气候变化领域集成服务门户(CCPortal): Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan

CCPortal

DOI	10.1016/j.atmosenv.2020.117909
	Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan
	Cheng F.-Y.; Feng C.-Y.; Yang Z.-M.; Hsu C.-H.; Chan K.-W.; Lee C.-Y.; Chang S.-C.
发表日期	2021
ISSN	1352-2310
卷号	244
英文摘要	A real-time air quality forecasting (AQF) system was developed in Taiwan using the Weather Research and Forecasting meteorological model and Community Multiscale Air Quality model framework. This study evaluated the performance of the one-year archived AQF PM2.5 forecasts (October 2018 to September 2019) and developed a bias-correction method to improve the accuracy of the PM2.5 forecasts. The bias-correction method incorporates a cluster-analysis-based synoptic weather pattern (WP) classification (one type of analog method). In principle, the historical model errors are categorized according to the synoptic WP and used to adjust the PM2.5 forecast. First, the synoptic WPs are determined using K-means cluster analysis (six WPs were identified). Second, the historical AQF PM2.5 bias at each surface station is estimated for each classified WP. Third, a linear-regression relationship between the AQF PM2.5 bias and PM2.5 forecasts for the six WPs is developed to postprocess the PM2.5 forecasts. The AQF PM2.5 bias is found to have a strong dependency on the synoptic WP. A performance assessment of the AQF PM2.5 forecasts reveals systematic PM2.5 underprediction, with the most pronounced underprediction occurring on days associated with weak synoptic weather conditions. Under these conditions, a severe PM2.5 event is likely to occur in Taiwan. The bias-correction method is able to reduce the PM2.5 forecast error and improve the root mean square error (RMSE) and mean bias (MB) calculations. The improvement is most significant on days associated with a weak synoptic WP and in regions where high PM2.5 concentrations are likely to occur. The method is shown to be effective at reducing the AQF PM2.5 bias. © 2020 Elsevier Ltd
关键词	Air quality forecasting system Bias correction PM2.5 forecast Synoptic weather pattern WRF-CMAQ
语种	英语
scopus关键词	Air quality; Cluster analysis; Electromagnetic wave attenuation; Errors; K-means clustering; Mean square error; Air quality forecasting; Community multi-scale air quality models; Meteorological modeling; Regression relationship; Root mean square errors; Synoptic weather conditions; Synoptic weather patterns; Weather research and forecasting; Weather forecasting; air quality; climate conditions; climate modeling; forecasting method; particulate matter; performance assessment; real time; synoptic meteorology; weather; air quality; article; calculation; cluster analysis; forecasting; linear regression analysis; particulate matter 2.5; Taiwan; weather; Taiwan
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/248856
作者单位	Department of Atmospheric Sciences, National Central University, Taiwan; Manysplendid Infotech, Taipei, Taiwan; Department of Mechanical Engineering, University of Colorado, Boulder, CO, United States; Environmental Protection Administration, Executive Yuan, R.O.C. Taiwa, Taipei, Taiwan
推荐引用方式 GB/T 7714	Cheng F.-Y.,Feng C.-Y.,Yang Z.-M.,et al. Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan[J],2021,244.
APA	Cheng F.-Y..,Feng C.-Y..,Yang Z.-M..,Hsu C.-H..,Chan K.-W..,...&Chang S.-C..(2021).Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan.ATMOSPHERIC ENVIRONMENT,244.
MLA	Cheng F.-Y.,et al."Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan".ATMOSPHERIC ENVIRONMENT 244(2021).