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

  4. 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, Autumn (October), Volume 20, Number 2.

  5. 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

  6. Nino-Ruiz, E. D., A Data-Driven Localization Method for Ensemble Based Data Assimilation. Journal of Computational Science, Vol. 51, 101328., Elsevier, (2021). (HTML)

  7. Lopez-Restrepo, S., Nino-Ruiz, E.D., Guzman-Reyes, L.G. et al. An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge. Computational Geosciences (2021). (HTML)

  8. Elias D. Nino-Ruiz, Hybrid Ensemble Kalman Filter and Markov Chain Monte Carlo Implementations for Non-Gaussian Data Assimilation, International Journal of Artificial Intelligence, 2020 Autumn (October) , Volume 18, Number 2. (HTML)

  9. Elias D. Nino-Ruiz, Luis Guzman, Daladier Jabba, An ensemble Kalman filter implementation based on the Ledoit and Wolf covariance matrix estimator, Journal of Computational and Applied Mathematics, Elsevier. (2020). (HTML)

  10. Nino-Ruiz, E. D. (2020, June). A Random Line-Search Optimization Method via Modified Cholesky Decomposition for Non-linear Data Assimilation. In International Conference on Computational Science (pp. 189-202). Springer, Cham. - ( HTML )

  11. Montoya, O.L.Q., Niño-Ruiz, E.D. & Pinel, N. On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes. Environ Sci Pollut Res, Springer. (2020). https://doi.org/10.1007/s11356-020-08268-4

  12. Nino-Ruiz, E.D., Mancilla-Herrera, A., Lopez-Restrepo, S., and Quintero-Montoya, O. A Maximum Likelihood Ensemble Filter Via A Modified Cholesky Decomposition For Non-Gaussian Data Assimilation. Sensors, MPDI. (2020). https://doi.org/10.3390/s20030877

  13. Elias D. Nino-Ruiz, Juan C. Calabria-Sarmiento, Luis G. Guzman-Reyes, and Alvin Henao. A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation. Atmosphere, MPDI. (2020). https://doi.org/10.3390/atmos11020167

  14. Nino-Ruiz, E. D. A numerical method for solving linear systems in the preconditioned Crank–Nicolson algorithm. Applied Mathematics Letters, Eslevier. (2020). https://doi.org/10.1016/j.aml.2020.106254.

  15. Elias D. Nino-Ruiz, Rolando Beltran-Arrieta, & Luis Guzman-Reyes. An adjoint-free four-dimensional variational data assimilation method via a modified Cholesky decomposition and an iterative Woodbury matrix formula. Non-Linear Dynamics, Springer. (2019). https://doi.org/10.1007/s11071-019-05411-w

  16. Jairo Pimentel, Carlos Julio Ardila Hernandez, Elías Niño, Daladier Jabba Molinares, Jonathan Ruiz-Rangel. Water Cycle Algorithm: Implementation and Analysis of Solutions to the Bi-Objective Travelling Salesman Problem, International Journal of Artificial Intelligence, CESER, Volume 17 (2), (2019). http://www.ceser.in/ceserp/index.php/ijai/article/view/6256

  17. Elias D. Nino-Ruiz, Xin-She Yang, Improved Tabu Search and Simulated Annealing methods for nonlinear data assimilation, Applied Soft Computing, Elsevier, Volume 83, (2019). https://doi.org/10.1016/j.asoc.2019.105624

  18. Nino-Ruiz, E. D. Non-linear data assimilation via trust region optimization. Computational and Applied Mathematics, Springer, 38:129 (2019). https://doi.org/10.1007/s40314-019-0901-x

  19. Elias D. Nino-Ruiz, Carlos Ardila, Jesus Estrada and Jose Capacho. A reduced-space line-search method for unconstrained optimization via random descent directions. Applied Mathematics and Computation, Elsevier, 341(2018): 15-30. https://doi.org/10.1016/j.amc.2018.08.020

  20. Nino-Ruiz, E. D., Mancilla-Herrera, A. M., & Beltran-Arrieta, R. (2018, May). Non-Gaussian data assimilation via modified cholesky decomposition. In 2018 7th International Conference on Computers Communications and Control (ICCCC) (pp. 29-36). IEEE. https://ieeexplore.ieee.org/document/8390433/

  21. Elias D. Nino-Ruiz & Luis E. Morales-Retat. A Tabu Search implementation for adaptive localization in ensemble-based methods. Soft Computing, Springer (2018) https://doi.org/10.1007/s00500-018-3210-1

  22. Nino-Ruiz, Elias D.; Cheng, Haiyan; Beltran, Rolando. A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models. Atmosphere 9, no. 4: 126. (2018), http://www.mdpi.com/2073-4433/9/4/126

  23. Elias D. Nino-Ruiz. Implicit Surrogate Models For Trust Region Based Methods, Journal of Scientific Computing, Elsevier, (2018), https://doi.org/10.1016/j.jocs.2018.02.003

  24. Elias D. Nino-Ruiz, A Matrix-Free Posterior Ensemble Kalman Filter Implementation Based on a Modified Cholesky Decomposition. Atmosphere Journal, 8:125, (2017), https://doi.org/10.3390/atmos8070125

  25. Nino-Ruiz, E.D., Ardila, C. & Capacho, R. Local search methods for the solution of implicit inverse problems, Soft Computing, Springer (2017), https://doi.org/10.1007/s00500-017-2670-z

  26. Elias D. Nino-Ruiz, Carlos J. Ardila, Alfonso Mancilla, Jesus Estrada, A Surrogate Model Based On Mixtures Of Taylor Expansions For Trust Region Based Methods. Procedia Computer Science, Volume 108, 2017, Pages 1473-1482, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2017.05.200.

  27. Elias D. Nino-Ruiz, Alfonso Mancilla, Juan C. Calabria, A Posterior Ensemble Kalman Filter Based On A Modified Cholesky Decomposition, Procedia Computer Science, Volume 108, 2017, Pages 2049-2058, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2017.05.062.