EAGER: Collaborative Research: Learning Relations between Extreme Weather Events and Planet-Wide Environmental Trends

EAGER：合作研究：学习极端天气事件与全球环境趋势之间的关系

基本信息

批准号：
1451954
负责人：
Claire Monteleoni
金额：
$ 10万
依托单位：
George Washington University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2017-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451954&HistoricalAwards=false
关键词：
EAGER Collaborative Research Learning Relations

项目摘要

Extreme events, such as heat waves, cold spells, extreme precipitation, and severe storms, play a significant role in the loss of lives and damage to ecosystems and infrastructure, presenting fundamental challenges to sustainability. Under anticipated trends in planet-scale environmental trends, there is considerable uncertainty in the projected changes in the intensity, duration, and frequency of extreme events. Reducing these uncertainties is a grand challenge that will require substantial advances in both the environmental and data sciences. The proposed research seeks to advance both the environmental science that underpins predictions of extreme events, and the data science required to identify relations between variables in massive data sets. The results of this research will provide a basis for improving predictions of extreme events for use in sustainability planning. This project will educate and cross-train graduate students in both disciplines, allowing them to contribute to this new emerging field. The proposed research will also inform course development, and will be disseminated through tutorials, conferences, and seminars. The team's involvement with workshops, the GW Sustainability Institute, and GW Planet Forward will help to broaden the impact through public outreach.The proposed research will advance machine learning and statistical modeling of large-scale and regional events by: (1) using new tools in sparse regression in high dimensions, (2) identifying nonlinear relations in data, and (3) learning relations in spatiotemporal data that are non-stationary over space and time. The results of this research will advance understanding of extreme weather events and their relation to planet-wide environmental trends. Such relations will be learned by applying new statistical algorithms to analyze extensive climate model simulations which generate very large data sets. The findings will be validated against observations, and the learned relations will be compared between different models to assess consistency and robustness, and to validate models.

极端事件，例如热浪，寒冷，极端降水和严重的风暴，在生命丧失以及对生态系统和基础设施的损害中起着重要作用，对可持续性提出了根本性的挑战。在行星规模的环境趋势的预期趋势下，极端事件的强度，持续时间和频率预计变化存在很大的不确定性。减少这些不确定性是一个巨大的挑战，需要在环境和数据科学方面取得重大进展。拟议的研究旨在推进基础极端事件预测的环境科学，以及确定大量数据集中变量之间关系所需的数据科学。这项研究的结果将为改善对可持续性计划中使用的极端事件的预测提供基础。该项目将在这两个学科中教育和跨培训研究生，使他们能够为这个新的新兴领域做出贡献。拟议的研究还将为课程开发提供信息，并将通过教程，会议和研讨会进行传播。该团队参与研讨会，GW可持续发展研究所和GW Planet Forward将有助于通过公共宣传来扩大影响。拟议的研究将通过以下方式通过以下方面的机器学习和大规模和区域事件的统计建模，通过：（1）在高维度中使用稀疏回归中的新工具，（2）在空间中识别出数据的非线关系，并（3）跨越了Spatiotiot的数据，并跨越了Spatiotiot的数据。这项研究的结果将提高对极端天气事件的了解及其与行星范围内环境趋势的关系。将通过应用新的统计算法来分析产生非常大数据集的广泛气候模型模拟来学习这种关系。这些发现将通过观察结果进行验证，并将在不同模型之间比较学习的关系，以评估一致性和鲁棒性并验证模型。