Predicting Vertical Concentration Profiles in the Marine Atmospheric Boundary Layer With a Markov Chain Random Walk Model

Autor(es)

Park, Hyungwon John ^[1] ; Sherman, Thomas ^{[2, 3]} ; Freire, Livia S. ^[4] ; Wang, Guiquan ^[2] ; Bolster, Diogo ^[2] ; Xian, Peng ^[5] ; Sorooshian, Armin ^[6] ; Reid, Jeffrey S. ^[5] ; Richter, David H. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Notre Dame, Dept Aerosp & Mech Engn, Notre Dame, IN 46556 - USA

^[2] Univ Notre Dame, Dept Civil & Environm Engn & Earth Sci, Notre Dame, IN 46556 - USA

^[3] FTS Int LLC, Dulles, VA - USA

^[4] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos - Brazil

^[5] US Naval Res Lab, Monterey, CA - USA

^[6] Univ Arizona, Dept Chem & Environm Engn, Tucson, AZ - USA

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES; v. 125, n. 19 OCT 16 2020.
• Citações Web of Science: 1

Resumo

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. (AU)

• Processo FAPESP: 18/24284-1 - Estudo sobre as trocas entre a superfície do planeta e a atmosfera utilizando Large-Eddy Simulation
• Beneficiário: Livia Souza Freire Grion
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores