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Showing 1 to 20 of 28 for “"EnKF"”.
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Localization with the EnKF for the Automatic History Matching of the SAGD Processes
The Ensemble Kalman Filter (EnKF) has been successfully applied to data assimilation in Steam Assisted Gravity Drainage (SAGD) processes, but applications of localization for the EnKF in SAGD processes have not been studied. Distance-based localization has been reported to be very efficient for …
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Evaluating the 3D EnKF - VAR Hybrid Data Assimilation in GSI for Surface and Upper Level Analyses
… using the Hybrid Ensemble Kalman Filter (EnKF) Variational (VAR) Data Assimilation in the Gridpoint Statistical Interpolation (GSI) System. The data assimilation ingests the 1 hour forecast High-Resolution Rapid Refresh (HRRR) and The Global Ensemble Forecast System, as the background and …
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Data Assimilation and Applications in Forecasting
… specifically the ensemble Kalman filter (EnKF). The first explores how spatial localization, an important method commonly used in the EnKF, can be extended to multiscale problems. Rather than using a single length scale when localizing, we construct a localized covariance matrix through …
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Multi-sensor large scale land surface data assimilation using ensemble approaches
One of the ensemble Kalman filter's (EnKF) attractive features in land surface applications is its ability to provide distributional information. The EnKF relies on normality approximations that improve its efficiency but can also compromise the accuracy of its distributional estimates. The effects …
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Strategies for real time reservoir management
… well control adjustment. Second, two gathered EnKF methods are proposed to save computational cost and reduce sampling error: gathered EnKF with a fixed gather size and adaptively gathered EnKF. Finally, oil price uncertainty is forecasted and quantified with three price forecasting models: …
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Geomechanical Reservoir Model Calibration and Uncertainty Assessment from Microseismic Data
… We apply the ensemble Kalman filter (EnKF) to integrate the resulting continuous seismicity map to estimate hydraulic and geomechanical property distributions. We demonstrate that the standard application of the EnKF with such large correlated datasets can result in substantial loss of …
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Efficient formulation and implementation of ensemble based methods in data assimilation
… A major bottleneck in ensemble Kalman filter (EnKF) implementations is the solution of a linear system at each analysis step. To alleviate it an EnKF implementation based on an iterative Sherman Morrison formula is proposed. The rank deficiency of the ensemble covariance matrix is exploited in …
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Continuous reservoir model updating by ensemble Kalman filter on Grid computing architectures
… for optimization. The ensemble Kalman filter (EnKF), a Bayesian approach for model updating, uses Monte Carlo statistics for fusing observation data with forecasts from simulations to estimate a range of plausible models. The ensemble of updated models can be used for uncertainty forecasting or …
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Continuous reservoir modeling updating by integrating experimental data using an ensemble Kalman Filter
… high-frequency sequential data with noise, the EnKF method is used to efficiently integrate them. To better understand the problem, scaling analysis is done on the capillary transition zone. Two new dimensionless numbers are introduced-capillary time and capillary length. We found that for …
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Carbon Data Assimilation Using an Ensemble Kalman Filter
… capability in an ensemble Kalman filter (EnKF) data assimilation system with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). In spite of its global coverage, the CO retrievals from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard …
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Hydrologic data assimilation of multi-resolution microwave radiometer and radar measurements using ensemble smoothing
Previously, the ensemble Kalman filter (EnKF) has been used to estimate soil moisture and related fluxes by merging noisy low frequency microwave observations with forecasts from a conventional though uncertain land surface model (LSM). Here it is argued that soil moisture estimation is a …
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Advances in Algorithms for Atmospheric Sciences
… assimilation scheme for ensemble Kalman filters (EnKFs), which improve the state estimate of the system using observations. Our distributed data assimilation technique, known as the distributed EnKF (dEnKF), is inspired by domain splitting and coarse correction techniques from domain decomposition …
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Real-time data assimilation in nonlinear dynamical systems
… bias-aware ensemble Kalman Filter* (r-EnKF). To estimate the bias in the low-order model, we propose an echo state network, which is a generalized auto-regressive function. We derive the Jacobian of the network and design a robust training strategy with data augmentation to accurately …
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Modeling volcanic unrest by data assimilation
… assimilation technique, Ensemble Kalman Filter (EnKF), to improve its performance in forecasting volcanic unrests with multiple geodetic observations. Then, the robustness of the EnKF is confirmed in application to the unrest and 2009 eruption of Kerinci volcano, Indonesia. To understand the …
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Hillslope-scale soil moisture estimation with a physically-based ecohydrology model and L-band microwave remote sensing observations from space
… that employs the ensemble Kalman Filter (EnKF) to estimate the spatial distribution of soil moisture at hillslope scales by combining uncertain model estimates with noisy active and passive L-band microwave observations. Uncertainty in the modeled soil moisture state is estimated through …
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Ensemble-based reservoir history matching using hyper-reduced-order models
… cost. First, an ensemble Kalman filter (EnKF) is considered for Gaussian system and a procedure embedding the hyper-reduced model (HRM) into the EnKF is presented. The use of the HRM for the EnKF significantly reduces the computational cost without much loss of accuracy, but the …
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Data assimilation into physics-based thermoacoustic models using Bayesian neural network ensembles
… Bunsen flames, the ensemble Kalman filter (EnKF) infers the parameters of the physics-based model and their uncertainties from a sequence of images. The method is reliable but computationally expensive: it takes hours for the EnKF to converge to parameter estimates and uncertainties for each …
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Investigation of sensitivity of surface deformation to subsurface properties and reservoir operations
… inversions with the Ensemble Kalman Filter (EnKF).
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Study on the Intensity and Track Prediction of Typhoon Yagi (2024) Based on Ensemble Kalman Filter Assimilation of Satellite and Radar Data
… and radar data using the Ensemble Kalman Filter (EnKF) on the prediction of Typhoon Yagi (2024). Based on the Weather Research and Forecasting (WRF) model and the EnKF method, a series of sensitivity experiments with different assimilated observational data types were conducted, including …
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Particle filtering with Lagrangian data in a point vortex model
… Kalman Filter (EKF) and Ensemble Kalman Filter (EnKF), particle filtering does not rely on linearization of the forward model and can provide very accurate estimates of the state, as it represents the true Bayesian posterior distribution using a large number of weighted particles. In this work, …
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