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• W3198148910 abstract "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 Aburra Valley in Colombia. The use of a less expensive monitoring network is also discussed. The Aburra 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 information into the forecast window. Although the model’s performance could be improved with application of DA, there were still issues with the emission inventories, the low number of observations, and the model’s difficulties in capturing essential transport dynamics within the valley. Given the significant impact the Aburra Valley emission inventory has on air quality modeling and perceived issues with the available inventory, we built a highresolution emission inventory for the Aburra Valley metropolitan area. We also assessed the ability of a low-cost network’s available in the metropolitan area to track the dynamics of PMኼ.኿ correctly and use it as observations in the DA process. With recent developments in the production of low-cost sensors, it is possible to use these devices for DA. The DA system is composed by the EnKF, LOTOS-EUROS, the latest emission inventory, and the low-cost monitoring network. The high measurement density of this type of network is an advantage in the DA process, and it can be used in places that cannot afford a standard monitoring network. Finally, the city’s air quality was improved through the revised emission inventory. Combined with a new emission inventory and a denser observation network, we have proposed two ensemble-based DA methods to deal with the high uncertainties in the model. The first is a variant of the EnKF using a covariance-based estimator called Ensemble Kalman Filter Knowledge-Aided (EnKF-KA). The method’s novelty is that it allows for incorporating prior knowledge of the system directly in the assimilation process through a target covariance matrix. The second method, the Ensemble Time Local Hጼ Filter Knowledge-Aided (EnTLHF-KA) is a robust version of the EnKF-KA that incorporates an adaptive covariance inflation factor to reduce the impact of uncertainties. Both approaches were first analyzed using simple models to isolate the proposed technique’s advantages and drawbacks and to compare the results of this new method with traditional algorithms. The formulation of both new methods is sufficiently general to be applicable in other contexts. Finally, we implemented the proposed methods with the LE model and the lowcost monitoring network in the Aburra valley. We used the target matrix to limit the influence of the observations, following the complex topography of the valley. This reduced the impact caused by a low resolution of the dynamics within the valley of the meteorological input. The results of the proposed methods were compared with the results of the Local Ensemble Transform Kalman Filter (LETKF) algorithm. Both new methods outperformed the LETKF and resulted in a more accurate spatial representation of the PM concentrations. Thus, by applying the DA method to the Aburra Valley, the modeling and forecasting of air quality improved tremendously when compared with the observations." @default.
• W3198148910 created "2021-09-13" @default.
• W3198148910 creator A5041469322 @default.
• W3198148910 date "2021-01-01" @default.
• W3198148910 modified "2023-10-03" @default.
• W3198148910 title "Ensemble-based Data Assimilation For High-uncertainty systems: a case of study with Particulate Matter in the Aburra Valley" @default.
• W3198148910 doi "https://doi.org/10.4233/uuid:1724a16e-1f6a-423d-9a16-34e1afed3067" @default.
• W3198148910 hasPublicationYear "2021" @default.
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