Traversing the Gray Zone with Scale-aware Turbulence Closures

随着计算能力的进步，用于天气预报的大气模型的分辨率越来越高。天气预报模型最初设计的栅格分辨率约为10公里。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”，在那里传统的物理参数化不再适用，导致天气预报出现错误。该项目将创建一个新的尺度感知框架，以改进灰色地带的这些参数化，对天气和区域气候预报产生广泛影响，这些预报越来越多地在实际应用中使用灰色地带的模型。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经过彻底测试，并准备在操作代码中实施。最终，这将进一步实现灰色地带和更精细分辨率的大量应用，如天气预报、风能预报、野火烟雾建模和城市环境中的空气质量建模。该项目还将与加利福尼亚州斯托克顿的环境正义社区建立联系，那里的两名教师将获得夏季研究机会，开发一套动手操作的空气污染课程，该课程将在头两年惠及700名高中生。本研究的总体目标是将尺度感知湍流参数化扩展到灰色区域，以便随着网格分辨率的增加，湍流和已解决运动的一致表示。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封闭模型进行测试，重点是动态重建模型（DRM）和更简单的“混合模型”。混合模型将尺度相似分量与涡流黏度模型相结合；混合模式具有尺度意识，并且已被证明可以使中尺度模式更好地在灰色地带运行。该项目将扩展并彻底评估灰色地带的混合模型。网格嵌套的新策略也将被测试，包括调整网格嵌套比例和使用快速GPU代码与非常大的域来探索跳过灰色区域。模拟将在南部大平原进行，这是一个风力资源感兴趣的地区，在北加州进行，包括空气质量差的内陆地区和考虑开发风能的海上地区。将进行长期模拟以评估模型技能的提高。该项目还将采取初步措施，将这些湍流关闭创新纳入业务预报。该奖项反映了美国国家科学基金会的法定使命，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。

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.