Seminar: Introducing NEDAS: a Light-weight and Scalable Python Solution for Ensemble Data Assimilation

Speaker: Yue (Michael) Ying, Nansen Environmental and Remote Sensing Center (NERSC)

Abstract

The Next-generation Ensemble Data Assimilation System (NEDAS) provides a light-weight Python solution for implementing ensemble data assimilation methods for geophysical models. Thanks to its modular and scalable design, a wide range of ensemble assimilation algorithms become feasible for large-dimensional models. NEDAS provides a collection of state-of-the-art algorithms from existing research software using two main strategies: (1) serial assimilation where observations are used one at a time to update the model state iteratively, (2) batch assimilation where observations are in a local analysis for each model state. One can test new algorithmic ideas in NEDAS and compare them with existing methods early-on before committing resources to full implementation in a real operational setting. In this talk, I’ll describe the architectural design of NEDAS, highlight some new algorithmic ideas, and show some computational benchmarks as a guidance for picking the right algorithm in different application scenarios.

Biography

Yue Ying, aka Michael, is currently working as a DA researcher at the Nansen Environmental and Remote Sensing Center in Norway. He had his PhD in meteorology in PennState studying the predictability of multiscale tropical weather systems, then invented a multiscale alignment” approach to tackle the nonlinearity in DA problems. Now, he continues to develop new DA methods and apply them to the modeling of the Arctic systems.

For employees, add this event to your calendar. For invitation, contact kpierri@ucar.edu.