6 citations · 10 across the 9 of their papers we have counts for
10 papers
Covariance-Informed Ensemble Score Filtering: Embedding Localized Covariance Geometry into Score-Based Transport
Siming Liang, Peter Jan van Leeuwen, Hristo G. Chipilski +3
Background-error covariance models are central to atmospheric data assimilation because they spread partial observational information across space, variables, and dynamically coupl…
From Variational Optimization to Flow-Based Transport: Posterior-Geometry Regularization for Ill-Conditioned Data Assimilation
Siming Liang, Feng Bao, Hristo G. Chipilski +2
Variational data assimilation computes a Bayesian update through optimization on a fixed posterior geometry, whose conditioning is determined by the prior covariance and the observ…
A Two-Step Ensemble Score Filter for Data Assimilation in Partially Observed Systems
Zixiang Xiong, Feng Bao, Hristo G. Chipilski +3
Data assimilation blends model forecasts with observations to estimate the evolving state of complex dynamical systems, but sparse observing networks remain challenging because uno…
Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction
Xiao Wang, Zezhong Zhang, Isaac Lyngaas +10
Accurate Earth system prediction requires state inference from incomplete observations, but conventional two-stage data assimilation (DA) is computationally prohibitive because rep…
Two Localization Strategies for Sequential MCMC Data Assimilation with Applications to Nonlinear Non-Gaussian Geophysical Models
Hamza Ruzayqat, Hristo G. Chipilski, Omar Knio
We present a localized data assimilation (DA) scheme based on sequential Markov Chain Monte Carlo (SMCMC) \cite{smcmc}, a provably convergent method for filtering high-dimensional,…
On the sensitivity of different ensemble filters to the type of assimilated observation networks
Zixiang Xiong, Siming Liang, Feng Bao +2
Recent advances in data assimilation (DA) have focused on developing more flexible approaches that can better accommodate nonlinearities in models and observations. However, it rem…
Ensemble score filter with image inpainting for data assimilation in tracking surface quasi-geostrophic dynamics with partial observations
Siming Liang, Hoang Tran, Feng Bao +3
Data assimilation plays a pivotal role in understanding and predicting turbulent systems within geoscience and weather forecasting, where data assimilation is used to address three…
A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics
Junqi Yin, Siming Liang, Siyan Liu +4
The weather and climate domains are undergoing a significant transformation thanks to advances in AI-based foundation models such as FourCastNet, GraphCast, ClimaX and Pangu-Weathe…
Nonlinear ensemble filtering with diffusion models: Application to the surface quasi-geostrophic dynamics
Feng Bao, Hristo G. Chipilski, Siming Liang +2
The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here we explore another promi…
Exact nonlinear state estimation
Hristo G. Chipilski
The majority of data assimilation (DA) methods in the geosciences are based on Gaussian assumptions. While these assumptions facilitate efficient algorithms, they cause analysis bi…