Data Assimilation for Discontinuous State Variables
Data assimilation is a method for combining available observations with a background from numerical model, to find the best estimate of the system, which is crucial for improving environmental variable prediction. However, commonly used Gaussian distribution assumption could introduce biases for state variables with discontinuous profiles, such as sea ice thickness with sharp features. In this talk, we focus on the design of non-Gaussian prior based on various statistics of the state variables.