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. In particular, we adopt a covariance matrix, which is designed using the gradient information of the state variable, for a prior distribution in the data assimilation framework. This method is computationally efficient and is flexible to be applied in various data assimilation algorithms.

Thackeray Hall 427