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Simulating Lightning NO Production in CMAQv5.2: Performance Evaluations
Citation:
Kang, D., K. Foley, R. Mathur, S. Roselle, K. Pickering, AND D. Allen. Simulating Lightning NO Production in CMAQv5.2: Performance Evaluations. Geoscientific Model Development . Copernicus Publications, Katlenburg-Lindau, Germany, 12(10):4409–4424, (2019). https://doi.org/10.5194/gmd-12-4409-2019
Impact/Purpose:
In order to develop efficient emission control strategies in State Implementation Plans that seek to attain National Ambient Air Quality Standards (NAAQS) for ozone and fine particulate matter (PM2.5) under the Clean Air Act, all sources of airborne precursors -- anthropogenic and natural -- should be considered. Nitric oxides (NOx) generated by lightning can contribute approximately 10% of the total NOx burden across the U.S. This manuscript provides a comprehensive evaluation for the impact of the lightning NOx production schemes implemented in CMAQv5.2 on air quality modeling.
Description:
This study assesses the impact of the lightning NOX (LNOX) production schemes in the CMAQ model (Kang et al., 2019) on ground-level air quality as well as aloft atmospheric chemistry through detailed evaluation of model predictions of nitrogen oxides (NOx) and ozone (O3) with corresponding observations for the U.S. For ground-level evaluations, hourly O3 and NOx from the US EPA’s AQS monitoring network are used to assess the impact of different LNOx schemes on model prediction of these species in time and space. Vertical evaluations are performed using ozonesonde and P-3B aircraft measurements during the DISCOVER-AQ campaign conducted in the Baltimore/Washington region during July 2011. The impact on wet deposition of nitrate is assessed using measurements from the National Atmospheric Deposition Program’s National Trends Network (NADP/NTN). Compared with the base model (without LNOx), the impact of LNOx on surface O3 varies from region to region depending on the base model conditions. Overall statistics suggest that for regions where surface O3 mixing ratios are already overestimated, the incorporation of additional NOx from lightning generally increased model overestimation of mean daily maximum 8-hr (DM8HR) O3 by 1-2 ppb. In regions where surface O3 is underestimated by the base model, LNOx can significantly reduce the underestimation and bring model predictions close to observations. Analysis of vertical profiles reveals that LNOx can significantly improve the vertical structure of modeled O3 distributions by reducing underestimation aloft, and to a lesser degree decreasing overestimation near the surface. Since the base model underestimates the wet deposition of nitrate in most regions across the modeling domain except the Pacific Coast, the inclusion of LNOx leads to reduction in biases and errors and an increase in correlation coefficients at almost all the NADP/NTN sites. Among the three LNOx schemes described in Kang et al. (2019), the hNLDN scheme, which is implemented using hourly observed lightning flash data from National Lightning Detection Network (NLDN), performs best for the ground-level, vertical profiles, and wet deposition comparisons except that for the accumulated wet deposition of nitrate, the mNLDN scheme (the monthly NLDN-based scheme) performed slightly better. However, when observed lightning flash data are not available, the linear regression-based parameterization scheme, pNLDN, provides an improved estimate for LNOx compared to the base simulation that does not include LNOx.
