66 citations · 139 across the 8 of their papers we have counts for
8 papers
Model Training, Data Assimilation, and Forecast Experiments with a Hybrid Atmospheric Model that Incorporates Machine Learning
Dylan Elliott, Troy Arcomano, Istvan Szunyogh +1
The hybrid model combines the physics-based primitive-equations model SPEEDY with a machine learning-based (ML-based) model component, while ERA5 reanalyses provide the presumed tr…
Prediction Beyond the Medium Range with an Atmosphere-Ocean Model that Combines Physics-based Modeling and Machine Learning
Dhruvit Patel, Troy Arcomano, Brian Hunt +2
This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the mediu…
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
Alexander Wikner, Jaideep Pathak, Brian R. Hunt +3
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is in the form of noisy partial measurements of the past and present state…
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
Alexander Wikner, Jaideep Pathak, Brian Hunt +5
We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system…
Ensemble regional data assimilation using joint states
Young-noh Yoon, Brian R. Hunt, Edward Ott +1
We propose a data assimilation scheme that produces the analyses for a global and an embedded limited area model simultaneously, considering forecast information from both models.…
On the propagation of information and the use of localization in ensemble Kalman filtering
Young-noh Yoon, Edward Ott, Istvan Szunyogh
Several localized versions of the ensemble Kalman filter have been proposed. Although tests applying such schemes have proven them to be extremely promising, a full basic understan…
Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM: perfect model experiments
Junjie Liu, Elana Judith Fertig, Hong Li +5
This paper explores the potential of Local Ensemble Transform Kalman Filter (LETKF) by comparing the performance of LETKF with an operational 3D-Var assimilation system, Physical-S…
Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter
Brian R. Hunt, Eric J. Kostelich, Istvan Szunyogh
Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model…