Omitir los comandos de cinta
Saltar al contenido principal
Inicio de sesión
Universidad EAFIT
Carrera 49 # 7 sur -50 Medellín Antioquia Colombia
Carrera 12 # 96-23, oficina 304 Bogotá Cundinamarca Colombia
(57)(4) 2619500

​​​​​Gru​po de investigación en Modelado Matemático​​​​​​​​​​

Determina y describe problemas y sistemas con posibilidades de ser modelados matemáticamente, o estudiados mediante la simulación. También formula, desarrolla y analiza modelos  para la solución de problemas y sistemas seleccionados. ​

Más información​​​​​​

Gene​​ralidades​ del Grupo​​​

Nombre del grupo: Modelado Matemático

GrupLAC: COL0016229

Categoría Colciencias: A

Escuela a la que pertenece: Ciencias

Líder: Juan Carlos Rivera Agudelo​

E-mail: jrivera6@​​​

Perfil comercial​

El Grupo cuenta con el conocimiento y las herramientas para dar soluciones a las empresas del sector productivo y de servicios a través de modelos matemáticos complejos.​

Descargar perfil comercial.​​​

​Convocatoria Estudiante de Doctorado en Ingeniería Matemática

Convocatoria de Estudiantes de Doctorado en Ingeniería Matemática.pdf

Sensitivity and uncertainty sources in numerical modeling to forecast atmospheric systems: High-resolution WRF model simulations in urban

valleys applied to air quality issues. 

Convocatoria de Estudiantes de Doctorado en Ingeniería Matemática.pdf

Cooperative Project: Universidad de Antioquia, Universidad EAFIT

Contributions of the Grupo de Ingeniería y Gestión Ambiental (GIGA), Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia, Mathematical Modeling Research Group and Biodiversidad, Evolución y Conservación Research Group, Universidad EAFIT, DIAM at TUDELFT, TNO The Netherlands.


The Colombian air quality dynamics analysis will be develop through the use of the LOTOS-EUROS Model from TNO looking for the cooperation between Universities and Institutions. In a previous Cooperative project we proposed at least seven theoretical aspects related to the Data Assimilation Schemes regarding the Backtracking localization strategies for Ensemble Kalman Filter and the traj4D-Var proposed by (Fu et al, 2015 and Lu et al, 2015), also the application of Observational impact analysis algorithm (TSBOI-MM) (Verlaan and Sumihar, 2016). Another goals related to the theoretical aspects of the particular modeling and data assimilation of the Air Quality within the Colombia Domain were also adressed.
During the development of the mentioned research, we realize that the short-term meteorological forecast with the numerical model WRF (Weather Research and Forecasting model) is a very relevant stage for an integrated and accurate research on the field. We also have demonstrated the challenges and opportunities to contribute scientifically and practically due to this approach (López et al, 2017; Pinel et al, 2017; Rendón et al, 2017; Posada et al, 2017; Rodríguez et al, 2017). 

Also TNO experts declared this as project to research during next years, conditioned to The European conditions, not as limited as ours. Mathematical models are the main scientific tool for understanding and predicting the potential response of the atmosphere to perturbations such as different
meteorological conditions, altered emissions, and land use/land cover modifications. These models contain the non linear and complex differential
equations that rule the physics of the phenomenon such as Navier-Stokes equations, and they must to be solved numerically and present a series of
challenges related to their sensitivity and uncertainty when solutions are required in a non homogeneous domain also under restrictions in boundary and initial conditions.

The main goal of this research is to identify significant sources of uncertainty in the short-term meteorological forecast with the numerical model WRF (Weather Research and Forecasting model), reduce the uncertainty for the solution of the equations for modeling and forecast, determine and explain the sensitivity of the model and the numerical solution due to the aforementioned difficulties for its solution, perform a successful coupling with LOTOS EUROS model for Chemical and transport dynamics in pseudo real time, as well as to discuss the causes and provide scientific evidence of the implications for the air quality modeling in the Aburrá river valley. 

The urban population growth generates an increase in urbanization and pollutant emissions with negative consequences for the environment and public health.

Further, there are several physical phenomena occurring in the low atmosphere of the cities that affect the concentration of pollutants and the chemistry composition of atmosphere over urbanized areas. These problems are more critical in complex terrains where ventilation is limited. During the last years, air pollution problems have become more frequent in the Aburrá river valley as a result of the combined effects of the complex topography, the accelerated population growth and the associated surface alterations and pollutant emissions. In this sense it is necessary to improve our capability to understand and predict local meteorological phenomena and the associated air pollution episodes, and involve this knowledge on the decision-making processes in the metropolitan area. 

Our aim is to predict episodes of atmospheric pollution to make efficient decisions that allow guaranteeing air quality and human health, providing conditions to the future human exposure model to pollution to be developed by BEC research group. 

In cooperation with Universidad de Antioquia now we propose to study the uncertainty measurements and develop a framework for uncertainty reduction in a prediction step for Weather forecast via WRF Model, taking into account its NON real time computability and computational load.
We also propose the evaluation of both models WRF/OPEN LOTOS-EUROS on the characterization of daily cycle for meteorology and pollutants dispersion in Valle de Aburrá (Aburrá Valley) also the study of scenary for the Aburrá Valley and its environmental implications through WRF/OPEN LOTOS-EUROS. Coupling and static/dynamic downscaling of the models are challenging task depending on models and data availability. (Rendon et al, 2014; Posada et al, 2016)