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Model Dev."],"abstract":"<jats:p>\n                    This study assesses the impact of the lightning nitric oxide (LNO) production schemes\nin the Community Multiscale Air Quality (CMAQ) model on ground-level air\nquality as well as aloft atmospheric chemistry through detailed evaluation\nof model predictions of nitrogen oxides (NO\n                    <jats:sub>\n                      <jats:italic>x<\/jats:italic>\n                    <\/jats:sub>\n                    ) and ozone (O\n                    <jats:sub>3<\/jats:sub>\n                    ) with\ncorresponding observations for the US. For ground-level evaluations, hourly\nO\n                    <jats:sub>3<\/jats:sub>\n                    and NO\n                    <jats:sub>\n                      <jats:italic>x<\/jats:italic>\n                    <\/jats:sub>\n                    values from the U.S. EPA Air Quality System (AQS) monitoring network are used to\nassess the impact of different LNO schemes on model prediction of these\nspecies in time and space. Vertical evaluations are performed using\nozonesonde and P-3B aircraft measurements during the Deriving\nInformation on Surface Conditions from Column and Vertically Resolved\nObservations Relevant to Air Quality (DISCOVER-AQ) campaign\nconducted in the Baltimore\u2013Washington region during July\u00a02011. The impact on\nwet deposition of nitrate is assessed using measurements from the National\nAtmospheric Deposition Program's National Trends Network (NADP NTN).\nCompared with the Base model (without LNO), the impact of LNO on surface\nO\n                    <jats:sub>3<\/jats:sub>\n                    varies from region to region depending on the Base model conditions.\nOverall statistics suggest that for regions where surface O\n                    <jats:sub>3<\/jats:sub>\n                    mixing\nratios are already overestimated, the incorporation of additional NO from\nlightning generally increased model overestimation of mean daily maximum\n8\u2009h (DM8HR) O\n                    <jats:sub>3<\/jats:sub>\n                    by 1\u20132\u2009ppb. In regions where surface O\n                    <jats:sub>3<\/jats:sub>\n                    is\nunderestimated by the Base model, LNO can significantly reduce the\nunderestimation and bring model predictions close to observations. Analysis\nof vertical profiles reveals that LNO can significantly improve the vertical\nstructure of modeled O\n                    <jats:sub>3<\/jats:sub>\n                    distributions by reducing underestimation\naloft and to a lesser degree decreasing overestimation near the surface.\nSince the Base model underestimates the wet deposition of nitrate in most\nregions across the modeling domain with the exception of the Pacific Coast, the inclusion\nof LNO leads to reduction in biases and errors and an increase in\ncorrelation coefficients at almost all the NADP NTN sites. Among the three\nLNO schemes described in Kang et al.\u00a0(2019), the hNLDN scheme, which is\nimplemented using hourly observed lightning flash data from National\nLightning Detection Network (NLDN), performs best for comparisons with ground-level values, vertical profiles, and wet deposition of nitrate; the mNLDN scheme (the monthly\nNLDN-based scheme) performed slightly better. However, when observed\nlightning flash data are not available, the linear regression-based\nparameterization scheme, pNLDN, provides an improved estimate for nitrate\nwet deposition compared to the base simulation that does not include\u00a0LNO.\n                  <\/jats:p>","DOI":"10.5194\/gmd-12-4409-2019","type":"journal-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T02:35:07Z","timestamp":1571625307000},"page":"4409-4424","source":"Crossref","is-referenced-by-count":30,"title":["Simulating lightning NO production in CMAQv5.2:  performance evaluations"],"prefix":"10.5194","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4952-1021","authenticated-orcid":false,"given":"Daiwen","family":"Kang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"},{"role":"corresponding-author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3732-6074","authenticated-orcid":false,"given":"Kristen M.","family":"Foley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8927-5876","authenticated-orcid":false,"given":"Rohit","family":"Mathur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shawn J.","family":"Roselle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth E.","family":"Pickering","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3305-9669","authenticated-orcid":false,"given":"Dale J.","family":"Allen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3145","published-online":{"date-parts":[[2019,10,21]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"Allen, D. 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