Tootfinder

An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
I. Dau\v{z}ickait\.e, M. A. Freitag, S. G\"urol, A. S. Lawless, A. Ramage, J. A. Scott, J. M. Tabeart
https://arxiv.org/abs/2506.09211

An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
Variational data assimilation is a technique for combining measured data with dynamical models. It is a key component of Earth system state estimation and is commonly used in weather and ocean forecasting. The approach involves a large-scale generalized nonlinear least-squares problem. Solving the resulting sequence of sparse linear subproblems requires the use of sophisticated numerical linear algebra methods. In practical applications, the computational demands severely limit the number of it…

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu, Ruoying He, Ashesh Chattopadhyay
https://arxiv.org/abs/2507.06479

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence under such constraints. We leverage a deep learning framework that combines neural…

Capability demonstration of a JEDI-based system for TEMPO assimilation: system description and evaluation
Maryam Abdi-Oskouei, J\'er\^ome Barr\'e
https://arxiv.org/abs/2506.07321

Capability demonstration of a JEDI-based system for TEMPO assimilation: system description and evaluation
The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high-frequency, geostationary observations of column NO2 across North America. In this study, we present the first implementation of a TEMPO NO2 data assimilation system using the Joint Effort for Data assimilation Integration (JEDI) framework. Leveraging a four-dimensional ensemble variational (4DEnVar) approach and an Ensemble of Data Assimilations …

This https://arxiv.org/abs/2501.16642 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems. Model-driven methods (e.g., Kalman, particle) presuppose full knowledge of the true dynamics, which is not always satisfied in practice, while purely data-driven solvers learn a deterministic mapping between observations and states and therefore miss the intrinsic stochasticity of real processes. Recently, score-based diffusion models learn a global diffusion prior and provide a goo…

A localized particle filter for geophysical data assimilation
Dan Crisan, Eliana Fausti
https://arxiv.org/abs/2507.07103 https://arxi…

A localized particle filter for geophysical data assimilation
Particle filters are computational techniques for estimating the state of dynamical systems by integrating observational data with model predictions. This work introduces a class of Localized Particle Filters (LPFs) that exploit spatial localization to reduce computational costs and mitigate particle degeneracy in high-dimensional systems. By partitioning the state space into smaller regions and performing particle weight updates and resampling separately within each region, these filters lever…

Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics
Paul Jeanney, Ashton Hetherington, Shady E. Ahmed, David Lanceta, Susana Saiz, Jos\'e Miguel Perez, Soledad Le CLainche
https://arxiv.org/abs/2507.00539

Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics
This paper presents an innovative Reduced-Order Model (ROM) for merging experimental and simulation data using Data Assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach implementing the Ensemble Kalman Filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the prop…

This https://arxiv.org/abs/2407.11746 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Sparse data assimilation for under-resolved large-eddy simulations
The need for accurate and fast scale-resolving simulations of fluid flows, where turbulent dispersion is a crucial physical feature, is evident. Large-eddy simulations (LES) are computationally more affordable than direct numerical simulations, but their accuracy depends on sub-grid scale models and the quality of the computational mesh. In order to compensate related errors, a data assimilation approach for LES is devised in this work. The presented method is based on variational assimilatio…

This https://arxiv.org/abs/2303.00847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Analysis of Four-Dimensional Variational Data Assimilation Problems in Low Regularity Spaces
We carry out a rigorous analysis of four-dimensional variational data assimilation ($4D$-VAR) problems for linear and semilinear parabolic partial differential equations. Continuity of the state with respect to the spatial variable is required since pointwise observations of the state variable appear in the cost functional. Using maximal parabolic regularity tools, we prove this regularity for initial conditions with $L^β$-regularity guaranteed by control constraints, rather than Sobolev regul…

Convolutional method for data assimilation An improved method on neuronal electrophysiological data
Dawei Li, Henry D. I. Abarbanel
https://arxiv.org/abs/2506.11365

Convolutional method for data assimilation An improved method on neuronal electrophysiological data
We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. While conventional methods rely on nudging terms and pointwise deviation metrics, they often fail to account for spike timing precision, a key feature in neural signals. Our approach applies a Gaussian convolution to both measured data and model estimates, enabling a cost function that evaluates both amplitude and timin…

Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material
Guanjin Wang, Xiangxue Zhao, Shapour Azarm, Balakumar Balachandran
https://arxiv.org/abs/2506.10875

Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material
An alternative data-driven modeling approach has been proposed and employed to gain fundamental insights into robot motion interaction with granular terrain at certain length scales. The approach is based on an integration of dimension reduction (Sequentially Truncated Higher-Order Singular Value Decomposition), surrogate modeling (Gaussian Process), and data assimilation techniques (Reduced Order Particle Filter). This approach can be used online and is based on offline data, obtained from the…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space
Hang Fan, Lei Bai, Ben Fei, Yi Xiao, Kun Chen, Yubao Liu, Yongquan Qu, F…

This https://arxiv.org/abs/2411.13443 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Nonlinear Assimilation via Score-based Sequential Langevin Sampling
This paper presents score-based sequential Langevin sampling (SSLS), a novel approach to nonlinear data assimilation within a recursive Bayesian filtering framework. The proposed method decomposes the assimilation process into alternating prediction and update steps, leveraging dynamic models for state prediction while incorporating observational data through score-based Langevin Monte Carlo during updates. To address challenges in posterior sampling, we introduce an annealing strategy within t…

Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation
Ruiqi Shu, Yuan Gao, Hao Wu, Ruijian Gou, Yanfei Xiang, Fan Xu, Qingsong Wen, Xian Wu, Xiaomeng Huang
https://arxiv.org/abs/2505.22071

Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation
This work focuses on the end-to-end forecast of global extreme marine heatwaves (MHWs), which are unusually warm sea surface temperature events with profound impacts on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study, to address these issues, based on the physical nature of MHWs, we created a novel hybrid data-driven and numerical MH…

This https://arxiv.org/abs/2303.00847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Analysis of Four-Dimensional Variational Data Assimilation Problems in Low Regularity Spaces
We carry out a rigorous analysis of four-dimensional variational data assimilation ($4D$-VAR) problems for linear and semilinear parabolic partial differential equations. Continuity of the state with respect to the spatial variable is required since pointwise observations of the state variable appear in the cost functional. Using maximal parabolic regularity tools, we prove this regularity for initial conditions with $L^β$-regularity guaranteed by control constraints, rather than Sobolev regul…

Using Diffusion Models to do Data Assimilation
Daniel Hodyss, Matthias Morzfeld
https://arxiv.org/abs/2506.02249 https://arxiv.org/pd…

Using Diffusion Models to do Data Assimilation
The recent surge in machine learning (ML) methods for geophysical modeling has raised the question of how these same ML methods might be applied to data assimilation (DA). We focus on diffusion modeling (a form of generative artificial intelligence) and on systems that can perform the entire DA, rather than on ML-based tools that are used within an otherwise conventional DA system. We explain that there are (at least) three different types of diffusion-based DA systems and we show in detail tha…

Modular data assimilation for flow prediction
Aytekin \c{C}{\i}b{\i}k, Rui Fang, William Layton
https://arxiv.org/abs/2506.19002 https://

Modular data assimilation for flow prediction
This report develops several modular, 2-step realizations (inspired by Kalman filter algorithms) of nudging-based data assimilation \begin{equation*} \begin{array}{cc} Step{\text { }}1 & \begin{array}{c} \frac{\widetilde {v}^{n+1}-v^{n}}{k}+v^{n}\cdot \nabla \widetilde {v}% ^{n+1}-ν\triangle \widetilde {v}^{n+1}+\nabla q^{n+1}=f(x){\text { }% } \\ \nabla \cdot \widetilde {v}^{n+1}=0% \end{array} \\ Step{\text { }}2 & \frac{v^{n+1}-\widetilde {v}^{n+1}}{k}-χI_{H}(u(t^{n+1})-v^{n+1})=0.% \end{a…

Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Xiantao Fan, Xinyang Liu, Meng Wang, Jian-Xun Wang
https://arxiv.org/abs/2505.23940

Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Turbulent flows and fluid-structure interactions (FSI) are ubiquitous in scientific and engineering applications, but their accurate and efficient simulation remains a major challenge due to strong nonlinearities, multiscale interactions, and high computational demands. Traditional CFD solvers, though effective, struggle with scalability and adaptability for tasks such as inverse modeling, optimization, and data assimilation. Recent advances in machine learning (ML) have inspired hybrid modelin…

