Ensemble-based Data Assimilation For High-uncertainty systems: a case of study with Particulate Matter in the Aburra Valley

In order to avoid the adverse effects of air pollution, efforts have been made to monitor when air pollution reaches dangerous levels. A Chemical Transport Model (CTM) can simulate trace gases and particles concentration in specific areas. These models are not entirely reliable, owing to incomplete knowledge about emissions and meteorological conditions. Explaining and predicting variability in air quality models remains a challenge. In this thesis we want to demonstrate that data assimilation (DA) can reduce uncertainty in the model process. DA is a mathematical family of techniques in which observed values are combined with a dynamic model to improve the accuracy of the model. Standard DA methods have limitations when there is not a complete characterization of the uncertainties. In air quality applications, emission inventories’ accuracy is often low, and weather models often do not predict events very well. The problem is worse in developing countries where the knowledge available is sparse and of relatively low quality. The thesis’s main contribution is the development of a DA systems for improving the behavior of complex models in the presence of high uncertainty. The proposed methods and developments have been tested in the framework of the LOTOS-EUROS CTM with applications to forecast particular matter in the Aburrá Valley in Colombia. The use of a less expensive monitoring network is also discussed. The Aburrá valley represents a good testing scenario because of its current air quality issues, the difficulty of its terrain, the lack of a detailed emission inventory, and the operational availability of a low-cost monitoring network. Our first step was to apply the Ensemble Kalman Filter (EnKF) to assimilate the official air quality monitoring network. Evaluations of the system were performed by varying values of the covariance localization influence area. Moreover, various inheritance strategies were evaluated to optimize the assimilation window’s estimated
Mathematical Physics