Collaborative Research: Towards Better Understanding of the Climate System Using a Global Storm-Resolving Model
合作研究:利用全球风暴解决模型更好地了解气候系统
基本信息
- 批准号:2218829
- 负责人:
- 金额:$ 35.3万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-08-15 至 2025-07-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Weather phenomena come in all spatial scales, from the turbulent up-and-down motions within clouds to frontal systems that span a time zone to the jet streams that circle the globe. Naturally different models are used to capture phenomena at different scales, including Large Eddy Simulation (LES) models with grid spacings of perhaps 10m used to simulate individual clouds or small cloud clusters. LES models are typically applied on a limited domain, perhaps 10km to 100km wide, and the influence of larger scales of motion on the clouds is represented by imposing domain-wide conditions, for instance a single vertical profile of temperature and moisture for the whole domain. The drawback of such simulations is that they fail to capture two-way interactions between small and large scales, for instance the effect of small clouds on the large-scale temperature and moisture profiles. Models that can capture a larger range of scales would thus be quite valuable.One model which has proved quite useful for this purpose is the System for Atmospheric Modeling (SAM), developed by the Principal Investigator (PI) in the early 2000s. SAM has been used as an LES model, for instance in simulations of flow around a building at 1m resolution, but has also been used with grid spacings around 5km to simulate wave motions in a channel domain spanning the tropics. SAM has been a workhorse model for studies of cloud behaviors including the aggregation of convective clouds and the response of clouds to greenhouse gas-induced warming, in particular the extent to which the cloud response intensifies or counteracts the warming.Recently the PI developed a global version of SAM called gSAM, which extends the Cartesian coordinates to spherical coordinates and makes other modifications to represent flow on a global domain. The model inherits all of the features of SAM and also adds an immersed step topography, an advance over previous versions which were more idealized and assumed a flat surface. Another way in which gSAM adds realism is the ability to run simulations starting from observational initial conditions, allowing short-term "forecasts", also called hindcasts, of real-world weather system evolution. A recent study used this feature, along with "nudging" to reanalysis data, to simulate conditions observed during the SOCRATES field campaign (see AGS-16628674). The study concluded that the formation of cloud ice particles from the shattering of earlier ice particles plays a role in determining the width of clouds, thus regulating the amount of sunlight that reaches the surface of the Southern Ocean.The goal of this award is to further develop gSAM and make it available to the worldwide research community as a resource for weather and climate research. The work includes tasks devoted to improving model behavior near the poles, improving the accuracy and efficiency of radiative transfer calculations using machine learning techniques, improving input/ouput performance, and validating simulations against satellite data. Additional resources are developed to facilitate use and adoption of the model, including a full suite of documentation and tutorials, initial and boundary condition datasets for multiple configurations and resolutions, and