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

基本信息

批准号：
1835863
负责人：
Michael Pritchard
金额：
$ 28.94万
依托单位：
University of California-Irvine
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=1835863&HistoricalAwards=false
关键词：
Collaborative Research HDR Elements Software

项目摘要

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。对流的准确表示非常重要，因为当前大多数模型预测都依赖于它。解开对流条件、云和气溶胶所涉及的物理原理可能需要数年的建模才能完全理解；然而，一组称为“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，该项目探索了它们的潜力。该项目开发了一个 Python 库，可以在广泛的科学领域。重点是将对流和云形成整合到更大规模的气候模型中，以社区地球系统模型（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。神经网络将接受高保真模拟的训练，以明确解决对流问题。将使用两种类型的高分辨率模拟来训练神经网络：1）增强的超参数化模拟，2）基于 ICOsahedral 非静水压 (ICON) 建模框架的完整全局云解析模型 (GCRM) 模拟由马克斯普朗克研究所提供，使用初始 5 公里水平分辨率。这项工作有可能增加对跨尺度对流动力学和过程的理解，并且也有可能解决其他尺度问题，在这些问题中，表示在比主网格分辨率更精细的尺度上发生的过程的计算成本太高或不切实际该奖项反映了 NSF 的法定使命，并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，被认为值得支持。