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Data for Figures and Tables in Journal Article "Assessment of the Effects of Horizontal Grid Resolution on Long-Term Air Quality Trends using Coupled WRF-CMAQ Simulations", doi:10.1016/j.atmosenv.2016.02.036

Published by U.S. EPA Office of Research and Development (ORD) | U.S. Environmental Protection Agency | Metadata Last Checked: June 27, 2025 | Last Modified: 2016-02-08
The dataset represents the data depicted in the Figures and Tables of a Journal Manuscript with the following abstract: "The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. WRF-CMAQ simulations over the continental United State are performed over the 2001 to 2010 time period at two different horizontal resolutions of 12 and 36 km. Both simulations used the same emission inventory and model configurations. Model results are compared both in space and time to assess the potential weaknesses and strengths of using coarse resolution in long-term air quality applications. The results show that the 36 km and 12 km simulations are comparable in terms of trends analysis for both pollutant concentrations and radiation variables. The advantage of using the coarser 36 km resolution is a significant reduction of computational cost, time and storage requirement which are key considerations when performing multiple years of simulations for trend analysis. However, if such simulations are to be used for local air quality analysis, finer horizontal resolution may be beneficial since it can provide information on local gradients. In particular, divergences between the two simulations are noticeable in urban, complex terrain and coastal regions.". This dataset is associated with the following publication: Gan , M., C. Hogrefe , R. Mathur , J. Pleim , J. Xing , D. Wong , R. Gilliam , G. Pouliot , and C. Wei. Assessment of the effects of horizontal grid resolution on long-term air quality trends using coupled WRF-CMAQ simulations. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 132: 207-216, (2016).

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