气候变化领域集成服务门户(CCPortal): Machine learning based bias correction for numerical chemical transport models

CCPortal

DOI	10.1016/j.atmosenv.2020.118022
	Machine learning based bias correction for numerical chemical transport models
	Xu M.; Jin J.; Wang G.; Segers A.; Deng T.; Lin H.X.
发表日期	2021
ISSN	1352-2310
卷号	248
英文摘要	Air quality warning and forecasting systems are usually based on numerical chemical transport models (CTMs). Those dynamic models perform predictions by simulating the life cycles of the atmospheric components, including emission, transport and removal. However, the accuracy of these CTMs are still limited because of many imperfections, e.g., uncertainties in the input sources such as emission inventories, wind fields, boundary conditions, as well as insufficient knowledge about the atmospheric dynamics themselves. All these will mislead the CTM prediction constantly, or in a systematic way. In this paper, an approach based on machine learning is applied to predict model bias in the CTM. It is then combined with the CTM for formulating a hybrid forecast system. To our knowledge, it is the first time that machine learning methods are used in this way. The hybrid system is tested on the fine particular matter (PM2.5) prediction in Shanghai, China. The results showed that machine learning can be an effective tool to improve the accuracy of CTM prediction. In case of short term PM2.5 forecast (forecast length less than 12 h), statistical metrics of the root mean square error, mean absolute error, mean absolute percentage error as well as the air quality rank predicted accuracy all show the forecast skill is remarkably improved; while for long term prediction, improvement is not ensured. © 2021 Elsevier Ltd
英文关键词	Air quality; Atmospheric chemistry; Atmospheric movements; Errors; Hybrid systems; Life cycle; Machine learning; Mean square error; Atmospheric components; Atmospheric dynamics; Chemical transport models; Emission inventories; Long-term prediction; Machine learning methods; Mean absolute percentage error; Root mean square errors; Forecasting; air quality; atmospheric dynamics; atmospheric pollution; error correction; knowledge; machine learning; prediction; sampling bias; weather forecasting; wind field; air quality; article; China; machine learning; particulate matter 2.5; prediction; skill; China; Shanghai
语种	英语
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/168998
作者单位	School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, 201620, China; Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China; TNO, Department of Climate, Air and Sustainability, Utrecht, Netherlands; Delft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands
推荐引用方式 GB/T 7714	Xu M.,Jin J.,Wang G.,et al. Machine learning based bias correction for numerical chemical transport models[J],2021,248.
APA	Xu M.,Jin J.,Wang G.,Segers A.,Deng T.,&Lin H.X..(2021).Machine learning based bias correction for numerical chemical transport models.Atmospheric Environment,248.
MLA	Xu M.,et al."Machine learning based bias correction for numerical chemical transport models".Atmospheric Environment 248(2021).