ECCO Summer School
May 19-31 2019
Ocean state estimation requires expertise in diverse subjects including estimation theory, numerical modeling, and observational oceanography. The individual subject matters are often treated separately, often leading to misunderstandings about model-data synthesis. The Summer School will examine these subjects in a coherent manner for the students to gain a working understanding of ocean state estimation and its use in ocean research. Topics under estimation will include inverse theory, optimization, Kalman filtering and related smoothing, adjoint method, and algorithmic differentiation (AD) tools. Principles of numerical modeling will be described using the MITgcm as an example. Advanced modeling algorithms and concepts, especially those employed in the ECCO Central Estimate, will be treated including mixing schemes, coordinate systems, and boundary conditions. Practical issues will be discussed such as the basics of model parallelization and the nature of model errors. Different observing systems will be described, focusing on their complementary nature and impact on state estimation. Sources of data error will be examined and issues of data reduction will be discussed. A range of analysis tools developed within the ECCO consortium will be introduced through tutorials and projects.
Algae Bloom in the Barents Sea (GSFC/NASA Earth Observatory)
The school introduces the tools and mathematics of ocean state and parameter estimation and its application to ocean science through a mix of foundational lectures, hands-on tutorials, and projects. In doing so, the school aims to help nurture the next generation of oceanographers and climate scientists in the subject matter so that they may utilize the ECCO products and underlying modeling/estimation tools most effectively to further advance the state-of-the-art in ocean state estimation and ocean science.
Swirls in the East Greenland Current (GSFC/NASA Earth Observatory)
Argo Float Deployment (Argo Science Team)
Data assimilation (global & regional); state & parameter estimation; learning from observations and models; adjoint method; sensitivity analysis; algorithmic differentiation; ocean modeling; ocean dynamics and variability; ocean's role in climate; global ocean observing system (satellite an in-situ observations); physics of sea level; ocean mixing; sea ice dynamics; ice sheet-ocean interactions; ice shelf dynamics; ocean tides; cyberinfrastructure & data analytics; diversity and inclusion in oceanography.