Title: Multi-scale data assimilation for fine-resolution models

Author: Zhijin Li (Jet Propulsion Laboratory, California Institute of Technology)

The commonly used data assimilation algorithms are based on optimal estimation theory, in which error covariance is of fundamental importance. It is shown that the standard optimal estimation algorithm is inherently ineffective when it is applied to fine resolution models, and the ineffectiveness arises from its filtering properties. We propose a multi-scale data assimilation algorithm, in which the cost function is decomposed for a set of distinct spatial scales. Data assimilation is implemented sequentially from large to fine scales. Results are presented to demonstrate the algorithm.

GMAO Head: Steven Pawson Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: Feb 9 2015