AGS-PRF: Understanding Tropical High Cloud Feedbacks via Machine Learning and Super Parameterization
AGS-PRF:通过机器学习和超级参数化了解热带高云反馈
基本信息
- 批准号:2020305
- 负责人:
- 金额:$ 19万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Fellowship Award
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-06-01 至 2022-03-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Convective rainfall often takes the form of deep cumulus clouds, which appear as bright red spots on a radar image, embedded in a larger region of weaker rainfall colored orange or yellow. These broad areas of weaker rainfall typically contain high, relatively flat clouds similar to the "anvils" seen above isolated thunderstorms. The high stratiform clouds are a consequence of the convective cells, just as the anvil forms only after a thunderstorm is fully developed. But the presence of a broad region of upper-level stratus can affect the subsequent development of convective clouds, or in other words it can promote large-scale convective aggregation and organization. Upper-level clouds encourage new cloud formation by blocking outgoing infrared radiation, heating the column below and promoting rising motions. The effect is thought to be important over tropical oceans where the frontal weather systems that organize convection in higher latitudes are largely absent. But understanding of this infrared cloud-radiation feedback and its effects on tropical weather and climate is currently quite limited.Work under this award addresses the role of cloud-radiation feedback in the development of the Madden-Julian Oscillation (MJO), in which a large region of convection organizes over the tropical Indian Ocean and propagates slowly eastward for a period of 30 to 60 days. Cloud-radiation feedback has been invoked as a cause of convective organization in the MJO and as a key factor in determining its propagation speed. A second issue addressed in the proposal is the possible role of convective organization in determining the Earth's climate sensitivity, meaning the amount of global warming or cooling that results from a given increase or decrease in greenhouse gas concentrations.The research is conducted through a combination of observational analysis and numerical simulations. The observational analysis uses artificial neural networks (ANNs) applied to satellite and weather balloon data to determine how stratus cloud development relates to ambient temperature, moisture, and stability in the cloud layer. The relationships identified by the ANN are interpreted using layer-wise relevance propagation (LRP), a technique that identifies the inputs which matter the most for generating an ANN result. The value of machine learning tools like ANN for basic science is often questioned because of their "black box" nature: while machine learning methods can have uncanny predictive power, their results do not come with any explanation for why a particular set of inputs produces a given result. LRP is thus a means to open the black box and gain physical insight and understanding from the empirical relationships identified through the ANN machinery.The work has societal relevance due to the worldwide effects of the MJO, which influences weather and climate phenomena including tropical cyclones (particularly in the Gulf of Mexico), the amount and timing of monsoon rainfall, and the onset of El Nino events. MJO events are predictable in principle given their slow propagation, but prediction skill is limited in current weather and climate models. The possible influence of convective organization on climate sensitivity is also of societal interest given the rapid rise of greenhouse gas concentrations. The project also contributes to workforce development by providing support to an early-career scientist.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.
