Collaborative Research: HDR: Data-Driven Earth System Modeling

全球天气和气候模型在水平间距约为100公里的计算网格上表示大气层，这些网格可以堆叠成超过1公里厚的层。 这样的网格足以捕捉气旋，锋面和其他大尺度大气现象的动力学，但这些现象严重依赖于空间尺度比网格间距小得多的过程。 小尺度过程必须间接表示，通过参数化方案，估计其对解决大气状态的净影响。 例如，云通常对于网格间距来说太小，但它们对于将水分从海洋表面移动到对流层中部至关重要，因此即使在最大的空间尺度上，云参数化也在确定大气湿度方面发挥着关键作用。 参数化方案本质上是近似的，并且产生逼真模拟的方案的开发是模型开发的核心挑战。 参数化的局限性限制了天气气候模式在科学研究和社会应用中的实用性。大多数参数化方案严重依赖于各种参数，这些参数的值不能先验地确定，而必须通过试验和错误来确定。 这个任务，被称为“调整”，是费力的，因为它是单独执行每个参数化方案，并涉及多个配置中的模型的多个集成。它在利用观测数据方面也效率低下，鉴于从卫星和其他来源获得的大量观测数据，这是令人遗憾的。所得到的参数集可能不是最优的，并且当所有方案在全局模拟中彼此交互时可能产生意想不到的结果。 最后，手动调整不利于不确定性量化，这对于估计未来气候变化预测的不确定性很有价值。该项目的目标是用数据同化、机器学习和使用大涡模拟（LES）模型的精细尺度过程建模的组合来取代临时手动调整。 LES模式的网格间距为几十米，可以显式地模拟由参数化方案表示的云和湍流。 这些成分相结合，以创建一个全球机器学习大气模型（MLAM），其中嵌入在选定的网格列的全球模型的LES模型显式地模拟亚网格尺度的过程，这些过程由其他列中的参数化方案表示。机器学习用于调整方案以模拟LES模拟的行为，使得显式模拟成为参数化的在线基准。 通过这种方式，所有的方案都可以在一个运行的全局模拟中一起进行交互式调整。 来自各种来源的观测数据在模型集成过程中被同化，以提供对参数值的进一步约束，并且参数不确定性的估计作为自动调整的一部分而生成。 在海洋环流模型中实施了类似的调整过程，并将两者结合起来产生机器学习气候模型。 模型调优通常被视为一种必要但平凡的活动，其本身不是一个研究主题。 但是，一个能够从观测和过程模型中学习其参数的模型提供了一条新的前进道路，朝着更好的模型和更好的使用模型的方式。由于天气和气候模型更好的预测和预测的社会价值，这项工作具有更广泛的影响。 这项工作直接解决了决策者用于规划天气和气候影响的预测和预测中的不确定性。 此外，这里开发的建模策略适用于广泛的一类研究领域，这些领域面临着将大规模行为与小规模未解决的过程（例如，将基因型与进化生物学中的表型相关联的问题）相关联的问题。 此外，PI还将建立一个关于数据驱动的地球系统建模的跨学科研究生课程。该计划弥合了环境科学和计算科学之间的差距，目前阻碍了环境建模的进展。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Global weather and climate models represent the atmosphere on computational grids with horizontal spacing of perhaps 100km, stacked in layers which can be over a kilometer thick. Such grids suffice to capture the dynamics of cyclones, fronts, and other large-scale atmospheric phenomena, but these phenomena depend critically on processes with spatial scales much smaller than the grid spacing. The small-scale processes must be represented indirectly, through parameterization schemes which estimate their net impact on the resolved atmospheric state. For example clouds are typically too small for the grid spacing yet they are critical for moving moisture from the ocean surface to the mid-troposphere, thus cloud parameterizations play a key role in determining atmospheric humidity even on the largest spatial scales. Parameterization schemes are inherently approximate, and the development of schemes which produce realistic simulations is a central challenge of model development. Shortcomings in parameterization limit the usefulness of weather and climate models both for scientific research and for societal applications.Most parameterization schemes depend critically on various parameters whose values cannot be determined a priori but must instead be found through trial and error. This task, referred to as "tuning", is laborious as it is performed separately for each parameterization scheme and involves multiple integrations of the model in multiple configurations. It is also inefficient in its use of observations, which is unfortunate given the large amount of observational data available from satellites and other sources. The resulting parameter sets may not be optimal and may produce unexpected results when all the schemes interact with each other in global simulations. Finally, manual tuning is not conducive to uncertainty quantification, which would be valuable for estimating the uncertainty in future climate change projections. The goal of this project is to replace ad hoc manual tuning with a combination of data assimilation, machine learning, and fine-scale process modeling using large eddy simulation (LES) models. LES models have grid spacings of a few tens of meters and can explicitly simulate the clouds and turbulence represented by parameterization schemes. These ingredients are combined to create a global Machine Learning Atmospheric Model (MLAM), in which LES models embedded in selected grid columns of a global model explicitly simulate subgrid-scale processes which are represented by parameterization schemes in the other columns. Machine learning is used to tune the schemes to emulate the behavior of the LES simulations, so that explicit simulations become an online benchmark for parameterization. In this way all the schemes can be tuned together and interactively within a running global simulation. Observational data from a variety of sources is assimilated during the model integration to provide a further constraint on parameter values, and estimates of parameter uncertainty are generated as part of the automated tuning. A similar tuning process is implemented in an ocean general circulation model, and the two are combined to produce a machine learning climate model. Model tuning is generally viewed as a necessary but mundane activity which is not in itself a research topic. But a model capable of learning its parameters from observations and process models offers a new path forward, toward both better models and better ways of using models.The work has broader impacts due to the societal value of better forecasts and projections from weather and climate models. The work directly addresses uncertainty in forecasts and projections used by decision makers to plan for weather and climate impacts. In addition, the modeling strategy developed here is applicable to a broad class of research areas which face the problem of relating large-scale behaviors to small-scale unresolved processes (the problem of relating genotypes to phenotypes in evolutionary biology, for example). In addition, the PIs will establish a cross-disciplinary graduate program on data-driven Earth system modeling. The program bridges the gap between environmental and computational sciences which currently hinders progress in environmental modeling.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.