EAGER: Improving our Understanding of Supercell Storms through Data Science

EAGER：通过数据科学提高我们对超级细胞风暴的理解

• 批准号: 1802627
• 负责人: Amy McGovern
• 金额: $ 16.85万
• 依托单位: University of Oklahoma Norman Campus
• 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: 2018-01-15 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1802627&HistoricalAwards=false
• 关键词: EAGER; Improving; our; Understanding; Supercell

This study seeks to apply novel data science techniques (such as tree-based classification models and deep learning) to four-dimensional (4D) weather radar observations of thunderstorm dynamics to enable identification of storms capable of producing tornadoes up to an hour prior to tornadogenesis. Real-time severe storm prediction is a challenging task that currently requires a human forecaster with a thorough understanding of the dynamics and current state of the atmosphere. This study will develop and apply data science techniques to four-dimensional radar data from severe storms throughout the continental U.S. with the goal of identifying critical spatiotemporal relationships that can improve the understanding and prediction of tornadoes. The long-term goal will be to develop techniques to fundamentally improve our understanding of severe storms in general (including hail, wind, and tornadoes) by analyzing the new knowledge identified by the data science models.This study seeks to advance the scientific knowledge of tornadogenesis by identifying novel precursors to tornadoes in two unique 4D weather radar datasets. Data science has the potential to advance knowledge by processing and objectively evaluating a large amount of data in a relatively short period of time. This provides a mechanism by which large, complicated meteorological datasets can be assessed for their predictive capability or alternative applications without the need for time consuming subjective evaluation. The methods developed will enable others to evaluate existing Earth system data to a spatiotemporal extent that is not possible with established approaches. The application of data science techniques to a novel domain will require the development of new techniques focusing on spatiotemporal 4D weather radar data.

这项研究试图将新的数据科学技术(如基于树的分类模型和深度学习)应用于雷暴动力学的四维(4D)天气雷达观测，以识别能够在龙卷风发生前一小时内产生龙卷风的风暴。实时强风暴预报是一项具有挑战性的任务，目前需要一名对大气动态和当前状态有透彻了解的人类预报员。这项研究将开发数据科学技术并将其应用于美国大陆强风暴的四维雷达数据，目的是识别关键的时空关系，以提高对龙卷风的理解和预测。这项研究的长期目标是通过分析数据科学模型识别的新知识，从根本上提高我们对强风暴(包括冰雹、风和龙卷风)的理解。这项研究试图通过在两个独特的4D天气雷达数据集中识别龙卷风的新前兆来推进龙卷风发生的科学知识。通过在相对较短的时间内处理和客观地评估大量数据，数据科学具有推动知识进步的潜力。这提供了一种机制，可以用来评估大型、复杂的气象数据集的预测能力或替代应用，而不需要耗时的主观评估。所开发的方法将使其他人能够对现有的地球系统数据进行时空评估，这是现有方法所不可能做到的。将数据科学技术应用于一个新的领域将需要开发以时空4D天气雷达数据为重点的新技术。

项目成果

Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning

• DOI: 10.1175/bams-d-18-0195.1
• 发表时间: 2019-11-01
• 期刊: BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
• 影响因子: 8
• 作者: McGovern, Amy;Lagerquist, Ryan;Smith, Travis
• 通讯作者: Smith, Travis