This https://arxiv.org/abs/2506.03947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Block Alpha-Circulant Preconditioners for All-at-Once Diffusion-Based Covariance Operators
Covariance matrices are central to data assimilation and inverse methods derived from statistical estimation theory. Previous work has considered the application of an all-at-once diffusion-based representation of a covariance matrix operator in order to exploit inherent parallellism in the underlying problem. In this paper, we provide practical methods to apply block $α$-circulant preconditioners to the all-at-once system for the case where the main diffusion operation matrix cannot be readil…

Align-DA: Align Score-based Atmospheric Data Assimilation with Multiple Preferences
Jing-An Sun, Hang Fan, Junchao Gong, Ben Fei, Kun Chen, Fenghua Ling, Wenlong Zhang, Wanghan Xu, Li Yan, Pierre Gentine, Lei Bai
https://arxiv.org/abs/2505.22008

Align-DA: Align Score-based Atmospheric Data Assimilation with Multiple Preferences
Data assimilation (DA) aims to estimate the full state of a dynamical system by combining partial and noisy observations with a prior model forecast, commonly referred to as the background. In atmospheric applications, this problem is fundamentally ill-posed due to the sparsity of observations relative to the high-dimensional state space. Traditional methods address this challenge by simplifying background priors to regularize the solution, which are empirical and require continual tuning for a…

What is Past is Prologue:
#Paleoclimate constraints on #ECS Equilibrium #ClimateSensitivity #GreenhouseGas sensitivity of Earth's #Climate
First of 3 papers this summer.
My career trajectory has been building towards using past climate archives to narrow the possibilities of future climate. But it is HARD. And computationally expensive.
The next two papers of this project will rule out some high and low sensitivity simulations using Past Climate (Proxy) archives and Data Assimilation. Those should be out in preprint in the next couple of months.
We hope this project (first to constrain earth climate sensitivity using coupled atmosphere-ocean earth system models) will be streamlined in the future and applied widely.
https://essopenarchive.org/users/921675/articles/1297441-on-the-sensitivity-of-climate-sensitivity-in-a-coupled-climate-model

Posterior contraction rates of computational methods for Bayesian data assimilation
Erik Burman, Mingfei Lu
https://arxiv.org/abs/2506.14685 https://

Posterior contraction rates of computational methods for Bayesian data assimilation
In this paper, we analyze posterior consistency of a Bayesian data assimilation problem under discretization. We prove convergence rates for the discrete posterior to ground truth solution under both conforming discretization and finite element discretization (usually non-conforming). The analysis is based on the coupling of asymptotics between the number of samples and the dimension of discrete spaces. In the finite element discretization, tailor-made discrete priors, instead of the discretiza…

Active Digital Twins via Active Inference
Matteo Torzoni, Domenico Maisto, Andrea Manzoni, Francesco Donnarumma, Giovanni Pezzulo, Alberto Corigliano
https://arxiv.org/abs/2506.14453

Active Digital Twins via Active Inference
Digital twins are transforming engineering and applied sciences by enabling real-time monitoring, simulation, and predictive analysis of physical systems and processes. However, conventional digital twins rely primarily on passive data assimilation, which limits their adaptability in uncertain and dynamic environments. This paper introduces the active digital twin paradigm, based on active inference. Active inference is a neuroscience-inspired, Bayesian framework for probabilistic reasoning and…

A unified neural background-error covariance model for midlatitude and tropical atmospheric data assimilation
Bo\v{s}tjan Melinc, Uro\v{s} Perkan, \v{Z}iga Zaplotnik
https://arxiv.org/abs/2506.11968

A unified neural background-error covariance model for midlatitude and tropical atmospheric data assimilation
Estimating background-error covariances remains a core challenge in variational data assimilation (DA). Operational systems typically approximate these covariances by transformations that separate geostrophically balanced components from unbalanced inertio-gravity modes - an approach well-suited for the midlatitudes but less applicable in the tropics, where different physical balances prevail. This study estimates background-error covariances in a reduced-dimension latent space learned by a neu…