|
|
Forecast of air quality pollutants' concentrations based on BP neural network multi-model ensemble method |
ZHANG Heng-de1,2, ZHANG Ting-yu2, LI Tao2, ZHANG Tian-hang1 |
1. Nation Meteorological Center of China Meteorological Administration, Beijing 100081, China;
2. School of Electronic & Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China |
|
|
Abstract Based on the forecast products of three numerical models including CUACE, BREMPS and WRF-Chem, a multi-model ensemble forecast system was established by BP neural network. Firstly, the training function, the nodes number of hidden layer and the length of training samples of BP neural network were determined to be trainbr, ten and fifty, respectively, by sensitivity experiments. Then, 3sites in Beijing, Tianjin and Shijiazhuang were selected to evaluate the performances of ensemble forecast system. Results showed that (1) Compared with single models, the normalized mean biases, the root mean square error, and the correlation coefficient between forecasted and observed pollutant concentrations in 3~72hours decreased from -100%~200% to -20%~20%, decreased by 15%, and increased from 0.1~0.8 to 0.3~0.85, respectively, indicating that the forecast results were better than single models. (2) The TS scores of AQI values in mild and medium pollution events in 2016 in Beijing of ensemble forecast system were 22% and 10% higher than those results of CUACE model, respectively. The rate of vacancy forecast and missing forecast of heavy pollution in Tianjin decreased by 31% and 25%, respectively. (3) The forecasted and observed trends of PM2.5 concentrations in December 2016 were consistent well with each other.
|
Received: 15 September 2017
|
|
|
|
|
[1] |
赵普生,徐晓峰,孟伟,等.京津冀区域霾天气特征[J]. 中国环境科学, 2012,32(1):31-36.
|
[2] |
张霖琳,王超,刀谞,等.京津冀地区城市环境空气颗粒物及其元素特征分析[J]. 中国环境科学, 2014,34(12):2993-3000.
|
[3] |
赵秀娟,徐敬,张自银,等.北京区域环境气象数值预报系统及PM2.5预报检验[J]. 应用气象学报, 2016,27(2):160-172.
|
[4] |
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.
|
[5] |
江琪,王飞,张恒德,等.北京市PM2.5和反应性气体浓度的变化特征及其与气象条件的关系[J]. 中国环境科学, 2017,37(3):829-837.
|
[6] |
李晓岚,马雁军,王扬锋,等.基于CUACE系统沈阳地区春季空气质量预报的校验及修正[J]. 气象与环境学报, 2016,32(6):10-18.
|
[7] |
杨关盈,邓学良,吴必文,等.基于CUACE模式的合肥地区空气质量预报效果检验[J]. 气象与环境学报, 2017,33(1):51-57.
|
[8] |
Zhou C H, Gong S, Zhang X Y, et al. Towards the improvements of simulating the chemical and optical properties of Chinese aerosols using an online coupled model CUACE/Aero[J]. Tellus Series B-chemical & Physical Meteorology, 2012,64(1):91-102.
|
[9] |
常炉予,许建明,周广强,等.上海典型持续性PM2.5重度污染的数值模拟[J]. 环境科学, 2016,37(3):825-833.
|
[10] |
周广强,谢英,吴剑斌,等.基于WRF-Chem模式的华东区域PM2.5预报及偏差原因[J]. 中国环境科学, 2016,36(8):2251-2259.
|
[11] |
程念亮,李红霞,孟凡,等.我国城市PM2.5数值预报简述[J]. 安徽农业科学, 2015,43(7):243-246,271.
|
[12] |
段明铿,王盘兴.集合预报方法研究及应用进展综述[J]. 大气科学学报, 2004,27(2):279-288.
|
[13] |
李亚云,束炯,沈愈.上海市PM2.5浓度统计释用综合集成研究[J]. 中国环境科学, 2017,37(2):486-496.
|
[14] |
Monache L D, Stull R B. An ensemble air-quality forecast over western Europe during an ozone episode[J]. Atmospheric Environment, 2003,37(25):3469-3474.
|
[15] |
Pagowski M, Grell G A, Mckeen S A, et al. A simple method to improve ensemble-based ozone forecasts[J]. Geophysical Research Letters, 2005,32(7):351-394.
|
[16] |
陈焕盛,王自发,吴其重,等.空气质量多模式系统在广州应用及对PM10预报效果评估[J]. 气候与环境研究, 2013,18(4):427-435.
|
[17] |
王自发,吴其重,Alex GBAGUIDI,等.北京空气质量多模式集成预报系统的建立及初步应用[J]. 南京信息工程大学学报, 2009,1(1):19-26.
|
[18] |
黄思,唐晓,徐文帅,等.利用多模式集合和多元线性回归改进北京PM10预报[J]. 环境科学学报, 2015,35(1):56-64.
|
[19] |
林春泽,智协飞,韩艳,等.基于TIGGE资料的地面气温多模式超级集合预报[J]. 应用气象学报, 2009,20(6):706-712.
|
[20] |
智协飞,林春泽,白永清,等.北半球中纬度地区地面气温的超级集合预报[J]. 气象科学, 2009,29(5):569-574.
|
[21] |
Zhi X F, Qi H X, Bai Y Q, et al. A comparison of three kinds of multi-model ensemble forecast techniques based on the TIGGE data[J]. Acta Meteorologica Sinica, 2012,26(1):41-51.
|
[22] |
智协飞,季晓东,张璟,等.基于TIGGE资料的地面气温和降水的多模式集成预报[J]. 大气科学学报, 2013,36(3):257-266.
|
[23] |
赵声蓉.多模式温度集成预报[J]. 应用气象学报, 2006,17(1):52-58.
|
[24] |
熊聪聪,王静,宋鹏,等.遗传算法在多模式集成天气预报中的应用[J]. 天津科技大学学报, 2008,23(4):80-84.
|
[25] |
王俭,胡筱敏,郑龙熙,等.基于BP模型的大气污染预报方法的研究[J]. 环境科学研究, 2002,15(5):62-64.
|
[26] |
王恺,赵宏,刘爱霞,等.基于风险神经网络的大气能见度预测[J]. 中国环境科学, 2009,29(10):1029-1033.
|
[27] |
蔡子颖,韩素芹,姚青,等.基于BP神经网络天津地区霾天气能见度预报研究[C]//中国环境科学学会学术年会论文集(第一卷), 2016.
|
[28] |
张伟,王自发,安俊岭,等.利用BP神经网络提高奥运会空气质量实时预报系统预报效果[J]. 气候与环境研究, 2010,15(5):595-601.
|
[29] |
Ding W, Zhang J, Leung Y. Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks[J]. Environmental Science & Pollution Research, 2016,23(19):1-14.
|
[30] |
万显烈,杨凤林,王慧卿.利用人工神经网络对空气中O3浓度进行预测[J]. 中国环境科学, 2003,23(1):110-112.
|
[31] |
陈明,等. MATLAB神经网络原理与实例精解[M]. 北京:清华大学出版社, 2013:164-166.
|
[32] |
Hecht-Nielsen R. Counterpropagation networks[J]. Applied Optics, 1987,26(23):4979.
|
|
|
|