Auroop R. Ganguly

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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)
Part of Special Issue " Nonlinear deterministic dynamics in hydrologic systems: present activities and future challenges " Abstract. The ability to detect the chaotic signal from a finite time series observation of hydrologic systems is addressed in this paper. The presence of random and seasonal components in hydrological time series, like rainfall or(More)
The benefits of short-term (1–6 h), distributed quantitative precipitation forecasts (DQPFs) are well known. However, this area is acknowledged to be one of the most challenging in hydrometeorology. Previous studies suggest that the ''state of the art'' methods can be enhanced by exploiting relevant information from radar and numerical weather prediction(More)
The detection of unusual profiles or anomalous behavioral characteristics from sensor data is especially complicated in security applications where the threat indicators may or may not be known in advance. Predictive modeling of massive volumes of historical data can yield insights on usual or baseline profiles, which in turn can be utilized to isolate(More)
Knowledge discovery from temporal, spatial and spatiotemporal data is critical for climate change science and climate impacts. Climate statistics is a mature area. However, recent growth in observations and model outputs, combined with the increased availability of geographical data, presents new opportunities for data miners. This paper maps 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)
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)
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)