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AMPlifying the Simulation of Clouds and Precipitation

增强云和降水的模拟

基本信息

批准号：
2025103
负责人：
Adele Igel
金额：
$ 36.94万
依托单位：
University of California-Davis
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2025103&HistoricalAwards=false
关键词：
AMPlifying Simulation Clouds Precipitation

项目摘要

Properly simulating clouds is critical for accurate prediction of clouds and rainfall in weather and climate models. There are two common ways in which small-scale cloud processes are represented in atmospheric models; these are known as bin microphysics schemes and bulk microphysics schemes. Bulk schemes are simple models that execute quickly while bin schemes are much more complex but run slowly – in fact, too slowly to be used in weather forecasting or climate prediction. This project will use a new model, called the Arbitrary Moment Predictor (AMP), which benefits from the accuracy and flexibility of bin schemes to improve bulk schemes. AMP acts as a lens. It allows a weather model to see a bin scheme as though it were a bulk scheme. By changing the view, it will become possible to understand how best to implement bulk schemes in models for the first time. The results have the potential to lead to a major shift in the design and development of future weather and climate models.AMP predicts size distribution moments of cloud droplets and rain drops like existing bulk schemes, but the process parameterizations are identical to those used by a bin scheme. It is flexible in that it can predict mass mixing ratio and one or two additional arbitrary distribution moments. There will be two major parts of the work. 1) Large eddy simulations of warm phase stratocumulus clouds with two bin schemes and with two versions of AMP corresponding to each bin scheme will be compared to assess fundamental limitations of bulk schemes. Such a direct comparison between bin and bulk schemes has not been possible before. 2) Additional simulations will be run using AMP with alternative combinations of predicted moments. Simulations will be rigorously assessed with available observations to explore the possibility that nonstandard moment combinations should be used to improve simulation of clouds and precipitation. Such a possibility has been suggested based on observational evidence but has not been tested in 3D simulations.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.

正确模拟云对于天气和气候模式中云和降雨的准确预测至关重要。在大气模式中，小尺度云过程有两种常见的表示方法，它们被称为箱微物理方案和体微物理方案。批量方案是简单的模型，可以快速执行，而箱方案要复杂得多，但运行缓慢-事实上，太慢而不能用于天气预报或气候预测。该项目将使用一种称为任意矩预测器（AMP）的新模型，该模型受益于bin方案的准确性和灵活性，以改善bulk方案。 AMP充当透镜。它允许天气模型看到一个bin方案，就好像它是一个bulk方案一样。通过改变视图，将有可能首次了解如何最好地在模型中实现批量方案。结果有可能导致未来天气和气候模式的设计和发展的重大转变。AMP预测云滴和雨滴的大小分布矩像现有的散装方案，但过程参数化是相同的bin方案所使用的。它的灵活性在于，它可以预测质量混合比和一个或两个额外的任意分布的时刻。这项工作将有两个主要部分。1)暖相层积云的大涡模拟与两个箱方案和两个版本的AMP对应于每个箱方案将进行比较，以评估散装方案的基本局限性。这种直接比较箱和散装方案之前是不可能的。2)将使用AMP与预测矩的替代组合运行其他模拟。模拟将严格评估与现有的观测，以探讨非标准的时刻组合应被用来改善云和降水的模拟的可能性。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。