Collaborative Research: HDR Elements: Software for a new machine learning based parameterization of moist convection for improved climate and weather prediction using deep learning

这个项目针对的是天气和气候预测中的一个难题--对流的表示。对流的准确表示很重要，因为当前的大多数模型预测都依赖于它。解开与对流、云和气溶胶有关的物理问题可能需要数年的建模时间才能完全理解；然而，一套被称为“神经网络技术”的机器学习技术可能会在过渡期间提供更高的预测性，该项目探索了它们的潜力。该项目开发了一个Python库，使机器学习(人工神经网络)能够在广泛的科学领域中使用。重点是以共同体地球系统模式(CESM)为初始目标，将对流和云的形成纳入更大规模的气候模式。该项目开发了一套新的机器学习气候模型参数化，以减少天气和气候预测的不确定性。神经网络将接受明确解决对流问题的高保真模拟的训练。训练神经网络将使用两种类型的高分辨率模拟：1)增强的超参数模拟，2)基于Max Planck研究所提供的二十面体非流体静力(ICON)建模框架的完整全球云解析模式(GCRM)模拟，使用初始5公里的水平分辨率。这项工作有可能增加对对流动力学和跨尺度过程的理解，并可能被实施来解决其他尺度问题，在这些问题中，计算成本太高或不切实际，无法表示发生在比主要网格分辨率更精细的尺度上的过程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project targets a difficult problem in weather and climate prediction -- the representation of convection. Accurate representation of convection is important, since a majority of current model predictions depend on it. Unraveling the physics involved in convective conditions, clouds and aerosols may take years of modeling to fully understand; however, a set of machine learning techniques, known as "neural net techniques", may provide enhanced predictability in the interim, and this project explores their potential.The project develops a Python library enabling the use of machine learning (artificial neural networks) in a broad range of science domains. The focus is on integration of convection and cloud formation within larger-scale climate models, with the Community Earth System Model (CESM) as an initial target. The project develops a new set of machine learning climate model parameterizations to reduce uncertainty in weather and climate predictions. The neural networks will be trained on high-fidelity simulations that explicitly resolve convection. Two types of high-resolution simulations will be used for training the neural networks: 1) an augmented super-parameterized simulation, and 2) a full Global Cloud Resolving Model (GCRM) simulation based on the ICOsahedral Non-hydrostatic (ICON) modelling frameworks provided by the Max Planck Institute, using initial 5km horizontal resolution. The effort has the potential to increase understanding of convection dynamics and processes across scales, and could potentially be implemented to address other scale problems as well, where it is too computationally costly or impractical to represent processes occurring at much finer scales than the main grid resolution.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.