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Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model
In the ensemble Kalman filter (EnKF) framework for data assimilation, a limited ensemble size results in a spurious sampling error and...
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A comparison of nonlinear extensions to the ensemble Kalman filter
Ensemble Kalman filters are based on a Gaussian assumption, which can limit their performance in some non-Gaussian settings. This paper reviews two...
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LORA: a local ensemble transform Kalman filter-based ocean research analysis
We have produced an eddy-resolving local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) for the western North Pacific...
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Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter
The Localized Weighted Ensemble Kalman Filter (LWEnKF) is a new nonlinear/non-Gaussian data assimilation (DA) method that can effectively alleviate...
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Precision and convergence speed of the ensemble Kalman filter-based parameter estimation: setting parameter uncertainty for reliable and efficient estimation
Determining physical process parameters in atmospheric models is critical to obtaining accurate weather and climate simulations; estimating optimal...
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Covariance Matrix Estimation for Ensemble-Based Kalman Filters with Multiple Ensembles
We consider the implementation of ensemble-based Kalman filters (EnKF) in the framework of ensembles of different accuracies and sizes that are...
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Quick estimation of parameters for the land surface data assimilation system and its influence based on the extended Kalman filter and automatic differentiation
Soil moisture plays a crucial role in drought monitoring, flood forecasting, and water resource management. Data assimilation methods can integrate...
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Influence of the indirect assimilation of radar reflectivity data using the ensemble Kalman filter on the simulation of a warm-sector squall line
Radar data assimilation is an important method to improve the performance of numerical models in severe convective weather. In this study, the...
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Assimilation of D-InSAR snow depth data by an ensemble Kalman filter
Snow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of...
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A sequential calibration approach based on the ensemble Kalman filter (C-EnKF) for forecasting total electron content (TEC)
Ionospheric models are applied for computing the Total Electron Content (TEC) in ionosphere to reduce its effects on the Global Navigation Satellite...
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An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge
In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance matrix estimation. Our...
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Kalman filter sensitivity tests for the NWP and analog-based forecasts post-processing
The goal of this study is to perform a detailed sensitivity test to find the optimal value of the variance ratio r for four different post-processing...
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Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter
The joint identification of the parameters defining a contaminant source and the heterogeneous distribution of the hydraulic conductivities of the...
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Deformation prediction of reservoir landslides based on a Bayesian optimized random forest-combined Kalman filter
Prediction model plays an important role in the early warning of reservoir landslides. This paper proposes a novel synthetic prediction model, the...
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Investigation into the nonlinear Kalman filter to correct the INS/GNSS integrated navigation system
The integrated navigation system is the inertial navigation system (INS), corrected by global navigation satellite system (GNSS) data. The correction...
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Ensemble-Based Seismic and Production Data Assimilation Using Selection Kalman Model
Data assimilation in reservoir modeling often involves model variables that are multimodal, such as porosity and permeability. Well established data...
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Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13
AbstractWe have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble...
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Impact of Perturbation Schemes on the Ensemble Prediction in a Coupled Lorenz Model
Based on a simple coupled Lorenz model, we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale...
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Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction
An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (WRF) is cycled and evaluated for western...