摘要
基于CUACE(CMA Unified Atmospheric Chemistry Environment)和CMAQ(Community Multiscale Air Quality)空气质量模式预报产品,应用BP(Back-Propagation)人工神经网络法建立沈阳市不同地点小风和高湿条件下PM_(2.5)浓度集成预报模型,并对预报结果进行检验。结果表明:与单一空气质量模式相比,集成模型预报的PM_(2.5)浓度更接近实测值,预报的PM_(2.5)浓度的平均偏差和归一化均方误差均明显减小,预报的PM_(2.5)浓度的模拟值在观测值两倍范围内的百分比(FAC2)明显提高。集成模型能较好地预报PM_(2.5)浓度高值的变化,且显著提高了沈阳市外围城区PM_(2.5)浓度的预报水平。集成预报模型可以实现CUACE和CM AQ两种空气质量模式产品的最优综合,对空气质量的实时预报具有一定的参考价值。
Based on the forecasting products of CUACE （CMA Unified Atmospheric Chemistry Environment） and CMAQ （ Community Multiscale Air Quality） models, integration forecast models for PM2 5 at different positions in Shenyang under conditions of small wind speed and high relative humidity were developed and validated using an artificial neural network method with back-propagation （BP） algorithm. The results indicate that PM25 concentra- tions predicted by integration models are much closer to their observational values than those predicted by CUACE and CMAQ. The values of mean deviation and NMSE （Normalized Mean Square Error） of modelling results de- crease significantly, and the values of FAC2 increase obviously. The PM25 forecast from integration models can better reflect the variation of high PM2 5 concentrations, and its development at surrounding urban areas of Sheny- ang is significant. The integration models based on BP artificial neural network are a kind of effective method for PM2 5 forecast, which can provide a reference to the real-time operational forecast of air quality.
作者
李晓岚
刘旸
栾健
马雁军
王扬锋
张婉莹
LI Xiao-lan1,LIU Yang2, LUAN Jian3 ,MA Yan-jun1, WANG Yang-feng1, ZHANG Wan-ying4(1. Institute of Atmospheric Environment, CMA, Shenyang 110166, China; 2. Liaoning Weather Modification Office, Shenyang 110166 ,China; 3. Liaoning Branch of China Meteorological Administration Training Centre, Shenyang 110166, China; 4. Liaoning Meteorological Service Center, Shenyang 110166, Chin)
出处
《气象与环境学报》
2018年第2期100-106,共7页
Journal of Meteorology and Environment
基金
国家重点研发计划课题(2016YFC0203304)
辽宁省气象局科学技术研究项目(博士科研专项)(D201603)
国家科技支撑计划课题(2014BAC16B04)
公益性行业(气象)科研专项经费(GYHY201406031)
中央级公益性科研院所基本科研业务费专项(2016SYIAEZD3)共同资助
关键词
PM2.5质量浓度
集成预报
CUACE
CMAQ
BP神经网络
PM2.5 mass concentration
Integration forecast
CMA Unified Atmospheric Chemistry Environment （CUACE） model
Community Multiscale Air Quality （CMAQ） model
Back-propagation neural network
