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:1835769
- 负责人:
- 金额:$ 30.74万
- 依托单位:
- 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:
- 关键词:
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.
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。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:5.2
- 作者:Cunguang Wang;G. Tang;P. Gentine
- 通讯作者:Cunguang Wang;G. Tang;P. Gentine
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Pierre Gentine其他文献
Two for one: Partitioning COsub2/sub fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primary productivity using machine learning
二合一:利用机器学习划分二氧化碳通量并理解太阳诱导叶绿素荧光与总初级生产力之间的关系
- DOI:
10.1016/j.agrformet.2022.108980 - 发表时间:
2022-06-15 - 期刊:
- 影响因子:5.700
- 作者:
Weiwei Zhan;Xi Yang;Youngryel Ryu;Benjamin Dechant;Yu Huang;Yves Goulas;Minseok Kang;Pierre Gentine - 通讯作者:
Pierre Gentine
Estimating evapotranspiration using remotely sensed solar-induced fluorescence measurements
- DOI:
10.1016/j.agrformet.2021.108800 - 发表时间:
2022-03-01 - 期刊:
- 影响因子:5.700
- 作者:
Kai Zhou;Quan Zhang;Lihua Xiong;Pierre Gentine - 通讯作者:
Pierre Gentine
Emissions rebound from the COVID-19 pandemic
- DOI:
10.1038/s41558-022-01332-6 - 发表时间:
2022 - 期刊:
- 影响因子:30.7
- 作者:
Steven J. Davis;Zhu Liu;Zhu Deng;Biqing Zhu;Piyu Ke;Taochun Sun;Rui Guo;Chaopeng Hong;Bo Zheng;Yilong Wang;Olivier Boucher;Pierre Gentine;Philippe Ciais - 通讯作者:
Philippe Ciais
Shallow groundwater inhibits soil respiration and favors carbon uptake in a wet alpine meadow ecosystem
浅层地下水抑制土壤呼吸并有利于潮湿高山草甸生态系统的碳吸收
- DOI:
10.22541/au.158880248.84807120 - 发表时间:
2020-05 - 期刊:
- 影响因子:6.2
- 作者:
Shaobo Sun;Tao Che;Pierre Gentine;Qiting Chen;Zhaoliang Song - 通讯作者:
Zhaoliang Song
GEOSIF: A continental-scale sub-daily reconstructed solar-induced fluorescence derived from OCO-3 and GK-2A over Eastern Asia and Oceania
GEOSIF:源自东亚和大洋洲 OCO-3 和 GK-2A 的大陆尺度次日重建太阳诱导荧光
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:13.5
- 作者:
Sungchan Jeong;Youngryel Ryu;Xing Li;Benjamin Dechant;Jiangong Liu;Juwon Kong;Wonseok Choi;Jianing Fang;Xu Lian;Pierre Gentine - 通讯作者:
Pierre Gentine
Pierre Gentine的其他文献
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{{ truncateString('Pierre Gentine', 18)}}的其他基金
STC: Center for Learning the Earth with Artificial Intelligence and Physics (LEAP)
STC:利用人工智能和物理学习地球中心 (LEAP)
- 批准号:
2019625 - 财政年份:2021
- 资助金额:
$ 30.74万 - 项目类别:
Cooperative Agreement
Collaborative Research: Dynamics of Unsaturated Downdrafts, Cold Pools, and Their Roles in Convective Initiation and Organization
合作研究:不饱和下降气流、冷池的动力学及其在对流引发和组织中的作用
- 批准号:
1649770 - 财政年份:2017
- 资助金额:
$ 30.74万 - 项目类别:
Continuing Grant
Collaborative Research: Role of Cloud Albedo and Land-Atmosphere Interactions on Continental Tropical Climates
合作研究:云反照率和陆地-大气相互作用对大陆热带气候的作用
- 批准号:
1734156 - 财政年份:2017
- 资助金额:
$ 30.74万 - 项目类别:
Standard Grant
CAREER: Departure from Monin-Obukhov Similarity Theory (MOST) using high-resolution turbulence models
职业生涯:使用高分辨率湍流模型偏离 Monin-Obukhov 相似理论 (MOST)
- 批准号:
1552304 - 财政年份:2016
- 资助金额:
$ 30.74万 - 项目类别:
Continuing Grant
Summer School in Land-atmosphere Interactions
陆地-大气相互作用暑期学校
- 批准号:
1522174 - 财政年份:2015
- 资助金额:
$ 30.74万 - 项目类别:
Standard Grant
Collaborative Research: Quantifying the impacts of atmospheric and land surface heterogeneity and scale on soil moisture-precipitation feedbacks
合作研究:量化大气和地表异质性和规模对土壤湿度-降水反馈的影响
- 批准号:
1035843 - 财政年份:2011
- 资助金额:
$ 30.74万 - 项目类别:
Standard Grant
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