TRIPODS+X:RES: Collaborative Research: Data Science Frontiers in Climate Science

TRIPODS X:RES：合作研究：气候科学中的数据科学前沿

• 批准号: 1930049
• 负责人: Rebecca Willett
• 金额: $ 30万
• 依托单位: University of Chicago
• 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: 2018-09-15 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1930049&HistoricalAwards=false
• 关键词: TRIPODS+X; RES; Collaborative; Research; Data

Understanding the factors that determine regional climate variability and change is a challenge with important implications for the economy, security, and environmental sustainability of many regions around the globe. Our understanding and modeling of the large-scale dynamics of the Earth climate system and associated regional-scale climate variability significantly affects our ability to predict and mitigate climatic extremes and hazards. Earth observations and climate model outputs are witnessing an unprecedented increase in data volume, creating new opportunities to advance climate science but also leading to new data science challenges that must be addressed using tools from mathematics, statistics, and computer science. This project focuses on two central challenges at the heart of modern data-enabled climate science: (1) Increasing the predictive capacity of subseasonal forecasts by discovering and quantifying the sources of (un)predictability, including known and emergent climate modes and their interactions and non-stationarities; and (2) Understanding and quantifying the intricate space-time dynamics of the climate system to provide guidance for climate model assessment and regional forecasting. This project brings together an interdisciplinary team that combines expertise in both hydroclimate science and statistical machine learning to create new platforms for climate diagnostics and prognostics. The broader impacts of an enhanced knowledge of the climate system and robust and accurate seasonal forecasts have wide-ranging implications for society as a whole. For example, better seasonal forecasts will allow water resource managers to make sustainable decisions for water allocation.This TRIPODS+CLIMATE project will develop novel machine learning and network estimation methodologies for analyzing the climate system over a range of space and time scales, to understand climate modes of variability and change and to explore their predictive ability for regional hydroclimatology. The two main objectives of this project are the following. Objective 1: Develop novel classification and regression tools that account for highly-correlated features or covariates, nonlinear interaction terms in high-dimensional settings, and nonstationarity in climate observations. These tools will be used to improve seasonal-to-subseasonal forecasts of regional precipitation using multidimensional climate modes and feature vectors in the presence of evolving dynamics and nonstationarities. Objective 2: Develop network identification methods that leverage recent advances in machine learning and statistics and that can account for the nonstationarity and limited timeframe of climate data. The network representation will be used to analyze the structure and dynamics of the learned dependencies to contextualize and interpret them physically, and to quantify changing patterns in climate modes and their regional predictive capacity. Emphasis will be placed on the western Pacific dynamics where an interhemispheric bi-directional connection has recently been discovered, promising earlier and more accurate seasonal-to-subseasonal forecasts in the southwestern US and other parts of the world.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.

了解决定区域气候变率和变化的因素是一项挑战，对全球许多地区的经济、安全和环境可持续性具有重要影响。我们对地球气候系统大尺度动态和相关区域尺度气候变化的理解和建模极大地影响了我们预测和减轻极端气候和灾害的能力。地球观测和气候模型输出的数据量正在空前增加，为推进气候科学创造了新的机会，但也带来了新的数据科学挑战，必须使用数学、统计学和计算机科学的工具来解决这些挑战。该项目重点关注现代数据驱动的气候科学核心的两个核心挑战：（1）通过发现和量化（不可）预测性的来源（包括已知和新兴的气候模式及其相互作用和影响）来提高次季节预报的预测能力。非平稳性； （2）理解和量化气候系统复杂的时空动态，为气候模式评估和区域预报提供指导。 该项目汇集了一个跨学科团队，结合了水文气候科学和统计机器学习的专业知识，为气候诊断和预测创建了新的平台。 加强对气候系统的了解以及稳健而准确的季节性预报会产生更广泛的影响，对整个社会产生广泛的影响。例如，更好的季节性预测将使水资源管理者能够做出可持续的水资源分配决策。这个 TRIPODS+CLIMATE 项目将开发新颖的机器学习和网络估计方法，用于分析一系列空间和时间尺度的气候系统，以了解气候变率和变化模式，并探索其对区域水文气候学的预测能力。该项目的两个主要目标如下。 目标 1：开发新颖的分类和回归工具，以解释高度相关的特征或协变量、高维环境中的非线性相互作用项以及气候观测中的非平稳性。这些工具将用于在存在不断变化的动态和非平稳性的情况下，利用多维气候模式和特征向量来改进区域降水的季节到次季节预测。目标 2：开发网络识别方法，利用机器学习和统计学的最新进展，并能够解释气候数据的非平稳性和有限时间范围。网络表示将用于分析所学习的依赖关系的结构和动态，以对其进行物理背景化和解释，并量化气候模式的变化模式及其区域预测能力。 重点将放在西太平洋动力学上，该动力学最近发现了半球间双向连接，有望在美国西南部和世界其他地区提供更早、更准确的季节到次季节预报。该奖项反映了 NSF 的法定使命和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，该项目被认为值得支持。

项目成果

Localizing Changes in High-Dimensional Regression Models

本地化高维回归模型中的变化

• 发表时间: 2021-04
• 期刊: International Conference on Artificial Intelligence and Statistics
• 作者: Rinaldo, Alessandro;Wang, Daren;Wen, Qin;Willett, Rebecca;Yu, Yi
• 通讯作者: Yu, Yi

Prediction in the Presence of Response-Dependent Missing Labels

存在依赖于响应的缺失标签时的预测

• DOI: 10.1109/ssp49050.2021.9513750
• 发表时间: 2021-07
• 期刊: IEEE Statistical Signal Processing Workshop
• 作者: Song, Hyebin;Raskutti, Garvesh;Willett, Rebecca
• 通讯作者: Willett, Rebecca

Localizing Changes in High-Dimensional Regression Models

本地化高维回归模型中的变化

• 发表时间: 2021-04
• 期刊: Proceedings of Machine Learning Research
• 作者: Rinaldo, A.;Wang, D.;Willett, R.;Yu, Y.
• 通讯作者: Yu, Y.

Neumann Networks for Linear Inverse Problems in Imaging

用于成像中线性逆问题的诺伊曼网络

• DOI: 10.1109/tci.2019.2948732
• 发表时间: 2020-01
• 期刊: IEEE Transactions on Computational Imaging
• 影响因子: 5.4
• 作者: Gilton, Davis;Ongie, Greg;Willett, Rebecca
• 通讯作者: Willett, Rebecca

Graph-Guided Regularization for Improved Seasonal Forecasting

用于改进季节性预测的图形引导正则化

• 发表时间: 2019-10
• 期刊: Climate Informatics
• 作者: Stevens A.; R. Willett
• 通讯作者: R. Willett