气候变化领域集成服务门户

The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Principal Component Analysis (PCA) with the intent of motivating its use by the evaluation community. One of the main objectives of PCA is to identify, through data reduction, the recurring and independent modes of variations (or signals) within a very large dataset, thereby summarizing the essential information of that dataset so that meaningful and descriptive conclusions can be made. In this demonstration, PCA is applied to a simple evaluation metric - the model bias associated with EPA's Community Multi-scale Air Quality (CMAQ) model when compared to weekly observations of sulfate (SO42-) and ammonium (NH4+) ambient air concentrations measured by the Clean Air Status and Trends Network (CASTNet). The advantages of using this technique are demonstrated as it identifies strong and systematic patterns of CMAQ model bias across a myriad of spatial and temporal scales that are neither constrained to geopolitical boundaries nor monthly/seasonal time periods (a limitation of many current studies). The technique also identifies locations (station-grid cell pairs) that are used as indicators for a more thorough diagnostic evaluation thereby hastening and facilitating understanding of the probable mechanisms responsible for the unique behavior among bias regimes. A sampling of results indicates that biases are still prevalent in both SO42- and NH4+ simulations that can be attributed to either: 1) cloud processes in the meteorological model utilized by CMAQ, which are found to overestimated convective clouds and precipitation, while underestimating larger-scale resolved clouds that are less likely to precipitate, and 2) biases associated with Midwest NH3 emissions which may be partially ameliorated using the bi-directional NH3 exchange option in CMAQ. (C) 2013 Published by Elsevier Ltd.