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Parameterization and evaluation of the CALMET/CALPUFF model system in near-field and complex terrain-Terrain data, grid resolution and terrain adjustment method

机译:CALMET / CALPUFF模型系统在近场和复杂地形-地形数据中的参数化和评估,网格分辨率和地形调整方法

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Effective and accurate modeling of air quality in complex terrain constitutes one of the main challenges for the community of modelers. One of the basic problems is the selection of input data of adequate quality in combination with a uniform configuration of the modeling systems. Simultaneously, the primary aim is to obtain predictions of satisfying accuracy. In the article the results of the evaluation for the CALMET/CALPUFF modeling system in near-field and complex terrain were presented. Research was conducted based on three experimental databases for the dispersion model evaluation, i.e. Martin's Creek (SO2), Lovett (SO2) and Tracy Power Plant (SF6). Each experiment concerned an area characterized by different topography, meteorological conditions, emission source features and the location of tracer substance monitors (SO, SF6). The aim of the study was to determine the optimal settings for the CALMET/CALPUFF models with regard to the digital elevation model dataset (GTOPO30, SRTM3, NED), grid resolution (ranging from 100 to 4000 m) and the terrain adjustment methods available in the CALPUFF model (MCTADJ = 0,1, 2 and 3). The results of the CALMET/CALPUFF accuracy evaluation showed, that the use of the digital elevation model (DEM) with a horizontal accuracy of approximately 90 m and a vertical accuracy of 15 m (SRTM3) is sufficient. Application of more accurate data (NED) resulted in comparable model evaluation outcomes. Using both dense and coarse grids resulted in LB and/or FBRFIC higher than 0.6. Omitting the terrain adjustment method in the CALPUFF model results in the twofold underestimation of the measurements. The best results of the CALMET/CALPUFF accuracy evaluation for each experiment (vertical bar FB vertical bar 0.30, FAC = 1.0, NAD = 0.15, VG 1.12, IOA 0.5) were achieved for the grid resolution of 400 m with the use of a partial plume path adjustment method (MCTADJ = 3). (C) 2019 Elsevier B.V. All rights reserved.
机译:对复杂地形中的空气质量进行有效而准确的建模是建模者社区面临的主要挑战之一。基本问题之一是结合建模系统的统一配置来选择质量足够的输入数据。同时,主要目的是获得满足精度的预测。在本文中,介绍了在近场和复杂地形中对CALMET / CALPUFF建模系统的评估结果。基于三个用于色散模型评估的实验数据库进行了研究,即马丁溪(SO2),洛维特(SO2)和特雷西电厂(SF6)。每个实验都涉及一个具有不同地形,气象条件,排放源特征和示踪物质监测仪(SO,SF6)位置的区域。该研究的目的是针对数字高程模型数据集(GTOPO30,SRTM3,NED),网格分辨率(范围为100至4000 m)以及地形调整方法确定CALMET / CALPUFF模型的最佳设置。 CALPUFF模型(MCTADJ = 0、1、2和3)。 CALMET / CALPUFF精度评估的结果表明,使用水平精度约为90 m且垂直精度为15 m(SRTM3)的数字高程模型(DEM)就足够了。应用更准确的数据(NED)得出了可比的模型评估结果。同时使用密集网格和粗糙网格会导致LB和/或FBRFIC高于0.6。在CALPUFF模型中省略地形调整方法会导致测量值被双重低估。对于每个分辨率为400 m的网格分辨率,每个实验CALMET / CALPUFF精度评估的最佳结果(垂直条FB垂直条<0.30,FAC = 1.0,NAD <= 0.15,VG <1.12,IOA> 0.5)获得了最佳结果。使用局部羽流路径调整方法(MCTADJ = 3)。 (C)2019 Elsevier B.V.保留所有权利。

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