A5103245179- Meteorological Phenomena and Simulations
- Oceanographic and Atmospheric Processes
- Climate variability and models
- Wind and Air Flow Studies
- Hydrological Forecasting Using AI
- Energy Load and Power Forecasting
- Fluid Dynamics and Turbulent Flows
Universidad del Norte
2019-2021
Abstract In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance matrix estimation. Our combines information brought by model realizations, that based on our prior knowledge about dynamical system interest. We perform combination both sources optimal factors. The method exploits rank-deficiency matrices to provide analysis step in EnKF formulations. Localization inflation aspects are discussed, as well. Experimental tests...
In this paper, we propose a Four-Dimensional Variational (4D-Var) data assimilation framework for wind energy potential estimation. The is defined as follows: choose numerical model which can provide forecasts of speeds then, an ensemble realizations employed to build control spaces at observation steps via modified Cholesky decomposition. These are utilized estimate initial analysis increments and avoid the intrinsic use adjoint models in 4D-Var context. mapped back onto domain from obtain...
Earth and Space Science Open Archive This preprint has been submitted to is under consideration at Geophysical Research Letters. ESSOAr a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]A Robust Ensemble-based Assimilation Method using Shrinkage Estimator Adaptive InflationAuthorsSantiagoLopez-RestrepoiDElias DavidNino RuiziDAndresYarce BoteroLuis...