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Regionalized PM2.5 Community Multiscale Air Quality model performance evaluation across a continuous spatiotemporal domain

机译:连续时空范围内的区域PM2.5社区多尺度空气质量模型绩效评估

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摘要

The regulatory Community Multiscale Air Quality (CMAQ) model is a means to understanding the sources, concentrations and regulatory attainment of air pollutants within a model's domain. Substantial resources are allocated to the evaluation of model performance. The Regionalized Air quality Model Performance (RAMP) method introduced here explores novel ways of visualizing and evaluating CMAQ model performance and errors for daily Particulate Matter <= 2.5 mu m (PM2.5) concentrations across the continental United States. The RAMP method performs a non-homogenous, non-linear, non-homoscedastic model performance evaluation at each CMAQ grid. This work demonstrates that CMAQ model performance, for a well-documented 2001 regulatory episode, is non-homogeneous across space/time. The RAMP correction of systematic errors outperforms other model evaluation methods as demonstrated by a 22.1% reduction in Mean Square Error compared to a constant domain wide correction. The RAMP method is able to accurately reproduce simulated performance with a correlation of r = 76.1%. Most of the error coming from CMAQ is random error with only a minority of error being systematic. Areas of high systematic error are collocated with areas of high random error, implying both error types originate from similar sources. Therefore, addressing underlying causes of systematic error will have the added benefit of also addressing underlying causes of random error. (C) 2016 Published by Elsevier Ltd.
机译:法规共同体多尺度空气质量(CMAQ)模型是一种了解模型域内空气污染物的来源,浓度和法规要求的手段。大量资源用于评估模型性能。此处介绍的区域空气质量模型性能(RAMP)方法探索了可视化和评估CMAQ模型性能以及美国大陆每日颗粒物浓度≤2.5微米(PM2.5)的误差的新颖方法。 RAMP方法在每个CMAQ网格上执行非均匀,非线性,非随机模型性能评估。这项工作表明,对于一个有据可查的2001年管制事件,CMAQ模型的性能在时空上是不均匀的。系统误差的RAMP校正优于其他模型评估方法,与固定域范围的校正相比,均方误差降低了22.1%,这证明了这一点。 RAMP方法能够准确再现模拟性能,相关系数为r = 76.1%。来自CMAQ的大多数错误是随机错误,只有少数错误是系统性的。系统误差高的区域与随机误差高的区域并置,这意味着这两种错误类型均源自相似的来源。因此,解决系统错误的根本原因将带来更多好处,即解决随机错误的根本原因。 (C)2016由Elsevier Ltd.出版

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