Park Hyungwon John, Sherman Thomas, Freire Livia S, Wang Guiquan, Bolster Diogo, Xian Peng, Sorooshian Armin, Reid Jeffrey S, Richter David H
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, USA.
FTS International, LLC, Dulles, VA, USA.
J Geophys Res Atmos. 2020 Oct 16;125(19). doi: 10.1029/2020jd032731. Epub 2020 Sep 27.
In an effort to better represent aerosol transport in mesoscale and global-scale models, large eddy simulations (LES) from the National Center for Atmospheric Research (NCAR) Turbulence with Particles (NTLP) code are used to develop a Markov chain random walk model that predicts aerosol particle profiles in a cloud-free marine atmospheric boundary layer (MABL). The evolution of vertical concentration profiles are simulated for a range of aerosol particle sizes and in a neutral and an unstable boundary layer. For the neutral boundary layer we find, based on the LES statistics and a specific model time step, that there exist significant correlation for particle positions, meaning that particles near the bottom of the boundary are more likely to remain near the bottom of the boundary layer than being abruptly transported to the top, and vice versa. For the unstable boundary layer, a similar time interval exhibits a weaker tendency for an aerosol particle to remain close to its current location compared to the neutral case due to the strong nonlocal convective motions. In the limit of a large time interval, particles have been mixed throughout the MABL and virtually no temporal correlation exists. We leverage this information to parameterize a Markov chain random walk model that accurately predicts the evolution of vertical concentration profiles. The new methodology has significant potential to be applied at the subgrid level for coarser-scale weather and climate models, the utility of which is shown by comparison to airborne field data and global aerosol models.
为了在中尺度和全球尺度模型中更好地描述气溶胶传输，美国国家大气研究中心（NCAR）的含粒子大涡模拟（LES）代码中的湍流与粒子（NTLP）代码被用于开发一个马尔可夫链随机游走模型，该模型可预测无云海洋大气边界层（MABL）中的气溶胶粒子分布。针对一系列气溶胶粒径以及中性和不稳定边界层，模拟了垂直浓度分布的演变。对于中性边界层，基于LES统计数据和特定的模型时间步长，我们发现粒子位置存在显著相关性，这意味着边界层底部附近的粒子更有可能停留在边界层底部附近，而不是突然被传输到顶部，反之亦然。对于不稳定边界层，由于强烈的非局部对流运动，与中性情况相比，在类似的时间间隔内，气溶胶粒子保持在其当前位置附近的趋势较弱。在长时间间隔的极限情况下，粒子已在整个MABL中混合，几乎不存在时间相关性。我们利用这些信息来参数化一个马尔可夫链随机游走模型，该模型能够准确预测垂直浓度分布的演变。这种新方法在粗尺度天气和气候模型的亚网格级别上具有显著的应用潜力，通过与机载实地数据和全球气溶胶模型的比较展示了其效用。
