Homepage 🖱️ - Elías David Niño-Ruiz, Ph.D.

Selected Journal Publications

For a complete list, please visit my ORCID logo ORCID , Google Scholar, and SCOPUS profiles.

2023

  1. Popov, AA, Sandu, A, Nino Ruiz, ED and Evensen, G. 2023. A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering. Tellus A: Dynamic Meteorology and Oceanography, 75(1): 159–171. DOI: https://doi.org/10.16993/tellusa.214

  2. Nino-Ruiz, E.D., & Consuegra Ortega (2023) AMLCS-DA: A data assimilation package in Python for Atmospheric General Circulation Models. SoftwareX, Elsevier, 1– 10. Available from: https://doi.org/10.1016/j.softx.2023.101374

  3. Nino-Ruiz, E.D., Consuegra Ortega, R.S. & Lucini, M.(2023) Ensemble based methods for leapfrog integration in the simplified parameterizations, primitive-equation dynamics model. Quarterly Journal of the Royal Meteorological Society, RMetS, 1– 15. Available from: https://doi.org/10.1002/qj.4424

2022

  1. Nino-Ruiz, E. D., Guzman, L., & Jabba, D. (2022). Ensemble Driven Shrinkage Covariance Matrix Estimation for Sequential Data Assimilation. International Journal of Artificial Intelligence, CESER Publications, 2022 Autumn (October), Volume 20, Number 2.

2021

  1. Nino-Ruiz, E. D. (2021). A line-search optimization method for non-Gaussian data assimilation via random quasi-orthogonal sub-spaces. Journal of Computational Science, Elsevier, 53, 101373.y
  2. Lopez-Restrepo, S., Nino-Ruiz, E. D., Guzman-Reyes, L. G., Yarce, A., Pinel, N., & Heemink, A. W. (2021). An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge. Computational Geosciences, Springer, 25(3), 985-1003.
  3. Nino-Ruiz, E. D. (2021). A data-driven localization method for ensemble based data assimilation. Journal of Computational Science, Elsevier, 51, 101328.
  4. Nino-Ruiz, E. D., Guzman, L., & Jabba, D. (2021). An ensemble kalman filter implementation based on the ledoit and wolf covariance matrix estimator. Journal of Computational and Applied Mathematics, Elsevier, 384, 113163.

2020

  1. Montoya, O. L., Niño-Ruiz, E. D., & Pinel, N. (2020). On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes. Environmental Science and Pollution Research, Springer, 27(29), 35993-36012.
  2. Nino-Ruiz, E. D. (2020). A numerical method for solving linear systems in the preconditioned Crank–Nicolson algorithm. Applied Mathematics Letters, Elsevier, 104, 106254.
  3. Nino-Ruiz, E. D., Guzman-Reyes, L. G., & Beltran-Arrieta, R. (2020). An adjoint-free four-dimensional variational data assimilation method via a modified Cholesky decomposition and an iterative Woodbury matrix formula. Nonlinear Dynamics, Springer, 99(3), 2441-2457.

2019

  1. Mercado, V., Nino, E. D., & Arteta, C. A. (2019). Dynamic Site Response Characterization Via Bayesian Inference: Analysis of the SGC Station Deposit in Bogota, Colombia. Journal of Earthquake Engineering, Taylor & Francis, 23(10), 1629-1650.
  2. Nino-Ruiz, E. D., & Yang, X. S. (2019). Improved Tabu Search and Simulated Annealing methods for nonlinear data assimilation. Applied Soft Computing, Elsevier, 83, 105624.
  3. Nino-Ruiz, E. D. (2019). Non-linear data assimilation via trust region optimization. Computational and Applied Mathematics, Springer, 38(3), 1-26.
  4. Nino-Ruiz, E. D., Sandu, A., & Deng, X. (2019). A parallel implementation of the ensemble Kalman filter based on modified Cholesky decomposition. Journal of Computational Science, Elsevier, 36, 100654.
  5. Nino-Ruiz, E. D., & Morales-Retat, L. E. (2019). A Tabu Search implementation for adaptive localization in ensemble-based methods. Soft Computing, Springer, 23(14), 5519-5535.
  6. Nino-Ruiz, E. D., Ardila, C., Estrada, J., & Capacho, J. (2019). A reduced-space line-search method for unconstrained optimization via random descent directions. Applied Mathematics and Computation, Elsevier 341, 15-30.
  7. Nino-Ruiz, E. D., & Sandu, A. (2019). Efficient parallel implementation of DDDAS inference using an ensemble Kalman filter with shrinkage covariance matrix estimation. Cluster Computing, Springer, 22(1), 2211-2221.

2018

  1. Nino-Ruiz, E. D., Ardila, C., & Capacho, R. (2018). Local search methods for the solution of implicit inverse problems. Soft Computing, 22(14), 4819-4832.
  2. Nino-Ruiz, E. D. (2018). Implicit surrogate models for trust region based methods. Journal of computational science, Elsevier, 26, 264-274.
  3. Nino-Ruiz, E. D., Sandu, A., & Deng, X. (2018). An ensemble Kalman filter implementation based on modified Cholesky decomposition for inverse covariance matrix estimation. SIAM Journal on Scientific Computing, SIAM, 40(2), A867-A886.

2017

  1. Rao, V., Sandu, A., Ng, M., & Nino-Ruiz, E. D. (2017). Robust Data Assimilation Using L_1 and Huber Norms. SIAM Journal on Scientific Computing, SIAM, 39(3), B548-B570.

2016

  1. Petra, C. G., Zavala, V. M., Nino-Ruiz, E. D., & Anitescu, M. (2016). A high-performance computing framework for analyzing the economic impacts of wind correlation. Electric Power Systems Research, Elsevier, 141, 372-380.
  2. Ruiz, E. D. N., & Sandu, A. (2016). A derivative-free trust region framework for variational data assimilation. Journal of Computational and Applied Mathematics, Elsevier, 293, 164-179.

2015

  1. Nino-Ruiz, E. D., & Sandu, A. (2015). Ensemble Kalman filter implementations based on shrinkage covariance matrix estimation. Ocean Dynamics, Springer, 65(11), 1423-1439.
  2. Nino Ruiz, E. D., Sandu, A., & Anderson, J. (2015). An efficient implementation of the ensemble Kalman filter based on an iterative Sherman–Morrison formula. Statistics and Computing, Springer, 25(3), 561-577.

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