Article
Environmental Sciences
G. Piazzi, G. Thirel, C. Perrin, O. Delaigue
Summary: Skillful streamflow forecasts are crucial for water-related applications, with a growing emphasis on improving initial condition estimates through data assimilation. This study assesses the sensitivity of DA-based IC estimation to various uncertainties and model updates over 232 watersheds in France. The comparison of two ensemble-based techniques shows that accurate routing store estimates benefit the DA-based IC estimation, with the EnKF outperforming the PF in forecasting meteorological uncertainty.
WATER RESOURCES RESEARCH
(2021)
Article
Meteorology & Atmospheric Sciences
Troy Arcomano, Istvan Szunyogh, Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Edward Ott
Summary: This paper describes the implementation of a combined hybrid-parallel prediction approach on a low-resolution atmospheric global circulation model. The hybrid model, which combines a physics-based numerical model with a machine learning component, produces more accurate forecasts for various atmospheric variables compared to the host model. Furthermore, the hybrid model exhibits smaller systematic errors and more realistic temporal variability in simulating the climate.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Kevin Raeder, Timothy J. Hoar, Mohamad El Gharamti, Benjamin K. Johnson, Nancy Collins, Jeffrey L. Anderson, Jeff Steward, Mick Coady
Summary: An ensemble Kalman filter reanalysis data set with a global, 80 member ensemble spanning from 2011 to 2019 is archived, providing opportunities for robust statistical analysis and machine learning training.
SCIENTIFIC REPORTS
(2021)
Article
Meteorology & Atmospheric Sciences
Jeffrey S. Whitaker, Anna Shlyaeva, Stephen G. Penny
Summary: This study compares two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system: the hybrid-covariance approach and the hybrid-gain approach. The results show that the simpler and less expensive hybrid-gain approach can achieve similar performance if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Haohao Sun, Lili Lei, Zhengyu Liu, Liang Ning, Zhe-Min Tan
Summary: An analog offline ensemble Kalman filter (AOEnKF) is proposed, which constructs ensemble priors from a control climate simulation for each assimilation time based on an analog criterion using proxy observations. AOEnKF generates smaller posterior errors and requires much less computational cost compared to the online cycling EnKF (CEnKF). It has the advantages of having a more accurate prior ensemble mean and flow-dependent background error covariances compared to the commonly applied offline EnKF (OEnKF).
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Lars Nerger
Summary: The study introduces a hybrid filter combining LETKF and NETF with the performance improved by adjusting the hybrid weight. Results show that a hybrid variant applying NETF followed by LETKF yields the best results in complex nonlinear models. Calculating the hybrid weight based on skewness, kurtosis, and effective sample size reduces estimation errors and enhances stability of the hybrid filter.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Water Resources
Andrew Pensoneault, Witold F. Krajewski, Nicolas Velasquez, Xueyu Zhu, Ricardo Mantilla
Summary: This paper discusses the application of data assimilation techniques in hydrology, focusing on the potential of EnKF and its extensions in sequential state estimation and Bayesian inverse problems. The authors improve the streamflow in a virtual catchment using the EKI algorithm and demonstrate its favorable performance.
ADVANCES IN WATER RESOURCES
(2023)
Article
Engineering, Civil
Behmard Sabzipour, Richard Arsenault, Magali Troin, Jean-Luc Martel
Summary: Data assimilation is an important step in improving hydrological model predictions. This study aims to identify optimal seasonal parameterizations to reduce uncertainty in initial conditions in a snow-dominated catchment in Canada. Sensitivity analysis shows that forecast performance is sensitive to individual hyperparameters and the choice of state variables.
JOURNAL OF HYDROLOGY
(2023)
Article
Cardiac & Cardiovascular Systems
Dario De Marinis, Dominik Obrist
Summary: The proposed data assimilation methodology aims to enhance the spatial and temporal resolution of voxel-based data obtained from biomedical imaging modalities, specifically focusing on turbulent blood flow assessment in large vessels. The methodology, utilizing a Stochastic Ensemble Kalman Filter approach, combines observed flow fields with numerical simulations to improve the accuracy of flow field predictions. Validation against canonical flows and application to a clinically relevant scenario demonstrate the potential of the method to enhance 4D flow MRI data for future use.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2021)
Article
Environmental Sciences
X. D. Lyu, Y. R. Fan
Summary: The study employs a multi-level factorial analysis approach to characterize the major impact factors on the performances of different data assimilation schemes, demonstrating that the impacts from stochastic perturbations vary for different schemes and some factors may be statistically insignificant. Results indicate that scenarios with extreme stochastic perturbations are more likely to result in good performance for all data assimilation schemes.
JOURNAL OF ENVIRONMENTAL INFORMATICS
(2021)
Article
Engineering, Ocean
Shintaro Gomi, Tsutomu Takagi, Katsuya Suzuki, Rika Shiraki, Ichiya Ogino, Shigeru Asaumi
Summary: A control method for changing the geometry of a fishing net was proposed, utilizing data assimilation to estimate unknown parameters and achieve the intended net geometry. The automatic control system was validated through numerical simulation experiments, demonstrating the successful control of net geometry using the extended Kalman filter.
