确定海洋雾预报的重要微物理特性和过程

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Monthly Weather Review Pub Date : 2023-09-01 DOI:10.1175/mwr-d-22-0294.1
Nathan Hexum Pope, A. Igel
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引用次数: 0

摘要

在本研究中,使用2018年9月7日0000至1800 UTC期间发生在加拿大大浅滩附近的海洋雾事件来研究模拟雾特性对主要在微物理方案中发现的六个模型参数的敏感性。为此,我们使用区域大气模拟系统(RAMS)进行了一套大型区域模拟,跨越了雾事件的生命周期。我们随机选择模拟套件的参数组合,并使用高斯过程回归来模拟各种模拟雾属性对参数的响应。研究发现,控制雾滴粒径分布相对宽度的微物理形状参数和气溶胶数量浓度对雾的空间范围、持续时间和地面能见度的影响最大。一般来说,降低水滴平均下降速度和/或抑制毛毛雨形成的参数会导致雾中的能见度降低,但也会延迟开始,缩短寿命,缩小空间范围。分布宽度的重要性表明,需要更好地表征雾滴分布的这一性质,并在微物理方案中更好地处理这一性质。
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Identifying Important Microphysical Properties and Processes for Marine Fog Forecasts
In this study, a marine fog event that occurred from 0000 to 1800 UTC 7 September 2018 near Canada’s Grand Banks is used to investigate the sensitivity of simulated fog properties to six model parameters found primarily in the microphysics scheme. To do so, we ran a large suite of regional simulations that spanned the life cycle of the fog event using the Regional Atmospheric Modeling System (RAMS). We randomly selected parameter combinations for the simulation suite and used Gaussian process regression to emulate the response of a variety of simulated fog properties to the parameters. We find that the microphysics shape parameter, which controls the relative width of the droplet size distribution, and the aerosol number concentration have the greatest impact on fog in terms of spatial extent, duration, and surface visibility. In general, parameters that reduce mean fall speed of droplets and/or suppress drizzle formation lead to reduced visibility in fog but also delayed onset, shorter lifetimes, and reduced spatial extent. The importance of the distribution width suggests a need for better characterization of this property for fog droplet distributions and better treatment of this property in microphysics schemes.
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来源期刊
Monthly Weather Review
Monthly Weather Review 地学-气象与大气科学
CiteScore
6.40
自引率
12.50%
发文量
186
审稿时长
3-6 weeks
期刊介绍: Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.
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