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

本计画针对天气气候预测中的一个难题--对流的表现。 对流的精确表示是很重要的，因为大多数当前的模式预测都依赖于它。解开对流条件、云和气溶胶所涉及的物理学可能需要多年的建模才能完全理解;然而，一组被称为“神经网络技术”的机器学习技术可以在过渡期间提供增强的可预测性，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。重点是将对流和云的形成纳入大尺度气候模式，并将社区地球系统模式（CESM）作为初始目标。 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。 神经网络将在明确解决对流的高保真模拟上进行训练。 两种类型的高分辨率模拟将用于训练神经网络：1）增强的超参数化模拟，以及2）基于马克斯普朗克研究所提供的ICOsahedral非流体静力学（ICON）建模框架的完整全球云解析模型（GCRM）模拟，使用初始5公里水平分辨率。 这项工作有可能增加对对流动力学和跨尺度过程的理解，也有可能解决其他尺度问题，该奖项反映了NSF的法定使命，并被认为是值得通过利用基金会的智力价值进行评估来支持的和更广泛的影响审查标准。

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.