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This study presents a scalable and robust approach to spatial downscaling in the context of climate downscaling. We explore the ability of four techniques to downscale a climate variable to a given location of interest. As an example, we focus on downscaling daily mean air temperature at twelve stations located across the topographically complex province of British Columbia, Canada. The techniques...
Deep learning techniques have been successfully applied to solve many problems in climate and geoscience using massive-scaled observed and modeled data. For extreme climate event detections, several models based on deep neural networks have been recently proposed and attend superior performance that overshadows all previous handcrafted expert based method. The issue arising, though, is that accurate...
Renewable source of energy has lots of relevance today. Solar energy is available in abundance and it is preferable because it is pollution free and there is no cost attached to it. However solar energy is not reliable in nature because its production depends upon weather condition, time of the day and other local parameters. If a consumer have accurate prediction of solar power, the trade-off with...
In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show these features in practice,...
The goal of this work is to present a software package which is able to process binary climate data through spawning Map-Reduce tasks while introducing minimum computational overhead and without modifying existing application code. The package is formed by the combination of two tools, Pipistrello, a Java utility that allows users to execute Map-Reduce tasks over any kind of binary file, Tina a lightweight...
Climate Model Diagnostic Analyzer (CMDA) is a web-based information system designed for the climate modeling and model analysis community to analyze climate data from models and observations. CMDA provides tools to diagnostically analyze climate data for model validation and improvement, and to systematically manage analysis provenance for sharing results with other investigators. CMDA utilizes cloud...
This paper introduces a methodology that allows investigating the impact of Information and Communication Technologies (ICTs) implemented within a Smart Grid framework. Such implementations are of imperative importance to overcome the current needs for increased network utilization and connection of renewable energy sources. Common applications include Flexible AC transmission systems (FACTS) and...
This paper presents a novel multi-task learning framework for the accurate prediction of spatio-temporal data at multiple locations. The framework encodes the data as a third-order tensor and performs supervised tensor decomposition to identify the latent factors that capture the inherent spatiotemporal variabilities of the data and their relationship to the target variable of interest. The framework...
Large data (over Terabyte) are produced by ultra high-resolution Earth science simulations with a long period of time. This creates a challenge to distribute and analyze in an effective, efficient, and scalable way. One key reason is that typical Earth science data are represented in NetCDF, which is not supported by the popular and powerful Hadoop Distribute File System (HDFS) and consequently cannot...
We have developed a new parallel Python tool for the standardization of Earth System Model (ESM) data for publication as part of Model Intercomparison Projects (MIPs). It was specifically designed to aid Community Earth System Model (CESM) scientists at the National Center for Atmospheric Research (NCAR) in preparation for the Coupled Model Intercomparison Project, Phase 6 (CMIP6), expected to start...
The Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is establishing an adaptive data services and operations architecture in support of the Next-Generation ARM Facility as explained in its Decadal Vision. In this paper, we describe the capabilities of the ARM Data Center (ADC) and the upcoming high-performance computing infrastructure in support of this...
Intelligent Transportation Systems are an important aspect of our life and are going to become ubiquitous in the near future. Traffic flow prediction is a key component of any Intelligent Transportation Systems. This report uses Artificial Neural Network based models to predict short term traffic flow. Two new input parameters; temperature and truck flow has been introduced into a multi input parameters...
In order to understand the hydrologie changes in the watersheds due to climate changes, the EPSCoR jurisdictions of Idaho, Nevada, and New Mexico have collaborated to create the Western Consortium for Watershed Analysis, Visualization and Exploration (WC-WAVE). WC-WAVE will create a Virtual Watershed Platform (VWP) framework for assisting watershed scientists in their research. This software environment...
The data volume of many scientific applications has substantially increased in the past decade and continues to increase due to the rising needs of high-resolution and fine- granularity scientific discovery. The data movement between stor- age and compute nodes has become a critical performance factor and has attracted intense research and development attention in recent years. In this paper, we propose...
This paper presents the ongoing research on large data transfers for scientific applications (e.g. bioinformatics) within singledomain software-defined networks. We identify the key requirements for performance improvements of data transfers, and briefly describe our approach. While the initial results seem promising, we identify next research steps to the problem at hand.
General circulation models (GCMs) are used for estimating future climate scenarios, run on a very coarse scale, so the outputs from GCMs need to be downscaled to obtain a finer spatial resolution. This paper provides a methodology for GCM-Ensembles performance evaluation using a GIS platform by applying statistical spatial downscaling methods. Statistical downscaling methods were used in the projection...
Understanding the impact of global climate change on the world's ecosystem is critical to society at large and represents a significant challenge to researchers in the climate community. One important piece of the climate puzzle is how the dynamics of large-scale ice sheets, such as those covering Greenland and Antarctica, will respond to a warming climate. Relatively recently, glaciologists have...
In this paper, we describe an emergent tool called DAWN (short for "Distributed Analytics, Workflows and Numeric") which is a model for simulating, analyzing and optimizing system architectures for executing arbitrary data processing pipelines. As an example, we will apply DAWN to the investigation of a real-life Big Data use case in climate science: the evaluation of simulated rainfall...
Big-data systems are increasingly important for solving the data-driven problems in many science domains including geosciences. However, existing big-data systems cannot support the self-describing data formats such as NetCDF which are commonly used by scientific communities for data distribution and sharing. This limitation presents a serious hurdle to the further adoption of big-data systems by...
This article describes the Earth System Grid Federation (ESGF) mission and an international integration strategy for data, database and computational architecture, and stable infrastructure highlighted by the authors (the ESGF Executive Committee). These highlights are key developments needed over the next five to seven years in response to large-scale national and international climate community...
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