ATD: Statistical and Machine Learning Methods for Studying the Dynamics of Weather and Climate Extremes
ATD:研究天气和极端气候动态的统计和机器学习方法
基本信息
- 批准号:2124576
- 负责人:
- 金额:$ 38万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-15 至 2024-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Weather and climate extremes profoundly impact human society and the natural environment of all countries, rich and poor. Recent years have seen a number of large losses of life as well as a tremendous increase in economic losses from weather hazards. The start of 2020 found Australia amid its worst-ever bushfire season, following on from its hottest year on record which had left soil and fuels exceptionally dry. The fires have burned through more than 10 million hectares, killed at least 28 people, and left millions of people affected by a hazardous smoke haze. Higher sea temperatures have doubled the likelihood of drought in the Horn of Africa region. Severe droughts have left 15 million people in Ethiopia, Kenya and Somalia in need of aid, and millions of people are facing acute food and water shortages. In the summer of 2020, the West Coast of the U.S. saw its most catastrophic wildfires following the arguably most intensive heat waves in its modern history. According to NOAA’s report (2020), just during the month of August in 2020 the U.S. was hit by four different billion-dollar disasters: two hurricanes, huge wildfires, and an extraordinary Midwest derecho. While extreme weather is a part of the natural cycle, the recent uptick in the ferocity and frequency of these extremes is evidence of an acceleration of climate impacts. This project will support one graduate student each year of the three year project. This project will develop statistical and machine learning methods to study weather and climate extremes from three different perspectives: climate model validation, changepoint estimation for extremes, and integration of multi-model climate ensembles. Climate models are vital tools for scientists studying climate dynamics and extremes. Hence, validating climate models in their capacity of mimicking real climate extremes is a critical task. This involves comparing the modeled and observed spatial extremes, and adjustment for multiple testing is one of the key statistical challenges in comparing random fields. We will develop optimal statistical techniques for comparing the return levels of two spatial extremes random fields. The detection of changepoints and estimation of break time in extreme weather and climate have not received due attention to date, yet changepoints can signal a climate system’s tipping point and thus are important for disaster preparedness and activation of adaptation measures against climate risks. We will also develop a novel method for estimating spatially varying changepoints for functional time series to study abrupt changes in climate extremes. Finally, an array of methodology for multi-model ensemble integration has been developed, ranging from simple or weighted averaging of the models to fully Bayesian hierarchical models. The multiple levels of hierarchy in Bayesian models motivated us to take advantage of neural networks to learn the complex relationship between different climate models and actual observations. Finally, we will develop a Bayesian machine learning approach to integrating model outputs with observations to project future climate extremes.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.
极端天气和气候对人类社会和所有国家(无论贫富)的自然环境产生了深远影响,2020 年初,澳大利亚因天气灾害造成了大量人员伤亡和经济损失。继有记录以来最热的一年之后,今年正值有史以来最严重的丛林大火季节,这场大火导致土壤和燃料异常干燥,火灾面积已超过 1000 万公顷,造成至少 28 人死亡,数百万人受到影响。海洋温度升高使非洲之角地区发生干旱的可能性增加了一倍,严重干旱导致埃塞俄比亚、肯尼亚和索马里有 1500 万人需要援助,数百万人面临严重的粮食和水短缺。根据 NOAA 的报告(2020),2020 年夏季,美国西海岸经历了现代历史上最严重的野火。 2020 年 8 月,美国遭受了四场价值数十亿美元的灾难:两场飓风、巨大的野火和一场非同寻常的中西部飓风。虽然极端天气是自然循环的一部分,但最近这些极端天气的严重程度和频率有所上升。是气候影响加速的证据。该项目将在为期三年的项目中每年支持一名研究生,该项目将开发统计和机器学习方法,从三个不同的角度研究天气和气候极端情况:气候模型验证、极端事件的变化点估计和多模型气候集合的集成气候模型是科学家研究气候动态和极端事件的重要工具,因此验证气候模型模拟真实气候极端事件的能力是一项关键任务。和观察到的空间极值,以及多重测试的调整是比较随机场的关键统计挑战之一,我们将开发用于比较两个空间极值随机场的返回水平的最佳统计技术。极端的天气和气候并没有迄今为止,变化点已受到应有的关注,但变化点可以标志着气候系统的临界点,因此对于防灾和针对气候风险启动适应措施非常重要,我们还将开发一种新方法来估计功能时间序列的空间变化变化点,以研究突变。最后,开发了一系列多模型集合集成的方法,从模型的简单或加权平均到完全贝叶斯分层模型,贝叶斯模型的多个层次结构促使我们利用这些方法。神经的最后,我们将开发一种贝叶斯机器学习方法,将模型输出与观测结果相结合,以预测未来的极端气候。该奖项是 NSF 的法定使命,并通过评估被认为值得支持。利用基金会的智力优势和更广泛的影响审查标准。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Crop Yield Prediction Using Bayesian Spatially Varying Coefficient Models with Functional Predictors
