在整体微物理方案中改进暖雨模拟

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Robert Conrick, Clifford F. Mass, Lynn McMurdie
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引用次数: 0

摘要

当前的大量微物理参数化方案低估了暖雨期的降水强度和雨滴大小分布(dsd),特别是沿海地形的逆风。为了帮助解决这一缺陷,本研究引入了一组称为RCON的修改,用于目前在Thompson-Eidhammer微物理参数化方案中使用的液相(暖雨)参数化。RCON引入了几种模式修改,其动机是对bin方案的模拟进行评估,它们共同导致在暖雨期间更准确的降水模拟。其中最显著的变化是:(1)使用了更宽的对数正态形状的云水DSD,而不是Thompson-Eidhammer参数化使用的伽马DSD,以及(2)增强了云到雨的自动转换参数化。在2015-16年冬季奥林匹克山脉实验(OLYMPEX)野外活动期间,对两个暖雨事件和一段较长的时间进行了RCON评估。我们表明,RCON修改比默认的Thompson-Eidhammer配置产生更真实的降水分布和降雨DSDs。在多个月的OLYMPEX期间,降雨率、雨水混合比和雨滴数量浓度相对于Thompson-Eidhammer微物理参数化有所增加,同时液相云中的雨滴直径也在减少。这些变化与模拟暖雨的增加是一致的。最后,对该方案2021年8月至2022年8月的实时评估表明,该方案对西北太平洋沿海地区的降水预测有所改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving simulations of warm rain in a bulk microphysics scheme
Abstract Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson-Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are (1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson-Eidhammer parameterization, and (2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015-16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson-Eidhammer configuration. For the multi-month OLYMPEX period, we show that rain rates, rain water mixing ratios, and rain drop number concentrations were increased relative to the Thompson-Eidhammer microphysical parameterization, while concurrently decreasing rain drop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest.
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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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