Shaping the Development of EarthCube to Enable Advances in Data Assimilation and Ensemble Prediction Workshop; Boulder, Colorado; December 17-18, 2012

University Corporation For Atmospheric Res, Boulder CO

Investigators

Abstract

Intellectual Merit: Data intensive science has rapidly emerged as the Fourth Paradigm of scientific discovery after empirical, theoretical, and computational methods. This is particularly true in the area of data assimilation and ensemble prediction. Yet, significant barriers exist in using the data efficiently or integrating them into data assimilation or ensemble prediction systems as the scientific community lacks easy-to-use common cyberinfrastructure frameworks. By some estimates, researchers may spend 80 percent of their time dealing with data discovery, access, and processing, and only 20 percent "doing science" by way of interpretation, synthesis, and knowledge creation. The goal of the National Science Foundation's EarthCube initiative is to transform the conduct of research by supporting the development of community-guided cyberinfrastructure. It is critical that EarthCube is both shaped by as well as benefits the different scientific communities to which it is targeted. This project will fund a workshop to bring the research, education, and information technology communities together to discuss some of the science, technology and cyberinfrastructure issues related to distributed but shared mesoscale modeling, data assimilation, and ensemble prediction. The title of the workshop, which is planned to be held 17-18 December 2012 in Boulder, CO, is "Shaping the Development of EarthCube to Enable Advances in Data Assimilation and Ensemble Prediction." One of the goals of the workshop is to shape the development of EarthCube and help in building a cyberinfrastructure and work toward a scientific ecosystem in which "data friction" is reduced, and data transparency and ease-of-use are significantly increased. We believe achieving the workshop goals will help mesoscale ensemble prediction and data assimilation communities to work toward a transformation in the conduct of data-centric research and education. To that end, we would like to assemble a team from across the country to develop a multi-institutional, multi-model, multi-data-assimilation regional scale ensemble prediction and analysis system that is capable of real-time forecasts, as well as historical reanalysis. It is anticipated that workshop participants will come from U. S. universities, NCAR and UCAR, NOAA, NSF, and other research organizations. Broad Impacts: There is an urgent need for educating and training the next generation of students in mesoscale modeling, data assimilation, ensemble and probabilistic forecasting in the United States. The workshop will engage students pursuing careers in the aforementioned three areas of research. It is envisioned that the products from the planned data assimilation and ensemble prediction systems will be readily accessed by a broad community of university researchers, students and educators for exploring dynamics, physics of the atmosphere, as well as for educating the next generation of students to gain knowledge and expertise in advanced numerical weather prediction topics. The real-time ensemble prediction system can be used as a complementary tool by operational forecasters, especially in terms of probabilistic forecasting of severe weather and tropical cyclones. Finally, this workshop will help build capacity among the community of researchers and users of ensemble prediction and data assimilation and will foster further collaborative efforts to advance research in mesoscale meteorology.