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DOI	10.1029/2019JD032293
	Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 1. Bias Correction With Surface Observations in Nonrural Areas
	Zhang H.; Wang J.; García L.C.; Ge C.; Plessel T.; Szykman J.; Murphy B.; Spero T.L.
发表日期	2020
ISSN	2169897X
卷号	125 期号:14
英文摘要	This work is the first of a two-part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble-based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS-Chem, WRF-Chem, and WRF-CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20–50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least-square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite-based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	air quality in nonrural areas; GEOS-Chem; Kalman filter ensemble; PM2.5 real-time forecast; WRF-Chem; WRF-CMAQ
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/185910
作者单位	Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, United States; Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, United States; General Dynamics Information Technology, RTPNC, United States; U.S. Environmental Protection Agency, RTPNC, United States
推荐引用方式 GB/T 7714	Zhang H.,Wang J.,García L.C.,et al. Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 1. Bias Correction With Surface Observations in Nonrural Areas[J],2020,125(14).
APA	Zhang H..,Wang J..,García L.C..,Ge C..,Plessel T..,...&Spero T.L..(2020).Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 1. Bias Correction With Surface Observations in Nonrural Areas.Journal of Geophysical Research: Atmospheres,125(14).
MLA	Zhang H.,et al."Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 1. Bias Correction With Surface Observations in Nonrural Areas".Journal of Geophysical Research: Atmospheres 125.14(2020).