model output for several six-month simulations. The model is maintained on GitHub and users can contribute to code development using GitHub repositories. The PI also maintains a model website that tracks publications using the model and provides additional information and resources. Since gSAM is an extension of SAM it is easily configured to run as a limited-domain LES model, thereby continuing to serve the SAM user community.The work has broader impacts due to the power of gSAM as a tool for conducting basic science research on a wide range of topics. One area of particular interest is the interaction between clouds and climate change, as the sensitivies of clouds to a warming climate could affect the amount of warming that occurs. gSAM can also contribute to our understanding of how the intensity of extreme precipitation events is likely to change in a warming world. In both cases gSAM serves to lower the barriers between the research communities studying climate processes on the global scale and cloud properties on the local scale. The project also supports a graduate student, thereby building the next generation scientific workforce.This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support Artificial Intelligence/Machine Learning and open science activities in the geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
天气现象存在于所有的空间尺度,从云内的湍流上下运动到跨越一个时区的锋面系统,再到环绕地球仪的急流。自然地,不同的模型被用来捕捉不同尺度的现象,包括网格间距约为10米的大涡模拟(LES)模型,用于模拟单个云或小云团。 LES模式通常应用于有限的区域,可能是10公里到100公里宽,较大尺度的运动对云的影响通过施加全域条件来表示,例如整个区域的温度和湿度的单一垂直廓线。 这种模拟的缺点是,它们无法捕捉到小尺度和大尺度之间的双向相互作用,例如小云层对大尺度温度和湿度廓线的影响。 因此,能够捕捉更大尺度范围的模型将是非常有价值的。其中一个被证明非常有用的模型是由首席研究员(PI)在21世纪初开发的大气模拟系统(SAM)。 SAM已被用作大涡模拟模式,例如,在1米分辨率下模拟建筑物周围的气流,但也被用于网格间距约为5公里的模拟跨越热带的通道域中的波动。 SAM一直是研究云行为的主力模式,包括对流云的聚集和云对温室气体引起的变暖的响应,特别是云响应加剧或抵消变暖的程度。最近,PI开发了一个全球版本的SAM,称为gSAM,它将笛卡尔坐标扩展到球坐标,并进行其他修改,以表示全球域上的流。 该模型继承了SAM的所有功能,并增加了一个浸入式台阶地形,比以前的版本更理想化,假设一个平坦的表面。 gSAM增加真实性的另一种方式是能够从观测的初始条件开始运行模拟,允许对真实世界的天气系统演变进行短期“预报”,也称为后报。 最近的一项研究使用了这一特征,沿着“轻推”再分析数据,以模拟SOCRATES现场活动期间观察到的条件(见AGS-16628674)。 该研究的结论是,早期冰粒破碎形成的云冰粒在确定云的宽度方面发挥着作用,从而调节到达南大洋表面的阳光量。该奖项的目标是进一步开发gSAM,并将其作为天气和气候研究的资源提供给全球研究界。 这项工作包括致力于改善两极附近的模型行为,使用机器学习技术提高辐射传输计算的准确性和效率,提高输入/输出性能,并根据卫星数据验证模拟。开发了其他资源以促进模型的使用和采用,包括全套文档和教程,多种配置和分辨率的初始和边界条件数据集,以及几个六个月模拟的模型输出。 该模型在GitHub上维护,用户可以使用GitHub存储库为代码开发做出贡献。 PI还维护一个使用该模型跟踪出版物的模型网站,并提供更多信息和资源。 由于gSAM是SAM的扩展,它可以很容易地配置为作为有限域LES模型运行,从而继续为SAM用户社区服务。由于gSAM作为一种工具,可以在广泛的主题上进行基础科学研究,因此这项工作具有更广泛的影响。特别感兴趣的一个领域是云和气候变化之间的相互作用,因为云对气候变暖的敏感性可能会影响发生的变暖量。gSAM还有助于我们了解极端降水事件的强度在变暖的世界中可能如何变化。 在这两种情况下,gSAM都有助于降低研究团体在全球范围内研究气候过程和在当地范围内研究云特性之间的障碍。 该项目还支持一名研究生,该项目由地球科学理事会和高级网络基础设施办公室合作共同资助,以支持人工智能/地球科学中的机器学习和开放科学活动。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Peter Blossey其他文献
Peter Blossey的其他文献
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{{ truncateString('Peter Blossey', 18)}}的其他基金
Collaborative Research: EUREC4A-iso--Constraining the Interplay between Clouds, Convection, and Circulation with Stable Isotopologues of Water Vapor
合作研究:EUREC4A-iso——用水蒸气的稳定同位素体约束云、对流和环流之间的相互作用
- 批准号:
1938108 - 财政年份:2019
- 资助金额:
$ 35.3万 - 项目类别:
Continuing Grant
Collaborative Research: Advancing Understanding of Aerosol-Cloud Feedback Using the World's First Global Climate Model with Explicit Boundary Layer Turbulence
合作研究:利用世界上第一个具有明确边界层湍流的全球气候模型增进对气溶胶云反馈的理解
- 批准号:
1912130 - 财政年份:2019
- 资助金额:
$ 35.3万 - 项目类别:
Standard Grant
Collaborative Research: Isotopic Fractionation in Snow (IFRACS)
合作研究:雪中同位素分馏 (IFRACS)
- 批准号:
1260368 - 财政年份:2013
- 资助金额:
$ 35.3万 - 项目类别:
Continuing Grant
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