Traversing the Gray Zone with Scale-aware Turbulence Closures

通过尺度感知的湍流闭合穿越灰色区域

• 批准号: 2337399
• 负责人: Fotini Chow
• 金额: $ 52.61万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337399&HistoricalAwards=false
• 关键词: Traversing; Gray; Zone; Scale; aware

With advances in computing power, atmospheric models for weather prediction are being used at higher and higher resolutions. Weather prediction models were originally designed to operate at about 10-km grid resolution. At about 1-km grid resolution, atmospheric models enter the so-called “gray zone,” where traditional physics parameterizations no longer hold, leading to errors in weather prediction. This project will create a new scale-aware framework to improve these parameterizations in the gray zone, with broad impacts for weather and regional climate forecasts, which increasingly use models in the gray zone for practical applications. The researchers will collaborate with scientists at NCAR and NOAA to guide this process and to make sure that the scale-aware approach is thoroughly tested and ready to implement in operational codes. Ultimately, this will further enable a vast array of applications at gray-zone and finer resolutions, such as weather prediction, wind energy forecasting, wildfire smoke modeling, and air quality modeling in urban environments. This project will also provide connections with the environmental justice community of concern in Stockton, California, where two teachers will be provided with summer research opportunities to develop a hands-on air pollution curriculum which will reach 700 high school students in the first two years. The overall goal of this research is to extend scale-aware turbulence parameterizations into the gray zone to allow a consistent representation of turbulent and resolved motions as grid resolutions increase. The scale-aware turbulence framework will be tested in the WRF model with a variety of physically and mathematically consistent closure models, with a focus on the dynamic reconstruction model (DRM) and simpler “mixed models”. Mixed models combine a scale-similarity component with an eddy-viscosity model; mixed models are scale aware and have been shown to allow mesoscale models to better operate in the gray zone. This project will extend and thoroughly evaluate mixed models across the gray zone. New strategies for grid nesting will also be tested, including adjusting grid nesting ratios and using the FastEddy GPU code with very large domains to explore skipping the gray zone. Simulations will be performed over the Southern Great Plains, an area of interest for wind resources, and over Northern California, including inland locations with poor air quality and offshore regions being considered for wind energy development. Long-term simulations will be performed to assess improvement in model skill. The project will also take initial steps to include these turbulence closure innovations in operational forecasting.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.

随着计算能力的提高，用于天气预报的大气模式的分辨率越来越高。天气预报模式最初设计为以大约10公里的网格分辨率运行。在大约1公里的网格分辨率下，大气模型进入了所谓的“灰色地带”，传统的物理参数化不再适用，导致天气预测的错误。该项目将创建一个新的尺度感知框架，以改善灰色地带的这些参数化，对天气和区域气候预测产生广泛影响，这些预测越来越多地将灰色地带的模型用于实际应用。研究人员将与NCAR和NOAA的科学家合作，指导这一过程，并确保规模感知方法得到彻底测试，并准备在操作代码中实施。最终，这将进一步实现灰色区域和更精细分辨率的大量应用，例如天气预测，风能预测，野火烟雾建模和城市环境中的空气质量建模。该项目还将与加州的斯托克顿的环境正义社区建立联系，将为两名教师提供暑期研究机会，以制定一个实际操作的空气污染课程，在头两年将有700名高中生参加。本研究的总体目标是扩展尺度感知湍流参数化到灰色区域，以允许一致的湍流和解决的运动表示网格分辨率的增加。尺度感知湍流框架将在WRF模型中使用各种物理和数学上一致的闭合模型进行测试，重点是动态重建模型（DRM）和更简单的“混合模型”。混合模式联合收割机将尺度相似性部分与涡流粘度模式相结合;混合模式具有尺度意识，并已被证明允许中尺度模式更好地在灰色区域中运作。该项目将扩展并彻底评估灰色地带的混合模型。还将测试网格嵌套的新策略，包括调整网格嵌套比率，以及使用具有非常大的域的FastEddy GPU代码来探索跳过灰色区域。模拟将在南部大平原（风能资源感兴趣的地区）和北方加州（包括空气质量差的内陆地区和考虑进行风能开发的近海地区）进行。将进行长期模拟，以评估模型技能的改善。该项目还将采取初步措施，将这些湍流闭合创新纳入业务预报中。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。