对流降雨通常以深积云的形式出现,在雷达图像上看起来像明亮的红色斑点,嵌入在一个更大的橙子或黄色的较弱降雨区域中。 这些降雨量较弱的广阔区域通常包含高而相对平坦的云,类似于孤立雷暴上方的“铁砧”。 高空的层状云是对流单体的结果,就像铁砧只有在雷暴充分发展后才形成一样。 但大范围上层层云的存在会影响对流云的后续发展,换句话说,它会促进大规模对流的聚集和组织。 上层云层通过阻挡向外的红外辐射,加热下层的气柱,促进上升运动,从而促进新云的形成。 这种影响被认为在热带海洋上很重要,因为在那里,组织高纬度对流的锋面天气系统基本上不存在。 但目前对这种红外云辐射反馈及其对热带天气和气候的影响的了解相当有限。该奖项的工作涉及云辐射反馈在马登-朱利安振荡(MJO)发展中的作用,其中热带印度洋上空的大面积对流组织并缓慢向东传播30至60天。 云辐射反馈被认为是MJO中对流组织的原因,也是确定其传播速度的关键因素。 该提案涉及的第二个问题是对流组织在确定地球气候敏感性方面可能发挥的作用,即温室气体浓度增加或减少导致的全球变暖或变冷的程度。该研究通过观测分析和数值模拟相结合进行。 观测分析使用应用于卫星和气象气球数据的人工神经网络(ANN)来确定层云的发展与环境温度,湿度和云层稳定性的关系。 使用逐层相关传播(LRP)来解释由ANN识别的关系,LRP是一种识别对于生成ANN结果最重要的输入的技术。 像人工神经网络这样的机器学习工具在基础科学中的价值经常受到质疑,因为它们的“黑箱”性质:虽然机器学习方法可以具有不可思议的预测能力,但它们的结果并没有解释为什么一组特定的输入会产生给定的结果。 因此,LRP是一种手段,打开黑匣子,并获得物理洞察力和理解的经验关系,通过人工神经网络machinery.The工作具有社会意义,由于全球影响的MJO,影响天气和气候现象,包括热带气旋(特别是在墨西哥湾),季风降雨的数量和时间,以及厄尔尼诺事件的发生。 由于其传播速度缓慢,MJO事件在原则上是可预测的,但在当前的天气和气候模式中,预测技术有限。由于温室气体浓度的迅速上升,对流组织对气候敏感性的可能影响也引起社会关注。这个奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Large-scale state and evolution of the atmosphere and ocean during PISTON 2018
- DOI:10.1175/jcli-d-20-0517.1
- 发表时间:2021-03
- 期刊:
- 影响因子:4.9
- 作者:A. Sobel;J. Sprintall;E. Maloney;Z. Martin;Shuguang Wang;S. Szoeke;B. C. Trabing;S. Rutledge
- 通讯作者:A. Sobel;J. Sprintall;E. Maloney;Z. Martin;Shuguang Wang;S. Szoeke;B. C. Trabing;S. Rutledge
The MJO-QBO Relationship in a GCM with Stratospheric Nudging
- DOI:10.1175/jcli-d-20-0636.1
- 发表时间:2021-03
- 期刊:
- 影响因子:4.9
- 作者:Z. Martin;C. Orbe;Shuguang Wang;A. Sobel
- 通讯作者:Z. Martin;C. Orbe;Shuguang Wang;A. Sobel
Using Simple, Explainable Neural Networks to Predict the Madden‐Julian Oscillation
- DOI:10.1029/2021ms002774
- 发表时间:2021-06
- 期刊:
- 影响因子:6.8
- 作者:Z. Martin;E. Barnes;E. Maloney
- 通讯作者:Z. Martin;E. Barnes;E. Maloney
The influence of the quasi-biennial oscillation on the Madden–Julian oscillation
- DOI:10.1038/s43017-021-00173-9
- 发表时间:2021-06
- 期刊:
- 影响因子:42.1
- 作者:Z. Martin;S. Son;A. Butler;H. Hendon;Hyemi Kim;A. Sobel;S. Yoden;Chidong Zhang
- 通讯作者:Z. Martin;S. Son;A. Butler;H. Hendon;Hyemi Kim;A. Sobel;S. Yoden;Chidong Zhang
Variability in QBO Temperature Anomalies on Annual and Decadal Time Scales
- DOI:10.1175/jcli-d-20-0287.1
- 发表时间:2020-10
- 期刊:
- 影响因子:4.9
- 作者:Z. Martin;A. Sobel;A. Butler;Shuguang Wang
- 通讯作者:Z. Martin;A. Sobel;A. Butler;Shuguang Wang
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Zane Martin其他文献
Which is a better target for AD immunotherapy, A beta or tau?
A beta 或 tau 哪个是 AD 免疫治疗更好的靶标?
- DOI:
- 发表时间:
2008 - 期刊:
- 影响因子:2.1
- 作者:
Zane Martin;C. Lasagna - 通讯作者:
C. Lasagna
AlzPED: An Open Science Tool Raising the Standards for Preclinical Testing of Candidate Therapeutics in Alzheimer’s Disease Animal Models
- DOI:
10.1016/j.neurot.2024.e00407 - 发表时间:
2024-07-01 - 期刊:
- 影响因子:
- 作者:
Jaya Viswanathan;Maria Fe Lanfranco Gallofre;Zane Martin;Suzana Petanceska;Shreaya Chakroborty;Lorenzo Refolo - 通讯作者:
Lorenzo Refolo
Left-Handedness and Artistic Abilities: A First Look
左利手与艺术能力:初探
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
S. Singg;Zane Martin - 通讯作者:
Zane Martin
Zane Martin的其他文献
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