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Total result(s) found: 17
  • ​A two-update ensemble Kalman filter for land hydrological data assimilation with an uncertain constraint
    M. Khaki, B. Ait-El-Fquih, I. Hoteit, E. Forootan, J. Awange, M. Kuhn
    Journal of Hydrology, 555, 447-462, 2017
Keywords:
Constrained data assimilation, Ensemble Kalman filtering, Weak constrained ensemble Kalman filter, Water budget closure, Hydrological modelling
  • ​Quantifying uncertainties in fault slip distribution during the Tohoku tsunami using Polynomial Chaos
    I. Sraj, K. Mandli, O. Knio, C. Dawson, and I. Hoteit
    Ocean Dynamics, doi:10.1007/s10236-017-1105-9, 2017
Keywords:
Basis pursuit denoising, Bayesian inference, Earthquake inversion, Non-intrusive spectral projection, Polynomial chaos, Tsunami
  • ​Inferring soil salinity from multi-configuration electromagnetic induction measurements using adaptive Markov Chain Monte Carlo
    K.Z. Jadoon, U. Altaf, M. McCabe, I. Hoteit, N. Muhammad, and L. Weihermuller
    Hydrology and Earth System Science, https://doi.org/10.5194/hess-21-5375-2017, 2017
Keywords:
Electromagnetic induction, Markov processes, Monte Carlo methods, Adaptive Markov chain Monte Carlo
  • ​Decadal trends in Red Sea maximum surface temperature
    V. Chaidez, D. Dreano, S. Augusti, C. Duarte, and I. Hoteit
    Scientific Reports, doi:10.1038/s41598-017-08146-z, 2017
Keywords:
Sea surface temperature, Warming
  • ​Sensing coral reef connectivity pathways from space
    D. Raitsos, R. Brewin, P. Zhan, D. Dreano, Y. Pradhan, and I. Hoteit
    Scientific Report, doi:10.1038/s41598-017-08729-w, 2017
Keywords:
coral reef, Gene flow, Gene structure, Genetic distance, Physical model, Population model, Simulation
  • Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model
    M. Khaki, I. Hoteit, M. Kuhn, J. Awange, E. Forootan, C. Pattiaratchi, and A. van Dijk
    Advances in Water Resources, 107, 301-316, 2017
Keywords:
Data assimilation, GRACE, Hydrological modeling, Kalman filtering, Particle filtering
  • Assessing an ensemble Kalman filter inference of Manning's n coefficient of a storm surge model against a Polynomial Chaos-based MCMC
    A. Sripitana, T. Mayo, I. Sraj, O. Knio, C. Dawson, O. Le Maitre, and I. Hoteit
    Ocean Dynamics, 67, 1067-1094, 2017
Keywords:
Manning's n coefficient, Storm surge model framework, Kalman filter, Bayesian Inference
  • Forecasting the ecology of the Red Sea using a cluster of regional 1D marine ecosystem assimilative models
    D. Dreano, G. Triantafyllou, K. Tsiaras, and I. Hoteit
    Ocean Dynamics, 67, 935-947, 2017
Keywords:
Data assimilation, Ensemble Kalman filter, SEIK, ERSEM, Marine ecosystem modelling, Red Sea, Clustering, Chlorophyll remote sensing
  • ​Analysis of a severe weather event over Mecca, Kingdom of Saudi Arabia using Observations and High-Resolution Modelling
    H. Dasari, R. Attada, O. Knio, and I. Hoteit
    Meteorological Applications, doi:10.1002/met.1662, 2017
Keywords:
Mecca region, Storm, Strong winds, Rainfall, Orography, Red Sea moisture, High‐resolution model
  • Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing
    H. Toye, P. Zhan, G. Gopalakrishnan, A. Kartadikaria, H. Huang, O. Knio, and I. Hoteit
    Ocean Dynamics, 67, 915-933, 2017
Keywords:
Red Sea, Data assimilation, Seasonal variability, Ensemble Kalman filter, Ensemble optimal interpolation
  • A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3D biogeochemical model of the Mediterranean
    T. Kostas, I. Hoteit, G. Petihakis, S. Kalaroni, and G. Triantafyllou
    Ocean Dynamics, doi:10.1007/s10236-017-1050-7, 2017

Keywords:
Data assimilation, Biogeochemical model, Kalman filter, Ocean color, Mediterranean
  • Estimation of error covariances in nonlinear state-space models using Kalman Smoothing and the Expectation-Maximization algorithm
    D. Dreano, P. Tandeo, M. Pulido, B. Ait-El-Fquih, T. Chonavel and I. Hoteit
    Quarterly Journal of the Royal Meteorological Society, doi:10.1002/qj.3048, 2017
Keywords:
Expectation–maximization, EnKF, EnKS, Extended Kalman filter, Model error, State‐space models
  • The climatology of the Red Sea – Part-I: The wind

    S. Langodan, A. Pomaro, L. Cavaleri, Y. Vishwanadhapalli, L. Bertotti, and I. Hoteit

    International Journal of Climatology, doi:10.1002/joc.5103, 2017

Keywords:
The Red Sea, Red Sea climatology, Wind climate, WRF, Long‐term climate trend
  • Climatic features of the Red Sea from a regional assimilative model

    V. Yesubabu, H. Dasari, S. Langodan, S. Challa, and I. Hoteit 


    International Journal of Climatology, 37, 2563-2581, 2017

Keywords:
Red Sea reanalysis, WRF model, Dynamical downscaling, Data assimilation, 3DVAR
  • Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surroga
    te
    L
    . Giraldi, O. Le Maitre, K. Mandli. C. Dawson, I. Hoteit, and O. Knio
    Computational Geosciences, doi:10.1007/s10596-017-9646-z, 2017
Keywords:
Uncertainty quantification, Bayesian inference, Polynomial chaos expansion, Noise model, Low-rank representation, Shallow water equation, Tsunami, Earthquake inversion
  • Downscaling the 2D Benard convection equations using continuous data assimilation

    U. Altaf, E. Titi, O. Knio, L. Zhao, M. McCabe, and I. Hoteit

    Computational Geosciences, 21(3), 393-410, 2017

Keywords:
Bénard convection equations, Continuous data assimilation, Dynamical downscaling
  • ​Integrating gravimetric and interferometric synthetic aperture radar data for enhancing reservoir history matching of carbonate gas and volatile oil reservoirs
    K. Katterbauer, S. Arango, S. Sun, and I. Hoteit
    Geophysical Prospecting, 65, 337-364, 2017
Keywords:
Ensemble Kalman filter; Gas condensate reservoir; Gravimetry; History matching