Collaborative Research: Scalable Statistical Validation and Uncertainty Quantification for Large Spatio-Temporal Datasets

合作研究：大型时空数据集的可扩展统计验证和不确定性量化

• 批准号: 1417857
• 负责人: Douglas Nychka
• 金额: $ 7.51万
• 依托单位: University Corporation For Atmospheric Res
• 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: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1417857&HistoricalAwards=false
• 关键词: Collaborative; Research; Scalable; Statistical; Validation

Computer simulations, satellites and various other technological advances have paved the way into unprecedented scientific territory by generating volumes of previously uncollectable datasets. Properly utilizing these new data products to promote scientific discovery requires that they, first, be (i) validated and (ii) paired with an appropriate measure of uncertainty. Validating a data product entails comparing "synthetic" data (e.g., computer model simulations, remote sensing measurements, or statistical predictions) with observational counterparts to substantiate the digital data for its use in scientific discovery. Uncertainty quantification (UQ) is a necessary component to validation and entails accounting for and stating the uncertainties associated with scientific conclusions derived from digital or observational data. The purpose of this research is to promote scientific discovery using digital data products by developing statistical methods to perform validation and uncertainty quantification.Given the strong need to perform, and the substantial challenges facing, statistical validation and UQ, this research will (i) develop new validation strategies for simulated and digital datasets based on scientifically motivated features; (ii) develop multivariate spatio-temporal statistical models that can be used to implement, and perform UQ for spatio-temporal data products; and, (iii) develop scalable computation techniques for fitting the developed spatio-temporal statistical models. This research will implement these techniques on data products in atmospheric, agricultural and environmental sciences to facilitate their use in scientific inquiry.

计算机模拟、卫星和各种其他技术进步通过生成大量以前无法收集的数据集，为前所未有的科学领域铺平了道路。 要正确利用这些新数据产品来促进科学发现，首先需要对它们进行 (i) 验证，以及 (ii) 与适当的不确定性度量相匹配。 验证数据产品需要将“合成”数据（例如计算机模型模拟、遥感测量或统计预测）与观测对应数据进行比较，以证实数字数据在科学发现中的用途。 不确定性量化 (UQ) 是验证的必要组成部分，需要解释和说明与从数字或观测数据得出的科学结论相关的不确定性。 本研究的目的是通过开发统计方法来执行验证和不确定性量化，以促进使用数字数据产品的科学发现。鉴于执行统计验证和 UQ 的强烈需求以及面临的重大挑战，本研究将 (i) 基于科学动机特征为模拟和数字数据集开发新的验证策略； (ii) 开发可用于实施和执行时空数据产品的 UQ 的多元时空统计模型； (iii) 开发可扩展的计算技术来拟合已开发的时空统计模型。 这项研究将在大气、农业和环境科学的数据产品上实施这些技术，以促进它们在科学探究中的使用。