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
Eun-Gyeong Yang, Hyun Mee Kim
Summary: This study investigated the performance of three data assimilation methods based on the WRF model over East Asia. The hybrid E3DVAR method outperformed 3DVAR and EnKF for both January and July seasons. Adjusting background error covariance can improve forecast accuracy, and each method has different strengths in different seasons.
ATMOSPHERIC RESEARCH
(2021)
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
Environmental Sciences
Uzzal Kumar Dash, Soon-Young Park, Chul Han Song, Jinhyeok Yu, Keiya Yumimoto, Itsushi Uno
Summary: In order to improve the predictability of atmospheric particulate matter concentrations, researchers have developed a data assimilation system using ensemble square root filter (EnSRF) for the CMAQ model. The EnSRF method outperforms other data assimilation methods, such as EnKF and 3DVAR, in terms of statistical metrics.
ENVIRONMENTAL POLLUTION
(2023)
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
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
Environmental Sciences
Shujun Zhu, Bin Wang, Lin Zhang, Juanjuan Liu, Yongzhu Liu, Jiandong Gong, Shiming Xu, Yong Wang, Wenyu Huang, Li Liu, Yujun He, Xiangjun Wu, Bin Zhao, Fajing Chen
Summary: Many ensemble-based data assimilation methods use observation space localization to mitigate sampling errors. This study proposes a weighted average hypsometry to determine the vertical coordinates of radiance observations and successfully integrates it with an ensemble four-dimensional variational DA system. Experimental results show significant improvements in the analysis quality and forecast skills of the system, especially in the Southern Hemisphere.
Article
Mathematics, Applied
Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott
Summary: This study discusses the forecasting of chaotic dynamical systems using noisy partial measurements data, with a focus on combining machine learning with knowledge-based models to improve predictions. By assimilating synthetic data and training machine learning models with partial measurements, it shows potential to correct imperfections in knowledge-based models and improve forecasting accuracy.
Article
Computer Science, Interdisciplinary Applications
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Eviatar Bach, Pedram Hassanzadeh
Summary: Data assimilation (DA) is a crucial part of forecasting models, allowing for better estimation of initial conditions in imperfect dynamical systems using observations. Ensemble Kalman filter (EnKF) is a widely-used DA algorithm, but its computational complexity is problematic for large systems. In this study, a hybrid ensemble Kalman filter (H-EnKF) is proposed, utilizing a data-driven surrogate to generate a large ensemble and accurately compute the background error covariance matrix. H-EnKF outperforms EnKF without the need for ad-hoc localization strategies, making it applicable to high-dimensional systems.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Meteorology & Atmospheric Sciences
Shujun Zhu, Bin Wang, Lin Zhang, Juanjuan Liu, Yongzhu Liu, Jiandong Gong, Shiming Xu, Yong Wang, Wenyu Huang, Li Liu, Yujun He, Xiangjun Wu, Bin Zhao, Fajing Chen
Summary: This study developed an ensemble four-dimensional variational (En4DVar) hybrid data assimilation system and evaluated its performance in terms of analysis quality and forecast skill. The results showed that the En4DVar system has the ability to improve the accuracy of forecasts, mainly due to the flow-dependent ensemble covariance provided by 4DEnVar.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
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
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
Mathematics, Interdisciplinary Applications
Dongrui Shao, Junyu Chu, Luonan Chen, Huanfei Ma
Summary: Data assimilation is crucial for both data driven and model driven research. The Kalman filter, a widely used data assimilation framework, has traditionally relied on theoretical models. However, recent efforts have aimed to develop model-free Kalman filters that solely rely on data. In this study, we propose a hybrid model framework that combines delay embedding theory and machine learning to bridge the gap between exact model-based and totally model-free methods. This hybrid approach is more flexible in application and has been validated using benchmark systems and real-world problems.
CHAOS SOLITONS & FRACTALS
(2023)
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
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
Francisco J. Tapiador, Remy Roca, Anthony Del Genio, Boris Dewitte, Walt Petersen, Fuqing Zhang
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2019)
Article
Geosciences, Multidisciplinary
Xingchao Chen, Fuqing Zhang
GEOPHYSICAL RESEARCH LETTERS
(2019)
Article
Meteorology & Atmospheric Sciences
Fuqing Zhang, Y. Qiang Sun, Linus Magnusson, Roberto Buizza, Shian-Jiann Lin, Jan-Huey Chen, Kerry Emanuel
JOURNAL OF THE ATMOSPHERIC SCIENCES
(2019)
Review
Meteorology & Atmospheric Sciences
Julia H. Keller, Christian M. Grams, Michael Riemer, Heather M. Archambault, Lance Bosart, James D. Doyle, Jenni L. Evans, Thomas J. Galarneau, Kyle Griffin, Patrick A. Harr, Naoko Kitabatake, Ron McTaggart-Cowan, Florian Pantillon, Julian F. Quinting, Carolyn A. Reynolds, Elizabeth A. Ritchie, Ryan D. Torn, Fuqing Zhang
MONTHLY WEATHER REVIEW
(2019)
Article
Meteorology & Atmospheric Sciences
Masashi Minamide, Fuqing Zhang
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2019)
Article
Geosciences, Multidisciplinary
Yan Wang, Kun Yang, Xu Zhou, Binbin Wang, Deliang Chen, Hui Lu, Changgui Lin, Fuqing Zhang
GEOPHYSICAL RESEARCH LETTERS
(2019)
Article
Geosciences, Multidisciplinary
Hans W. Chen, Fuqing Zhang, Thomas Lauvaux, Kenneth J. Davis, Sha Feng, Martha P. Butler, Richard B. Alley
GEOPHYSICAL RESEARCH LETTERS
(2019)
Article
Meteorology & Atmospheric Sciences
Xingchao Chen, Fuqing Zhang, James H. Ruppert
JOURNAL OF CLIMATE
(2019)
Article
Meteorology & Atmospheric Sciences
Dandan Tao, Fuqing Zhang
MONTHLY WEATHER REVIEW
(2019)
Article
Meteorology & Atmospheric Sciences
Andrew Thomas, Amy K. Huff, Xiao-Ming Hu, Fuqing Zhang
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2019)
Article
Meteorology & Atmospheric Sciences
Hans W. Chen, Lily N. Zhang, Fuqing Zhang, Kenneth J. Davis, Thomas Lauvaux, Sandip Pal, Brian Gaudet, Joshua P. DiGangi
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2019)
Article
Meteorology & Atmospheric Sciences
Xiaofei Li, Fuqing Zhang, Qinghong Zhang, Matthew R. Kumjian
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2019)
Article
Meteorology & Atmospheric Sciences
Xinghua Bao, Fuqing Zhang
JOURNAL OF CLIMATE
(2019)
Article
Meteorology & Atmospheric Sciences
Xingchao Chen, Fuqing Zhang
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2019)
Article
Meteorology & Atmospheric Sciences
Atsushi Okazaki, Takemasa Miyoshi, Kei Yoshimura, Steven J. Greybush, Fuqing Zhang
Summary: Online data assimilation performs better than offline data assimilation when the predictability of the system exceeds the averaging time of observations. The ocean plays a crucial role in extending predictability, aiding online data assimilation to outperform offline data assimilation. Moreover, the observations of near-surface air temperature over land are highly valuable in updating ocean variables, highlighting the importance of utilizing cross-domain covariance information between the atmosphere and the ocean in paleoclimate reconstruction.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2021)