107 citations · 386 across the 22 of their papers we have counts for
24 papers
Should All Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness
Salma Alsinan, Maksim Makarenko, Sixiu Liu +2
Geophysical data collected from active field sites are often contaminated by complex and heterogeneous noise, obscuring weak seismic events, and complicating automated interpretati…
Oil Spill Risk Analysis For The NEOM Shoreline
HVR Mittal, Mohamad Abed El Rahman Hammoud, Ana K. Carrasco +2
A risk analysis is conducted considering several release sources located around the NEOM shoreline. The sources are selected close to the coast and in neighboring regions of high m…
Continuous and Discrete Data Assimilation with Noisy Observations for the Rayleigh-Benard Convection: A Computational Study
Mohamad Abed El Rahman Hammoud, Olivier LeMaitre, Edriss S. Titi +2
Obtaining accurate high-resolution representations of model outputs is essential to describe the system dynamics. In general, however, only spatially- and temporally-coarse observa…
Enhanced Simulation of the Indian Summer Monsoon Rainfall Using Regional Climate Modeling and Continuous Data Assimilation
Srinivas Desamsetti, Hari Prasad Dasari, Sabique Langodan +6
This study assesses a Continuous Data Assimilation (CDA) dynamical-downscaling algorithm for enhancing the simulation of the Indian summer monsoon (ISM) system. CDA is a mathematic…
A Visual Analytics Based Decision Making Environment for COVID-19 Modeling and Visualization
Shehzad Afzal, Sohaib Ghani, Hank C. Jenkins-Smith +3
Public health officials dealing with pandemics like COVID-19 have to evaluate and prepare response plans. This planning phase requires not only looking into the spatiotemporal dyna…
Impact of Atmospheric and Model Physics Perturbations On a High-Resolution Ensemble Data Assimilation System of the Red Sea
Sivareddy Sanikommu, Habib Toye, Peng Zhan +4
The Ensemble Adjustment Kalman Filter (EAKF) of the Data Assimilation Research Testbed (DART) is implemented to assimilate observations of satellite sea surface temperature, altime…
Efficient Dynamical Downscaling of General Circulation Models Using Continuous Data Assimilation
Srinivas Desamsetti, Hari Prasad Dasari, Sabique Langodan +3
Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the perfor…
Optimal projection of observations in a Bayesian setting
Loïc Giraldi, Olivier P. Le Maître, Ibrahim Hoteit +1
Optimal dimensionality reduction methods are proposed for the Bayesian inference of a Gaussian linear model with additive noise in presence of overabundant data. Three different op…
Quantifying Uncertainties in Fault Slip Distribution during the Tōhoku Tsunami using Polynomial Chaos
Ihab Sraj, Kyle T. Mandli, Omar M. Knio +2
An efficient method for inferring Manning's coefficients using water surface elevation data was presented in Sraj et al. (2014) focusing on a test case based on data collected…
Calibration of the K-Profile Parameterization of ocean boundary layer mixing. Part I: Development
S. E. Zedler, C. S. Jackson, F. Yao +4
In model comparisons with observational data, not all data contain information that is useful for answering a specific science question. If non-relevant or highly uncertain data ar…
Downscaling the 2D Benard Convection Equations Using Continuous Data Assimilation
M. U. Altaf, E. S. Titi, O. M. Knio +3
We consider a recently introduced continuous data assimilation (CDA) approach for downscaling a coarse resolution configuration of the 2D Benard convection equations into a finer g…
A Bayesian Consistent Dual Ensemble Kalman Filter for State-Parameter Estimation in Subsurface Hydrology
Boujemaa Ait-El-Fquih, Mohamad El Gharamti, Ibrahim Hoteit
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation…
Polynomial Chaos-based Bayesian Inference of K-Profile Parametrization in a General Circulation Model of the Tropical Pacific
Ihab Sraj, Sarah E. Zedler, Omar M. Knio +2
The authors present a Polynomial Chaos (PC)-based Bayesian inference method for quantifying the uncertainties of the K-Profile Parametrization (KPP) within the MIT General Circulat…
Coordinate Transformation and Polynomial Chaos for the Bayesian Inference of a Gaussian Process with Parametrized Prior Covariance Function
Ihab Sraj, Olivier P. Le Maître, Omar M. Knio +1
This paper addresses model dimensionality reduction for Bayesian inference based on prior Gaussian fields with uncertainty in the covariance function hyper-parameters. The dimensio…
Ensemble Kalman filtering with a divided state-space strategy for coupled data assimilation problems
Xiaodong Luo, Ibrahim Hoteit
This study considers the data assimilation problem in coupled systems, which consists of two components (sub-systems) interacting with each other through certain coupling terms. A…
Ensemble Kalman filtering with residual nudging: an extension to state estimation problems with nonlinear observation operators
Xiaodong Luo, Ibrahim Hoteit
The ensemble Kalman filter (EnKF) is an efficient algorithm for many data assimilation problems. In certain circumstances, however, divergence of the EnKF might be spotted. In prev…
Continuity in time of solutions of a phase-field model
Thomas G. Amler, Nikolai D. Botkin, Karl-Heinz Hoffmann +1
A phase field model proposed by G. Caginalp for the description of phase changes in materials is under consideration. It is assumed that the medium is located in a container with h…
Covariance inflation in the ensemble Kalman filter: a residual nudging perspective and some implications
Xiaodong Luo, Ibrahim Hoteit
This note examines the influence of covariance inflation on the distance between the measured observation and the simulated (or predicted) observation with respect to the state est…
Efficient particle filtering through residual nudging
Xiaodong Luo, Ibrahim Hoteit
We introduce an auxiliary technique, called residual nudging, to the particle filter to enhance its performance in cases that it performs poorly. The main idea of residual nudging…
Ensemble Kalman filtering with residual nudging
Xiaodong Luo, Ibrahim Hoteit
Covariance inflation and localization are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample…
Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters
Ibrahim Hoteit, Xiaodong Luo, Dinh-Tuan Pham
This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function…
Robust ensemble filtering and its relation to covariance inflation in the ensemble Kalman filter
Xiaodong Luo, Ibrahim Hoteit
We propose a robust ensemble filtering scheme based on the filtering theory. The optimal filter is derived by minimizing the supremum (or maximum) of a pr…
Scaled unscented transform Gaussian sum filter: theory and application
Xiaodong Luo, Irene M. Moroz, Ibrahim Hoteit
In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. We introduce a framework, called the scaled unscented transform Gaussian sum filter (SUT-GS…
Reply to "Comment on 'Ensemble Kalman filter with the unscented transform'"
Xiaodong Luo, Irene M. Moroz, Ibrahim Hoteit
This is a reply to the comment of Dr Sakov on the work "Ensemble Kalman filter with the unscented transform" of Luo and Moroz (2009).