Hydro-ABC模型(2.0版):一个简化的对流尺度湿动力学模型

IF 4 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Jiangshan Zhu, Ross Noel Bannister
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引用次数: 0

摘要

摘要对流的预测(在位置、时间和强度方面)对于实现高分辨率天气预报非常重要。这个问题不仅需要良好的对流尺度模型,还需要数据同化系统,该系统提供的初始条件在随后的预报中既不会不当阻碍对流,也不会不当加速对流。使用操作尺度的数值天气预报系统来解决这个问题既困难又昂贵,因此正在开发一种简化的对流尺度流动模型(称为“ABC模型”)。本文扩展了现有的干对流尺度流动ABC模型,加入了水的蒸汽相和凝结水相的混合比。修正后的模型被称为“Hydro-ABC”。Hydro-ABC包括在动力核心内的蒸汽和冷凝物混合比的输运,并通过微物理方案在这两个阶段之间转换。饱和混合比由模型量导出,它有助于确定是否发生蒸发或冷凝。潜热与浮力变量(ABC的类潜在温度变量)交换,以保存总能量,其中总能量是干能和潜热的总和。模型方程的设计是为了保存域的总质量、水和能量。最后给出了一个数值模型积分的实例,分析了在较宽的频率范围内形成的逼真的砧云,以及惯性-重力模式和声学模式的激发。这种行为意味着Hydro-ABC是一个足够具有挑战性的模型,可以在未来的工作中进行创新数据同化策略的实验。为了研究可能的预测误差协方差统计(这是数据同化所必需的知识),进行了Hydro-ABC积分的集成。这些显示了依赖于对流活动存在的模式(在任何模型的垂直柱上),从而提供了依赖于流的误差统计。还评估了对流的候选指标/前兆(即相对湿度、流体静力不平衡、水平辐散、对流有效势能、对流抑制、垂直风和凝结水混合比),其中一些指标似乎是对流存在的可靠诊断。这些诊断将有助于在开发Hydro-ABC数据同化时选择相关的预测误差协方差统计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics
Abstract. The prediction of convection (in terms of position, timing, and strength) is important to achieve for high-resolution weather forecasting. This problem requires not only good convective-scale models, but also data assimilation systems that give initial conditions which neither improperly hinder nor improperly hasten convection in the ensuing forecasts. Solving this problem is difficult and expensive using operational-scale numerical weather prediction systems, and so a simplified model of convective-scale flow is under development (called the “ABC model”). This paper extends the existing ABC model of dry convective-scale flow to include mixing ratios of vapour and condensate phases of water. The revised model is called “Hydro-ABC”. Hydro-ABC includes transport of the vapour and condensate mixing ratios within a dynamical core, and it transitions between these two phases via a micro-physics scheme. A saturated mixing ratio is derived from model quantities, which helps determine whether evaporation or condensation happens. Latent heat is exchanged with the buoyancy variable (ABC's potential-temperature-like variable) in such a way to conserve total energy, where total energy is the sum of dry energy and latent heat. The model equations are designed to conserve the domain-total mass, water, and energy. An example numerical model integration is performed and analysed, which shows the development of a realistic looking anvil cloud and excitation of inertio-gravity and acoustic modes over a wide range of frequencies. This behaviour means that Hydro-ABC is a sufficiently challenging model which will allow experimentation with innovative data assimilation strategies in future work. An ensemble of Hydro-ABC integrations is performed in order to study the possible forecast error covariance statistics (knowledge of which is necessary for data assimilation). These show patterns that are dependent on the presence of convective activity (at any model's vertical column), thus giving a taste of flow-dependent error statistics. Candidate indicators/harbingers of convection are also evaluated (namely relative humidity, hydrostatic imbalance, horizontal divergence, convective available potential energy, convective inhibition, vertical wind, and the condensate mixing ratio), some of which appear to be reliable diagnostics concerning the presence of convection. These diagnostics will be useful in the selection of the relevant forecast error covariance statistics when data assimilation for Hydro-ABC is developed.
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来源期刊
Geoscientific Model Development
Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
8.60
自引率
9.80%
发文量
352
审稿时长
6-12 weeks
期刊介绍: Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.
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