- DOI:10.1080/01621459.2022.2123333
- 发表时间:2022-09
- 期刊:
- 影响因子:3.7
- 作者:Yeonjoo Park;Bo Li;Yehua Li
- 通讯作者:Yeonjoo Park;Bo Li;Yehua Li
Bias correction for nonignorable missing counts of areal HIV new diagnosis
- DOI:10.1002/sta4.555
- 发表时间:2023-01-01
- 期刊:
- 影响因子:1.7
- 作者:Qu,Tianyi;Li,Bo;Albarracin,Dolores
- 通讯作者:Albarracin,Dolores
Reflections on the IDEA Forum—Statistics, Climate Change, and Sustainability
对 IDEA 论坛的思考——统计、气候变化和可持续发展
- DOI:10.1080/09332480.2023.2179273
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Li, Bo;Simpson, Douglas
- 通讯作者:Simpson, Douglas
Scalable multiple changepoint detection for functional data sequences
- DOI:10.1002/env.2710
- 发表时间:2020-08
- 期刊:
- 影响因子:1.7
- 作者:Trevor Harris;Bo Li;J. D. Tucker
- 通讯作者:Trevor Harris;Bo Li;J. D. Tucker
Discussion for “Bayesian Nonstationary and Nonparametric Co- variance Estimation for Large Spatial Data” by Kidd and Katzfuss
Kidd 和 Katzfuss 对“大型空间数据的贝叶斯非平稳和非参数协方差估计”的讨论
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:4.4
- 作者:6. Li, B. and
- 通讯作者:6. Li, B. and
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Bo Li其他文献
Wiener-filter-based Minimum Variance Self-tuning Regulation
基于维纳滤波器的最小方差自调节调节
- DOI:
10.1016/s0005-1098(97)00190-8 - 发表时间:
1996 - 期刊:
- 影响因子:0
- 作者:
R. Horowitz;Bo Li;James McCormick - 通讯作者:
James McCormick
Properties of photochlorinated graphene
光氯化石墨烯的特性
- DOI:
10.1109/nmdc.2011.6155366 - 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Di Wu;Bo Li;Lin Zhou;H. Peng;Kai Yan;Yu Zhou;Zhongfan Liu - 通讯作者:
Zhongfan Liu
An MMT based heterogeneous multimedia system using QUIC
使用QUIC的基于MMT的异构多媒体系统
- DOI:
10.1109/cciot.2016.7868318 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Bo Li;Chengzhi Wang;Yiling Xu;Zhan Ma - 通讯作者:
Zhan Ma
Formation mechanisms of solid in water in oil compound droplets in a horizontal T-junction device
水平T型连接装置中油包水复合液滴的形成机理
- DOI:
10.1016/j.ces.2017.10.049 - 发表时间:
2018-02 - 期刊:
- 影响因子:4.7
- 作者:
Dawei Pan;Meifang Liu;Fang Li;Qiang Chen;Xiangdong Liu;Yiyang Liu;Zhanwen Zhang;Weixing Huang;Bo Li - 通讯作者:
Bo Li
Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet
基于异常检测和SPP-PCANet的光学遥感图像船舶自动检测
- DOI:
10.3390/rs11010047 - 发表时间:
2018-12 - 期刊:
- 影响因子:5
- 作者:
Nan Wang;Bo Li;Yonghua Wang - 通讯作者:
Yonghua Wang
Bo Li的其他文献
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{{ truncateString('Bo Li', 18)}}的其他基金
ERI: Robust and Scalable Manufacturing of Ultra-Sensitive and Selective Molecule Sensor Arrays
ERI:稳健且可扩展的超灵敏和选择性分子传感器阵列制造
- 批准号:
2301668 - 财政年份:2024
- 资助金额:
$ 38万 - 项目类别:
Standard Grant
Characterizing CmodAA-Containing Biosynthetic Pathways of Nonribosomal Peptides
表征非核糖体肽的含 CmodAA 生物合成途径
- 批准号:
2310177 - 财政年份:2023
- 资助金额:
$ 38万 - 项目类别:
Standard Grant
Collaborative Research: NRI: Smart Skins for Robotic Prosthetic Hand
合作研究:NRI:机器人假手智能皮肤
- 批准号:
2221102 - 财政年份:2022
- 资助金额:
$ 38万 - 项目类别:
Standard Grant
CAREER: DeepTrust: Enabling Robust Machine Learning with Exogenous Information
职业:DeepTrust:利用外源信息实现稳健的机器学习
- 批准号:
2046726 - 财政年份:2021
- 资助金额:
$ 38万 - 项目类别:
Continuing Grant
Collaborative Research: Spatiotemporal Dynamics of Interacting Bacterial Communities in Compact Colonies
合作研究:紧密菌落中相互作用的细菌群落的时空动态
- 批准号:
2029574 - 财政年份:2020
- 资助金额:
$ 38万 - 项目类别:
Standard Grant
Sorting and Assembly of Nanomaterials on Polymer Substrates Using Fluidic and Weak Ultrasound Fields for Fabrication of Flexible Electronic Devices
使用流体和弱超声场在聚合物基底上分类和组装纳米材料以制造柔性电子器件
- 批准号:
2003077 - 财政年份:2020
- 资助金额:
$ 38万 - 项目类别:
Standard Grant
AF: Small: Collaborative Research: Rigorous Approaches for Scalable Privacy-preserving Deep Learning
AF:小型:协作研究:可扩展的隐私保护深度学习的严格方法
- 批准号:
1910100 - 财政年份:2019
- 资助金额:
$ 38万 - 项目类别:
Standard Grant
Travel Support for Student Participation at the 2018 ASME-IMECE Micro and Nano Technology Forum; Pittsburgh, PA, November 12-15, 2018
为学生参加2018年ASME-IMECE微纳米技术论坛提供差旅支持;
- 批准号:
1854005 - 财政年份:2018
- 资助金额:
$ 38万 - 项目类别:
Standard Grant
ATD: Collaborative Research: Predicting the Threat of Vector-Borne Illnesses Using Spatiotemporal Weather Patterns
ATD:合作研究:利用时空天气模式预测媒介传播疾病的威胁
- 批准号:
1830312 - 财政年份:2018
- 资助金额:
$ 38万 - 项目类别:
Continuing Grant
An integrated experimental and computational study of erythrocyte adhesion mechanics in blood flows
血流中红细胞粘附力学的综合实验和计算研究
- 批准号:
1706295 - 财政年份:2017
- 资助金额:
$ 38万 - 项目类别:
Standard Grant
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