AGS-PRF: Physics-Constrained Machine Learning-Based Models for Climate Simulations with Data Assimilation and Uncertainty Quantification

AGS-PRF：基于物理约束的机器学习模型，用于具有数据同化和不确定性量化的气候模拟

• 批准号: 2218197
• 负责人: Mohamed Aziz Bhouri
• 金额: $ 19万
• 依托单位: Bhouri, Mohamed Aziz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218197&HistoricalAwards=false
• 关键词: AGS; PRF; Physics; Constrained; Machine

Despite recent computational advancements, climate models still cannot explicitly resolve key physical processes like turbulence, convection, and clouds. These unresolved processes must be accounted for by parameterization schemes. The inability of these schemes to mimic reality has hindered the ability of model simulations to capture various observed phenomena, leading model biases and uncertainties. Recently, machine learning-based techniques have greatly improved these schemes. Nevertheless, the standard machine learning-based methods are built solely on idealized computational models without considering the wealth of observational data available. Thus, inherent inaccuracies of the parameterization schemes continue to undermine the performance of climate models. This project aims to improve machine-learning-based parameterization schemes and, thereby, enhance the performance of existing climate models. The proposer will incorporate observations and physical laws into machine learning techniques to make parameterization schemes more accurate and provide uncertainty estimates to capture the chaotic nature of the climate system. The proposed work will train a young postdoctoral scholar. Specifically, the proposal will utilize data from satellite observations and high-resolution simulations to improve machine learning schemes. Physics-constraints will be imposed either through the incorporation of a physical loss term or by considering specific machine learning tools such as Neural Networks with fixed output layer that strongly imposes the known physical constraint. Uncertainty quantification in machine learning schemes will be implemented using ensemble learning or Bayesian approaches such as Hamiltonian Monte Carlo sampling schemes. The knowledge gained in this project could have an impact across climate science, including the advancement of global climate models’ development and of machine learning application to Big Data in climate science, as well as the development of novel computational probabilistic methods for complex multi-scale and multi-physics real-world systems.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.

尽管最近的计算进步，气候模型仍然无法明确解决诸如湍流，构造和云之类的关键物理过程。这些未解决的过程必须通过参数化方案来解释。这些方案无法模仿现实的能力阻碍了模型模拟捕获各种观察到的现象，领先的模型偏见和不确定性的能力。最近，基于机器学习的技术极大地改善了这些方案。然而，基于机器学习的标准方法仅基于理想化的计算模型，而无需考虑可用的大量观察数据。该项目旨在改善基于机器的参数化方案，从而提高现有攀岩模型的性能。该提案将将观察结果和物理定律纳入机器学习技术中，以使参数化方案更加准确，并提供不确定性估计以捕获气候系统的混乱性。拟议的工作将培训一门年轻的博士后科学。具体而言，该提案将利用卫星观测和高分辨率模拟的数据来改善机器学习方案。物理构成将通过纳入物理损失项或考虑特定的机器学习工具（例如具有固定输出层的神经网络）来实现，这极大地不可能是已知的物理约束。机器学习方案中的不确定性量化将使用集合学习或贝叶斯方法（例如汉密尔顿蒙特卡洛抽样方案）实施。该项目中获得的知识可能会在整个气候科学上产生影响，包括全球气候模型的发展以及机器学习应用到气候科学中的大数据，以及用于复杂多尺度和多型物理学的新型计算概率方法的开发，这些方法是通过Interviation and Intelligation ryation rakes nsfient and Intelligation rakite，这反映了NSF的诚实构成的构成，这是诚实的支持。 标准。