Data assimilation in a LOTOS-EUROS chemical transport model for Colombia using satellite measurements

When considering air quality, notably in South America, it seems that we are falling behind more developed regions in exacerbating the issue. This shortfall serves not just as observation, but as a warning, as air quality problems here are rapidly escalating. Nevertheless, by examining how other countries have addressed similar issues, we can...

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...

Data Assimilation in High Dimensional Systems Using Local Particle Filters: Overcoming the curse of dimensionality in hydrology

This dissertation's ultimate goal is to provide solutions to two problems that the promising data assimilation method, called the Particle Filter, has when applied to high dimensional non-linear models, such as those often used in hydrological research and forecasting. Two local particle filters have been proposed to overcome three major issues....

Numerical optimization techniques for practical problems in Aerodynamics

The continuous increase in the number of flights in the last decades caused a steepgrowth of aviation-related pollution to the point that the aviation sector is responsible for3% of the global greenhouse gas emissions. Regulators have been slow at catching up withthis problem, and stringent emission targets have been put in place only very...

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Dynamic data-driven simulation (DDDS) incorporates real-time measurement data to improve simulation models during model run-time. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical...

Estimating Surface Heat Fluxes Using Temperature and Wetness Information: A Particle Data Assimilation Framework

Surface heat fluxes (latent and sensible heat over the land surface) play a key role in the land-atmosphere interaction, and their spatial pattern as well as temporal evolution are vital to the terrestrial water cycle and surface energy balance. Ideally, we want to have accurate estimates of spatially distributed and temporally continuous fluxes...

Data assimilation in the minerals industry: Real-time updating of spatial models using online production data

Declining ore grades, extraction at greater depths and longer hauling distances put pressure on maturing mines. Not enough new mines will be commissioned on time to compensate for the resulting shortages. Ore-body replacement rates are relatively low due to a reduced appetite for exploration. Development times are generally increasing and most...

Data Assimilation in Discrete Event Simulations: A Rollback Based Sequential Monte Carlo Approach

Data assimilation is an analysis technique which aims to incorporate measured observations into a dynamic system model in order to produce accurate estimates of the current state variables of the system. Although data assimilation is conventionally applied in continuous system models, it is also a desired ability for its discrete event...