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

合作研究：HDR Elements：基于新机器学习的湿对流参数化软件，利用深度学习改进气候和天气预报

• 批准号: 1835769
• 负责人: Pierre Gentine
• 金额: $ 30.74万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835769&HistoricalAwards=false
• 关键词: Collaborative; Research; HDR; Elements; Software

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.

该项目针对天气和气候预测中的一个困难问题 - 对流的代表。 对流的准确表示非常重要，因为大多数当前模型预测都取决于它。 揭开对流条件，云和气溶胶所涉及的物理学可能需要多年的建模才能充分理解；但是，一组被称为“神经网络技术”的机器学习技术可能会在此期间提供增强的可预测性，并且该项目探索了它们的潜力。该项目开发了一个python库，从而可以在广泛的科学领域中使用机器学习（人工神经网络）。重点是将对流和云形成的整合在大规模的气候模型中，将社区地球系统模型（CESM）作为初始目标。 该项目开发了一组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的高保真模拟培训。 将使用两种类型的高分辨率模拟来训练神经网络：1）增强的超级参数化模拟，以及2）使用最初的5km planck Institute提供的基于IcosaheDral非静态（ICON）建模框架的完整全球云解析模型（GCRM）仿真（GCRM）模拟。 这项努力有可能增加对对流动态和过程的理解，并且有可能实施以解决其他规模问题，在这种问题上，它在计算上太昂贵或不切实际，以至于代表比主要网格分辨率要大得多的过程相比，该奖项比NSF的法定任务反映了NSF的法定任务，并通过评估范围来反映了范围的范围。

项目成果

PrecipGAN: Merging Microwave and Infrared Data for Satellite Precipitation Estimation Using Generative Adversarial Network

• DOI: 10.1029/2020gl092032
• 发表时间: 2021-03
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Cunguang Wang;G. Tang;P. Gentine