station elements and characteristic curves in Guangzhou[J]. Journal of Guangdong Meteorology, 2001,(1):13-15.
[39] 张恒德,张碧辉,吕梦瑶,等.北京地区静稳天气综合指数的初步构建及其在环境气象中的应用[J].气象, 2017,43(8):998-1004. Zhang H D, Zhang B H, Lü M Y, et al. Development and application of stable weather index of Beijing in environmental meteorology[J]. Meteorological Monthly, 2017,43(8):998-1004.
[40] 郭倩,汪嘉杨,周子航,等.成都市一次典型空气重污染过程特征及成因分析[J].环境科学学报, 2018,38(2):629-639. Guo Q, Wang J Y, Zhou Z H, et al. Characteristics and reason analysis of a typical heavy air pollution event in Chengdu[J]. Acta Scientiae Circumstantiae, 2018,38(2):629-639.
[41] 张恒德,吕梦瑶,张碧辉,等.2014年2月下旬京津冀持续重污染过程的静稳天气及传输条件分析[J].环境科学学报, 2016,36(12):4340-4351. Zhang H D, Lü M Y, Zhang B H, et al. Analysis of the stagnant meteorological situation and the transmission condition of continuous heavy pollution course from February 20 to 26, 2014 in Beijing-Tianjin-Hebei[J]. Acta Scientiae Circumstantiae, 2016,36(12):4340-4351.
[42] 孙峰,张大伟,孙瑞雯,等.北京地区冬季典型PM_2.5重污染案例分析[J].中国环境监测, 2014,30(6):1-12. Sun F, Zhang D W, Sun R W, et al. Typical heavy pollution episode analysis on PM_2.5 in winter of Beijing[J]. Environmental Monitoring in China, 2014,30(6):1-12.
[43] 贺瑶,朱彬,李锋,等.长江三角洲地区PM_2.5两种污染来源对比分析[J].中国环境科学, 2017,37(4):1213-1222. He Y, Zhu B, Li F, et al. Modeling study on the comparison of the impacts of two kinds of pollution source in a haze episode occurred over the Yangtze River Delta during late January, 2015[J]. China Environmental Science, 2017,37(4):1213-1222.
[44] 高健,王韬,柴发合,等.长江三角洲大气颗粒物生成-成长过程的研究--不同气团影响下的过程特征[J].中国环境科学, 2010, 30(7):931-940. Gao J, Wang T, Chai F H, et al. Observational study on particle nucleation process in the Yangtze River Delta, China:The growth property in different air masses[J]. China Environmental Science, 2010,30(7):931-940.
[45] 蒋永成,赵天良,王宏,等.福州市PM_2.5污染过程中大气边界层和区域传输研究[J].中国环境科学, 2015,35(2):347-355. Jiang Y C, Zhao T L, Wang H, et al. Analysis on atmospheric boundary layer and regional transport during PM_2.5 Pollution episodes in Fuzhou[J]. China Environmental Science, 2015,35(2):347-355.
[46] 顾沛澍,刘端阳,陈鹏,等.2016年12月江苏省两次连续污染过程气象条件对比分析[J].环境科学研究, 2018,31(7):1223-1232. Gu P S, Liu D Y, Chen P, et al. Comparative analysis of meteorological conditions in two continuous air pollution processes in December 2016 in Jiangsu Province, China[J]. Research of Environmental Sciences, 2018,31(7):1223-1232.
[47] 花丛,张恒德,张碧辉.2013-2014冬半年北京重污染天气气象传输条件分析及预报指数初建[J].气象, 2016,42(3):314-321. Hua C, Zhang H D, Zhang B H. Analysis on advective transport condition for heavy air pollution processes in Beijing during the 2013-2014 Winter months and design of transmission weather index[J]. Meteorological Monthly, 2016,42(3):314-321.
[48] 瞿元昊,马井会,许建明,等.空气污染气象指数在上海地区的应用[J].气象, 2018,44(5):704-712. Qu Y H, Ma J H, Xu J M, et al. Application of meteorological air pollution index in Shanghai[J]. Meteorological Monthly, 2018,44(5):704-712.
[49] 朱乾根.天气学原理和方法[M].北京:气象出版社, 2007. Zhu Q G. Principles and methods of meteorology[M]. Beijing:China Meteorological Press, 2007.
[50] 余钟奇,马井会,曹钰,等.影响上海PM_2.5污染不同源地和路径的数值模拟[J].中国环境科学, 2019,39(1):21-31. Yu Z Q, Ma J H, Cao Y, et al. Numerical simulations of sources and transport pathways of different PM_2.5 pollution types in Shanghai. China Environmental Science, 2019,39(1):21-31.
[51] 许建明,常炉予,马井会,等.上海秋冬季PM_2.5污染天气形势的客观分型研究[J].环境科学学报, 2016,36(12):4303-4314. Xu J M, Chang L Y, Ma J H, et al. Objective synoptic weather classification on PM_2.5 pollution during autumn and winter seasons in Shanghai[J]. Acta Scientiae Circumstantiae, 2016,36(12):4303-4314.
[52] 张天航,迟茜元,饶晓琴,等.2018年国家级空气质量主客观预报TS评分对比检验[J].环境工程技术学报, 2019,9(3):4-13. Zhang T H, Chi Q Y, Rao X Q, et al. Verification of national subjective and objective air quality forecast in 2018 by TS score[J]. Journal of Environmental Engineering Technology, 2019,9(3):4-13.