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

doi:10.3969/j.issn.1673-503X.2018.02.013

Journal of Meteorology and Environment ›› 2018, Vol. 34 ›› Issue (2): 100-106.doi: 10.3969/j.issn.1673-503X.2018.02.013

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Integration forecast experimentation for PM_2.5 mass concentration in Shenyang based on BP artificial neural network

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, China

Received:2016-12-15 Revised:2017-03-07 Online:2018-04-30 Published:2018-04-30

Abstract

Abstract: Based on the forecasting products of CUACE (CMA Unified Atmospheric Chemistry Environment) and CMAQ (Community Multiscale Air Quality) models,integration forecast models for PM_2.5 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 PM_2.5 concentrations 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 decrease significantly,and the values of FAC2 increase obviously.The PM_2.5 forecast from integration models can better reflect the variation of high PM_2.5 concentrations,and its development at surrounding urban areas of Shenyang is significant.The integration models based on BP artificial neural network are a kind of effective method for PM_2.5 forecast,which can provide a reference to the real-time operational forecast of air quality.

Key words: PM_2.5 mass concentration, Integration forecast, CMA Unified Atmospheric Chemistry Environment (CUACE) model, Community Multiscale Air Quality (CMAQ) model, Back-propagation neural network

CLC Number:

P457.7

LI Xiao-lan, LIU Yang, LUAN Jian, MA Yan-jun, WANG Yang-feng, ZHANG Wan-ying. Integration forecast experimentation for PM_2.5 mass concentration in Shenyang based on BP artificial neural network[J]. Journal of Meteorology and Environment, 2018, 34(2): 100-106.

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Integration forecast experimentation for PM_2.5 mass concentration in Shenyang based on BP artificial neural network

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Integration forecast experimentation for PM_2.5 mass concentration in Shenyang based on BP artificial neural network