Kang Daiwen, Pickering Kenneth E, Allen Dale J, Foley Kristen M, Wong David C, Mathur Rohit, Roselle Shawn J
National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA.
Geosci Model Dev. 2019 Jul 18;12(7):3071-3083. doi: 10.5194/gmd-12-3071-2019.
This work describes the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model. We first document the existing LNO production scheme and vertical distribution algorithm. We then describe updates that were made to the scheme originally based on monthly National Lightning Detection Network (mNLDN) observations. The updated scheme uses hourly NLDN (hNLDN) observations. These NLDN-based schemes are good for retrospective model applications when historical lightning data are available. For applications when observed data are not available (i.e., air quality forecasts and climate studies that assume similar climate conditions), we have developed a scheme that is based on linear and log-linear parameters derived from regression of multiyear historical NLDN (pNLDN) observations and meteorological model simulations. Preliminary assessment for total column LNO production reveals that the mNLDN scheme overestimates LNO by over 40% during summer months compared with the updated hNLDN scheme that reflects the observed lightning activity more faithfully in time and space. The pNLDN performance varies with year, but it generally produced LNO columns that are comparable to hNLDN and mNLDN, and in most cases it outperformed mNLDN. Thus, when no observed lightning data are available, pNLDN can provide reasonable estimates of LNO emissions over time and space for this important natural NO source that influences air quality regulations.
这项工作描述了社区多尺度空气质量（CMAQ）模型中的闪电一氧化氮（LNO）生成方案。我们首先记录了现有的LNO生成方案和垂直分布算法。然后，我们描述了最初基于每月国家闪电探测网络（mNLDN）观测数据对该方案所做的更新。更新后的方案使用每小时的NLDN（hNLDN）观测数据。这些基于NLDN的方案在有历史闪电数据时，适用于回顾性模型应用。对于没有观测数据可用的应用（即空气质量预报和假设类似气候条件的气候研究），我们开发了一种基于从多年历史NLDN（pNLDN）观测数据与气象模型模拟的回归中得出的线性和对数线性参数的方案。对总柱状LNO生成的初步评估表明，与在时间和空间上更忠实地反映观测到的闪电活动的更新后的hNLDN方案相比，mNLDN方案在夏季月份高估LNO超过40%。pNLDN的性能随年份变化，但它通常生成的LNO柱与hNLDN和mNLDN相当，并且在大多数情况下优于mNLDN。因此，当没有观测到的闪电数据可用时，pNLDN可以为这个影响空气质量法规的重要天然NO源在时间和空间上提供合理的LNO排放估计。
