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Prognostic Meteorological Data AERMOD: A Case Study Using MMIF

机译：预后气象数据和AERMOD：使用MMIF的案例研究

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Prognostic meteorological models use advanced algorithms to estimate meteorological variables at points where observational data is otherwise unavailable. This presents one possible solution to the problem of finding representative meteorological data for use in air quality dispersion modeling analyses. In 2012, the United States Environmental Protection Agency (U.S. EPA) published the beta version of a new utility called the Mesoscale Model Interface Program (MMIF). MMIF "converts prognostic meteorological model output fields...from the Fifth Generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) model" into direct input formats compatible with three air quality models: AERMOD, CALPUFF, and SCICHEM. The AERMOD modeling system typically utilizes a meteorological preprocessor called AERMET to estimate the planetary boundary layer depth from hourly surface observations and twice-daily upper air soundings. MMIF can either replace AERMET entirely by generating model-ready meteorological data files or it can create direct input files to AERMET. This study compares differences in meteorological parameters for a single AERMET run (dated 11059) using National Weather Service data to runs using "pseudo-station" data extracted by MMIF from MM5 and WRF. The resulting impacts upon ground-level concentrations calculated by AERMOD (dated 12060) for these meteorological data sets are also reviewed. Comparison of the meteorological data files show the peak average model mixing height output by AERMET was 4-16% lower than prognostic data formatted by MMIF. Average nighttime mixing heights were 1.75-2.15 times greater in the AERMET data set than the prognostic models, however. Winds speeds from the MM5 and WRF data averaged 2-3 m/s lower than the National Weather Service data, and peak speeds from the prognostic data were 5-6 m/s lower. The AERMOD results reflect these meteorological differences and highlight some significant differences in how prognostic models handle the planetary boundary layer. For short-term averaging periods, high concentrations between prognostic data and AERMET fell within 20% for elevated sources. The worst short-term comparisons were for the 1-hour averages of the surface-based volume source where AERMET's routines potentially underestimated the mixing height causing a gap of 26 μg/m~3 between the 1st and 2nd high values. The MMIF data set extracted more detailed meteorological profiles and used a lower bound mixing height which resulted in small differences (5-10 μg/m~3) between source highs. Conversely, long-term averages presented some significant dissimilarities between the data sets. MM5 produced lower concentrations from elevated sources but had increases of 35-43% over AERMET when comparing the volume source results. WRF, on the other hand, trended higher than AERMET when comparing point source output, and produced the only long-term case where the volume source concentration was lower than that estimated by AERMET.
机译：预后气象模型使用高级算法来估计否则无法获得观测数据的点处的气象变量。这为找到用于空气质量扩散模型分析的代表性气象数据的问题提供了一种可能的解决方案。 2012年，美国环境保护署（U.S. EPA）发布了称为Mesoscale模型接口程序（MMIF）的新实用程序的beta版。 MMIF“将预测的气象模型输出字段...从第五代中尺度模型（MM5）和天气研究与预报（WRF）模型转换为与三种空气质量模型兼容的直接输入格式：AERMOD，CALPUFF和SCICHEM。 AERMOD建模系统通常利用称为AERMET的气象预处理器，从每小时的地面观测和每天两次的高空测深估算出行星边界层的深度。 MMIF可以通过生成模型就绪的气象数据文件来完全替代AERMET，也可以为AERMET创建直接输入文件。这项研究比较了使用国家气象局数据与使用MMIF从MM5和WRF提取的“伪站”数据运行的单个AERMET运行（日期为11059）的气象参数的差异。还审查了对这些气象数据集由AERMOD（日期为12060）计算出的对地面浓度的影响。气象数据文件的比较显示，AERMET输出的峰值平均模型混合高度比MMIF格式化的预测数据低4-16％。但是，在AERMET数据集中，平均夜间混合高度比预后模型高1.75-2.15倍。 MM5和WRF数据的风速平均比国家气象局数据低2-3 m / s，而预后数据的峰值速度低5-6 m / s。 AERMOD的结果反映了这些气象差异，并突出了预测模型处理行星边界层的方式上的一些重大差异。对于短期平均期间，对于升高的来源，预后数据和AERMET之间的高浓度下降在20％以内。最差的短期比较是基于表面的体积源的1小时平均值，其中AERMET的程序可能低估了混合高度，从而导致第一高值和第二高值之间存在26μg/ m〜3的差距。 MMIF数据集提取了更详细的气象资料，并使用了较低的混合高度，这导致源高之间的微小差异（5-10μg/ m〜3）。相反，长期平均值在数据集之间存在一些明显的差异。 MM5从升高的源中产生较低的浓度，但在比较体积源结果时，其浓度比AERMET高出35-43％。另一方面，在比较点源输出时，WRF趋向于高于AERMET，并且是唯一的长期情况，即体积源浓度低于AERMET估计的体积源浓度。

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