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Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including adaptability to climate change, electric grid(More)
The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate,(More)
To discover patterns in historical data, climate scientists have applied various clustering methods with the goal of identifying regions that share some common climatological behavior. However, past approaches are limited by the fact that they either consider only a single time period (snapshot) of multivariate data, or they consider only a single variable(More)
[1] Analyses of climate model simulations and observations reveal that extreme cold events are likely to persist across each land‐continent even under 21st‐century warming scenarios. The grid‐based intensity, duration and frequency of cold extreme events are calculated annually through three indices: the coldest annual consecutive three‐day average of daily(More)
Generating credible climate change and extremes projections remains a high-priority challenge, especially since recent observed emissions are above the worst-case scenario. Bias and uncertainty analyses of ensemble simulations from a global earth systems model show increased warming and more intense heat waves combined with greater uncertainty and large(More)
[1] Recent research on the projection of precipitation extremes has either focused on conceptual physical mechanisms that generate heavy precipitation or rigorous statistical methods that extrapolate tail behavior. However, informing both climate prediction and impact assessment requires concurrent physically and statistically oriented analysis. A combined(More)
The design of statistical predictive models for climate data gives rise to some unique challenges due to the high dimensionality and spatio-temporal nature of the datasets, which dictate that models should exhibit parsimony in variable selection. Recently, a class of methods which promote structured sparsity in the model have been developed, which is(More)
Ultrascale computing and high-throughput experimental technologies have enabled the production of scientific data about complex natural phenomena. With this opportunity, comes a new problem – the massive quantities of data so produced. Answers to fundamental questions about the nature of those phenomena remain largely hidden in the produced data. The goal(More)
We present a fast and statistically principled approach for land cover change detection. The approach is illustrated with a geographic application that involves analyzing remotely sensed data to detect changes in the normalized difference vegetation index (NDVI) in near real time. We use the Wal-Mart land cover change data set as a nontra-ditional way to(More)