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: 1839338
• 负责人: Rebecca Willett
• 金额: $ 30万
• 依托单位: University of Wisconsin-Madison
• 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 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839338&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意义。我们对地球气候系统大尺度动力学的理解和建模以及相关的区域尺度气候变率显著影响着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出现前所未有的增长，这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，必须利用数学、统计学和计算机科学的工具来解决这些挑战。本项目重点关注现代数据气候科学的两个核心挑战：(1)通过发现和量化（非）可预测性的来源，包括已知和紧急气候模式及其相互作用和非平稳性，提高亚季节预报的预测能力；(2)认识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方面的专业知识，为气候诊断和预测创造了新的平台。增强对气候系统的认识以及强劲而准确的季节预报所产生的更广泛的影响对整个社会具有广泛的影响。例如，更好的季节预报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上的气候系统，以了解变率和变化的气候模式，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和气候观测中的非平稳性。这些工具将用于在不断变化的动力和非平稳性存在的情况下，利用多维气候模式和特征向量改进区域降水的季节到亚季节预报。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，并可以解释气候数据的非平稳性和有限的时间框架。网络表示将用于分析学习依赖关系的结构和动态，以将它们置于环境中并进行物理解释，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的动力上，在那里最近发现了半球间的双向联系，有望在美国西南部和世界其他地区进行更早和更准确的季节到亚季节预报。该奖项反映了美国国家科学基金会的法定使命，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

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
• 期刊: Climate Informatics
• 作者: Stevens A., R. Willett

Complexity of a projected Newton-CG method for optimization with bounds

• DOI: 10.1007/s10107-023-02000-z
• 发表时间: 2021-03
• 期刊: Mathematical Programming
• 影响因子: 2.7
• 作者: Yue Xie;Stephen J. Wright

Data-Driven Cloud Clustering via a Rotationally Invariant Autoencoder

• DOI: 10.1109/tgrs.2021.3098008
• 发表时间: 2021-07-26
• 期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
• 影响因子: 8.2
• 作者: Kurihana, Takuya;Moyer, Elisabeth;Foster, Ian
• 通讯作者: Foster, Ian