Currently, the analysis of large data collections from earth science research is executed through traditional computational and data analysis approaches, which require users to bring data to their desktops and perform local data analysis. Future earth science remote sensing missions, which historically assume that all data can be collected, transmitted, processed, and archived, may not scale as more capable instruments stress existing architectural approaches and systems. A new paradigm is needed in order to increase the productivity and effectiveness of scientific data analysis. This paradigm must recognize that architectural and analytical choices are interrelated, and must be carefully coordinated in any system that aims to allow efficient, interactive scientific exploration and discovery to exploit massive data collections, from point of collection (e.g., onboard) to analysis and decision support. Both future observational systems, including satellite and airborne experiments, and research in climate modeling will significantly increase the size of the data requiring new approaches across the entire data lifecycle from capture to generation, management, and analysis of the data.

This workshop seeks computational and data science experts to present on their research and discuss Big Data roadmaps, architectures, technologies, and methodologies for future Earth Science data challenges emerging from both observational systems and climate studies.

Complete details about the workshop are available.