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Commonly used dependence measures, such as linear correlation, cross-correlogram, or Kendall's tau , cannot capture the complete dependence structure in data unless the structure is restricted to linear, periodic, or monotonic. Mutual information (MI) has been frequently utilized for capturing the complete dependence structure including nonlinear(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)
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)
[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 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)
A systematic characterization of multivariate dependence at multiple spatio-temporal scales is critical to understanding climate system dynamics and improving pre-dictive ability from models and data. However, dependence structures in climate are complex due to nonlinear dynamical generating processes, long-range spatial and long-memory temporal(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 timing and strength of wind-driven coastal upwelling along the eastern margins of major ocean basins regulate the productivity of critical fisheries and marine ecosystems by bringing deep and nutrient-rich waters to the sunlit surface, where photosynthesis can occur. How coastal upwelling regimes might change in a warming climate is therefore a question(More)
The concept of offshoring of professional services first gained attention slightly over 25 years ago. At that time, US companies began to realize the cost-advantage of getting their computer software developed in India and other countries. The concept gained momentum with the advent of Internet and the availability of inexpensive communication technologies.(More)