- Sponsor:
- sighpc
Numerical simulation based science follows a new paradigm: its knowledge discovery process rests upon massive amounts of data. Climate scientists generate data faster than can be interpreted and need to prepare for further exponential data increases. Current analysis approaches are primarily focused on traditional methods, best suited for large-scale phenomena and coarseresolution data sets. Tools that employ a combination of high-performance analytics, with algorithms motivated by network science, nonlinear dynamics and statistics, as well as data mining and machine learning, could provide unique insights into challenging features of the Earth system, including extreme events and chaotic regimes. The breakthroughs needed to address these challenges will come from collaborative efforts involving several disciplines, including end-user scientists, computer and computational scientists, computing engineers, and mathematicians.
The SC11 Climate Knowledge Discovery workshop at Supercomputing '11 brought together experts from various domains to investigate the use and application of large-scale graph analytics, semantic technologies and knowledge discovery algorithms in climate science. The workshop was the second in a series of planned workshops, the first workshop being held in Hamburg in March 2011.
The goal of the workshop was to provide more than a broad overview of the topic, but establish a platform for the ongoing discussions in this field. It was designed to be highly interactive to concentrate on the challenges facing the climate community: "What steps are required to realize the potential of data analytics, semantic technologies and knowledge discovery algorithms in climate science? Where methods and technologies already exist, how can they be leveraged? Where gaps are identified, what steps must be taken to address them?"
Proceeding Downloads
Knowledge discovery with networks for climate science: questions and answers from ckd hamburg
The use of complex networks has been motivated in climate to understand attributes of large-scale dynamics, for example, correlations between variables and relations among climate oscillators. This talk addresses two specific questions that arose from ...
Basics and visual analytics of climate networks
This paper/talk discusses potentials of using visual analytics technology for climate network analysis. It presents basic definitions, challenges arising in this field, suitable methods & tools and key references.
Probabilistic graphical models for climate data analysis
The prominence and usage of probabilistic graphical models for data analysis have increased substantially over the past decade. Unlike traditional models in statistical machine learning, graphical models capture statistical dependencies between ...
Meeting the challenges of data-intensive science
Meeting the needs of Data-Intensive Science requires addressing many challenges in the entire life cycle of data including analysis, exploitation and dissemination. The broadening range of data sources often used in multi-discipline and multi-scale ...
The transformational capabilities offered by ontologies in climate science
Climate science has long discovered the benefits of standardization of formats to support discovery, analysis and comparison of climate results. Having started at a time when hierarchical metadata models where state of the art, much of their standards, ...
- Proceedings of the 2011 workshop on Climate knowledge discovery