Evaluation of the GEM-AQ air quality model during the Québec smoke event of 2002: Analysis of extensive and intensive optical disparities
Evaluation of the GEM-AQ air quality model during the Québec smoke event of 2002: Analysis of extensive and intensive optical disparities
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2002 年魁北克烟雾事件期间 GEM-AQ 空气质量模型的评估：广泛和密集的光学差异分析
DOI：
10.1016/j.atmosenv.2006.03.006
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发表时间：
2006
影响因子：
5
通讯作者：
J. McConnell
中科院分区：
文献类型：
--
作者：
N. O'Neill;M. Campanelli;A. Lupu;S. Thulasiraman;J. Reid;M. Aubé;L. Neary;J. Kaminski;J. McConnell
使用2002年7月魁北克烟雾事件的数据，研究了加拿大空气质量模式GEM-AQ与密集和广泛光学变量（气溶胶光学厚度或AOD和Lngström指数或α）测量值之间的均方根（rms）差异。为了量化模型和测量值之间的区域差异，我们采用了均方根差异的三分量分析。AOD的两个绝对幅度均方根分量的行为（均值差和标准偏差差）使我们能够推断出发射特性，否则这些特性将被较大的“反相关”分量所掩盖。我们发现，推断的排放通量显着高于原来的地球静止，卫星衍生的FLAMBESTA（火灾定位和燃烧排放建模）的排放通量估计作为输入的模拟。该模型捕捉到了密集的α指数的区域性下降（颗粒尺寸随轨迹时间的增加），而与广泛的AOD参数的协议是边际的，但显然取决于用于表征模型性能的时空统计工具的性质。在建立α与轨迹时间的趋势时，以与测量数据相同的方式过滤模拟AOD数据（消除非常大的AOD）。为了使α结果与测量结果具有可比性，认为有必要对建模结果进行这种处理;在后一种情况下，很难（如果不是不可能的话）区分由于仪器伪影（低信号强度下的非线性）导致的测量α趋势与由于凝血效应导致的趋势。
The root-mean-square (rms) differences between the Canadian air quality model GEM-AQ and measurements for intensive and extensive optical variables (aerosol optical depth or AOD and Ångström exponent or α) were investigated using data from the July 2002 Québec smoke event. In order to quantify regional differences between model and measurements we employed a three component analysis of rms differences. The behaviour of the two absolute amplitude rms components of AOD (difference of the means and the difference of the standard deviations) enabled us to infer emission properties which would otherwise have been masked by the larger ‘anti-correlation’ component. We found the inferred emission fluxes to be significantly higher than the original geostationary, satellite-derived FLAMBÉ (fire locating and modelling of burning emissions) emissions flux estimates employed as inputs to the simulations. The model captured the regional decrease of the intensive α exponent (increase of particle size with trajectory time), while the agreement with the extensive AOD parameter was marginal but clearly dependent on the nature of the spatio-temporal statistical tools employed to characterize model performance. In establishing the α versus trajectory time trend, the modelled AOD data was filtered in the same way as the measured data (very large AODs are eliminated). This processing of modelled results was deemed necessary in order to render the α results comparable with the measurements; in the latter case it was difficult, if not impossible, to discriminate between measured α trends due to instrumental artifacts (non-linearities at low signal strength) versus trends due to coagulative effects.
影响因子：
11.4
作者：
Sapkota, A;Symons, JM;Buckley, TJ
通讯作者：
Buckley, TJ