Global climate data accumulate daily across a range of information systems. As international efforts to create and maintain federated ecosystems evolve, so too does the need for robust infrastructure support. For two decades, the Analytics and Informatics Management Systems (AIMS) project has established LLNL as a leader and visionary architect for climate model data management and analytics systems in the era of big data.
By working with others in a global setting, we are resolving many of the technical challenges of scaling and federation (e.g., authentication, sharing, location of data and processing resources, interface standards)—issues that face any attempted large-scale information system. Key objectives:
- Strategy. Support the Department of Energy’s Offices of Biological and Environmental Research and Advance Scientific Computing Research missions and strategic Big Data vision.
- Discovery. Ensure technical and developmental integrity of a software infrastructure and data ecosystem to support scientific discovery.
- Technology. Anticipate cutting-edge technologies and their use within a supported federated ecosystem.
- Expertise. Enable the most qualified scientists and software engineers to enhance their skills and expertise through interagency proposals and external projects.
- Collaboration. Facilitate scientific collaborations with academia, industry, and other national and international government laboratories and agencies.
Climate science brings together many disciplines beyond atmospheric, ocean, land, and other Earth sciences, including data analysis, computational mathematics, software architecture, and network engineering. As data science transforms to accommodate climate applications, the AIMS program is positioned at the forefront of the climate science community.
Our program plays a significant role in the integration of data-driven infrastructure and analysis with science research and discoveries. We help researchers around the world better organize and integrate climate knowledge via a cooperative federation.
- Distributed Resources for the Earth System Grid Federation Advanced Management (DREAM): access to large data sets across multiple facilities for improved research efforts and numerous data-intensive applications
- Energy Exascale Earth System Model (E3SM): a state-of-the-science, computationally advanced global climate and Earth system model addressing challenges posed by the interactions of weather-climate scale variability with energy and related sectors
- Earth System Grid Federation (ESGF): a peer-to-peer enterprise collaboration that develops, deploys, and maintains software infrastructure for the management, dissemination, and analysis of model output and observational data
- Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT): enables analysis, diagnosis, and visualization of data for atmosphere, ocean, and land model components, and other impact studies