APPLIED OCEAN RESEARCH
(2021)
Article
Mechanics
Zhiwen Deng, Chuangxin He, Yingzheng Liu
Summary: This paper focuses on the optimal sensor placement strategy based on a deep neural network for turbulent flow recovery within the data assimilation framework of the ensemble Kalman filter. The results demonstrate the effectiveness and robustness of the proposed strategy, showing that RANS models with EnKF augmentation were substantially improved over their original counterparts. The study concludes that the DNN-based OSP with the selection of the five most sensitive sensors can efficiently reduce the number of sensors while achieving similar or better assimilated performance.
Article
Engineering, Civil
Teng Xu, J. Jaime Gomez-Hernandez, Zi Chen, Chunhui Lu
Summary: Understanding a contaminant source is crucial for managing a polluted aquifer, but source information may be unavailable when pollutants are detected. The Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is proposed as a more efficient solution than the restart Ensemble Kalman Filter (r-EnKF), but requires a large number of assimilations to achieve the same level of accuracy.
JOURNAL OF HYDROLOGY
(2021)
Article
Geosciences, Multidisciplinary
Chuan-An Xia, Xiaodong Luo, Bill X. Hu, Monica Riva, Alberto Guadagnini
Summary: In this study, we used the MEs-EnKF approach to investigate the relationship between conductivity estimates and the type of available hydraulic head information in a heterogeneous groundwater field. Our results show that monitoring wells of Type A provide the best quality estimates, while Type B and C wells yield similar quality estimates. Additionally, inflating the measurement-error covariance matrix can improve conductivity estimates in simplified flow models.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2021)
Article
Meteorology & Atmospheric Sciences
Jonathan Poterjoy
Summary: This study explores the use of iterative and hybrid strategies for improving localized particle filters in geophysical models. The experiments show that these strategies have the largest benefits in observation-sparse regimes with non-Gaussian prior errors.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Computer Science, Interdisciplinary Applications
Yin Yang, Cordelia Robinson, Dominique Heitz, Etienne Memin
COMPUTERS & FLUIDS
(2015)
Article
Computer Science, Interdisciplinary Applications
Pranav Chandramouli, Dominique Heitz, Sylvain Laizet, Etienne Memin
COMPUTERS & FLUIDS
(2018)
Article
Computer Science, Artificial Intelligence
Patrick Heas, Cedric Herzet, Etienne Memin, Dominique Heitz, Pablo D. Mininni
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2013)
Article
Computer Science, Interdisciplinary Applications
A. Gronskis, D. Heitz, E. Memin
JOURNAL OF COMPUTATIONAL PHYSICS
(2013)
Article
Engineering, Multidisciplinary
M. Ndoye, J. Delville, D. Heitz, G. Arroyo
MEASUREMENT SCIENCE AND TECHNOLOGY
(2010)
Article
Meteorology & Atmospheric Sciences
Patrick Heas, Etienne Memin, Dominique Heitz, Pablo D. Mininni
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
(2012)
Article
Engineering, Mechanical
Pranav Chandramouli, Etienne Memin, Dominique Heitz, Lionel Fiabane
EXPERIMENTS IN FLUIDS
(2019)
Article
Construction & Building Technology
Romain Schuster, Dominique Heitz, Philippe Georgeault, Etienne Memin
INDOOR AND BUILT ENVIRONMENT
(2020)
Article
Computer Science, Interdisciplinary Applications
Pranav Chandramouli, Etienne Memin, Dominique Heitz
JOURNAL OF COMPUTATIONAL PHYSICS
(2020)
Article
Mechanics
Ali Rahimi Khojasteh, Yin Yang, Dominique Heitz, Sylvain Laizet
Summary: The proposed Lagrangian Coherent Track Initialization (LCTI) technique based on Finite Time Lyapunov Exponent (FTLE) shows robust behavior in finding true particle tracks and performs well in flows with high particle concentrations. LCTI is demonstrated to be a reliable tracking tool in complex flow motions, with particular strength in flows with high velocity and acceleration gradients.
Article
Engineering, Mechanical
Yin Yang, Dominique Heitz
Summary: The article introduces a novel Lagrangian particle tracking method named KLPT, which outperforms the conventional method STB in terms of robustness and accuracy when dealing with challenging cases.
EXPERIMENTS IN FLUIDS
(2021)
Article
Multidisciplinary Sciences
Ali Rahimi Khojasteh, Sylvain Laizet, Dominique Heitz, Yin Yang
Summary: The dataset includes Eulerian velocity and pressure fields, as well as Lagrangian particle trajectories, of the wake flow downstream of a smooth cylinder at a Reynolds number of 3900. These data can be used for tracking algorithm assessment, exploring Lagrangian physics, statistical analysis, machine learning, and data assimilation research.
Article
Computer Science, Interdisciplinary Applications
Valentin Resseguier, Matheus Ladvig, Dominique Heitz
Summary: This study proposes a new data assimilation algorithm for the estimation and prediction of unsteady flows in complex hydrodynamic and aerodynamic systems. It combines onboard measurements and fluid dynamics simulations in real time, and has been validated through case studies.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Mechanics
Valentin Resseguier, Etienne Memin, Dominique Heitz, Bertrand Chapron
JOURNAL OF FLUID MECHANICS
(2017)