Statistical Analysis of Massive Spatio-Temporal Datasets Using Distributed Computing

卫星和飞机上的自动传感仪器使收集大量在空间和时间上编制索引的数据成为可能。如果能够有效地利用这些类型的数据集，它们可以为各种问题提供新的见解，例如气候的温室气体浓度，精准农业的土壤属性，以及天气预报的大气状态。然而，传统的空间统计技术在计算上对于大数据集是不可行的。该项目将开发快速和用户友好的软件，可以填补空白，捕捉从非常精细到非常大的不均匀空间结构，并适当地量化不确定性。作为例证，该软件将应用于对恶劣天气预报至关重要的数百万小时总可降水量的卫星测量。该项目的目标是开发用于统计分析海量、高分辨率空间数据集的方法。所采用的模型是根据多分辨率的空间基函数来指定的。选择基函数来最佳地逼近给定的协方差函数。不需要对协方差函数进行限制，观测值可以不规则地间隔。基函数表示的结构导致可扩展的并行推理算法，从而能够充分利用现代计算环境中的许多节点，这是至关重要的。该方法将扩展到时空数据的分析，允许实时分析海量、流动的时空数据集。所有方法都将在适用于多核台式计算机和超级计算环境的软件中实施。

Automated sensing instruments on satellites and aircraft have enabled the collection of massive amounts of data indexed in space and time. If these kinds of datasets can be efficiently exploited, they can provide new insights on a wide variety of issues, such as greenhouse gas concentrations for climate, soil properties for precision agriculture, and atmospheric states for weather forecasting. However, traditional spatial-statistical techniques are not computationally feasible for big datasets. This project will develop fast and user-friendly software that can fill gaps, capture inhomogeneous spatial structure from very fine to very large scales, and properly quantify uncertainty. As an illustration, the software will be applied to millions of satellite measurements of hourly Total Precipitable Water fields, which are critical in severe weather forecasting.The goal of this project is to develop methodology for the statistical analysis of massive, high-resolution spatial datasets. The employed model is specified in terms of spatial basis functions at multiple resolutions. The basis functions are chosen to optimally approximate a given covariance function. No restrictions on the covariance function are necessary, and observations can be irregularly spaced. It is crucial that the structure of the basis-function representation results in scalable, parallel inference algorithms that can take full advantage of the many nodes available in modern computing environments. The methodology will be extended to the analysis of spatio-temporal data, allowing real-time analysis of massive, streaming spatio-temporal datasets. All methods will be implemented in software suitable for both multi-core desktop computers and supercomputing environments.