基于BP人工神经网络法沈阳市PM2.5质量浓度集成预报试验

Xiao-lan LI,Yang LIU,Jian LUAN,Yan-jun MA,Yang-feng WANG,Wan-ying ZHANG

Journal of Meteorology and Environment（2018）

Cited 3|Views7

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Abstract

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PM2.5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PM2.5浓度更接近实测值,预报的PM2.5浓度的平均偏差和归一化均方误差均明显减小,预报的PM2.5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PM2.5浓度高值的变化,且显著提高了沈阳市外围城区PM2.5浓度的预报水平.集成预报模型可以实现CUACE和CMAQ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.

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Key words

PM2.5mass concentration,Integration forecast,CMA Unified Atmospheric Chemistry Environment (CUACE)model,Community Multiscale Air Quality(CMAQ)model,Back-propagation neural network

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