气候变化领域集成服务门户(CCPortal): Analog ensemble technique to post-process WRF-CAMx ozone and particulate matter forecasts

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DOI	10.1016/j.atmosenv.2021.118439
	Analog ensemble technique to post-process WRF-CAMx ozone and particulate matter forecasts
	Solomou E.S.; Pappa A.; Kioutsioukis I.; Poupkou A.; Liora N.; Kontos S.; Giannaros C.; Melas D.
发表日期	2021
ISSN	1352-2310
卷号	256
英文摘要	Post-processing techniques can provide significant improvement in the forecast skill of air quality models. In this study, the implementation of an analog-based technique to Comprehensive Air Quality Model with Extensions (CAMx) coupled with Weather Research and Forecasting (WRF) model results is examined. WRF-CAMx runs with a 2-km horizontal grid increment over Greece for one month of every season of the year 2012 (i.e., January, April, July and October). The analog ensemble (AnEn) technique attempts to improve the accuracy of ozone and particulate matter forecasts by using a method that searches for analogs in past forecasts. An optimization process that minimizes Root Mean Square Error (RMSE) metric has been used to find the best AnEn configuration. The corrected forecasts are computed with two approaches, i.e., AnEn ‘mean’ and AnEn ‘bias correction’ (AnEn-bias) approach. The methods are tested with observations from 23 surface stations for ozone, 16 stations for PM10 and 3 stations for PM2.5 for an 11-day period for each month. The results which are very similar for both techniques show an improvement of the forecast skill of all pollutants. The corrected forecasts have smaller RMSE and higher Correlation Coefficient (R). A reduction of 40 and 70% for AnEn RMSE values is found for ozone and particulate matter, respectively. For AnEn R, an improvement of 11% for ozone, 46% for PM10 and 26% for PM2.5 is estimated. These techniques are also successful in drastically reducing the mean bias of raw forecasts to close to zero. © 2021 Elsevier Ltd
关键词	Analog ensemble Bias correction Ozone Particulate matter Post-processing technique WRF-CAMx
语种	英语
scopus关键词	Air quality; Mean square error; Particles (particulate matter); Weather forecasting; Air quality modelling; Analog ensemble; Bias correction; Ensemble techniques; Particulate Matter; Post process; Post-processing techniques; Root mean square errors; Weather research and forecasting; Weather research and forecasting-CAMx; Ozone; ozone; accuracy assessment; air quality; atmospheric modeling; ensemble forecasting; ozone; particulate matter; weather forecasting; air pollutant; air pollution; air quality; analog ensemble technique; Article; correlation coefficient; ecological procedures; forecasting; Greece; particulate matter; particulate matter 10; particulate matter 2.5; priority journal; process optimization; simulation; weather; Greece
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/248401
作者单位	Laboratory of Atmospheric Physics, Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece; Laboratory of Atmospheric Physics, Department of Physics, University of Patras, Rio, Greece; Frederick Research Center, Nicosia, 1303, Cyprus; National Observatory of Athens, Institute of Environmental Research and Sustainable Development, Athens, 15236, Greece
推荐引用方式 GB/T 7714	Solomou E.S.,Pappa A.,Kioutsioukis I.,et al. Analog ensemble technique to post-process WRF-CAMx ozone and particulate matter forecasts[J],2021,256.
APA	Solomou E.S..,Pappa A..,Kioutsioukis I..,Poupkou A..,Liora N..,...&Melas D..(2021).Analog ensemble technique to post-process WRF-CAMx ozone and particulate matter forecasts.ATMOSPHERIC ENVIRONMENT,256.
MLA	Solomou E.S.,et al."Analog ensemble technique to post-process WRF-CAMx ozone and particulate matter forecasts".ATMOSPHERIC ENVIRONMENT 256(2021).