کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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4443701 | 1311205 | 2006 | 13 صفحه PDF | دانلود رایگان |
The statistical evaluation of an air quality model is part of a broader process, generally referred to as ‘model assessment’, including sensitivity analysis and other tools. The evaluation process is usually implemented through the comparison of model predicted data with point-wise observations. However, this analysis is based on several (implicit) assumptions which are difficult, if not impossible, to assess: e.g. unbiased observations, measurements errors small enough in comparison to the typical usage of observed data, observations representative of the true area-averaged values within each computational cell, numerical model errors small enough in comparison to mis/un-represented physics/chemistry, and so on.In this work we address the problem of the comparison between point measured data and cell-averaged model values. We present a Bayesian approach for the space-time interpolation of measured data and the prediction of cell-averaged values.We used cell-averaged observations to validate the results from the CAMx air quality model. We found that a relevant fraction of the model bias can be explained by the subgrid spatial variability. This analysis may be important in all cases in which one is interested in a model and/or process comparison exercise.
Journal: Atmospheric Environment - Volume 40, Issue 3, January 2006, Pages 554–566