Elias Nino-Ruiz
My name is Elias D. Nino-Ruiz, I am a Computer Scientist whose research focus on numerical schemes and efficient data assimilation methods.
Chapters authored
Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems
Part of the book: Real-World Applications of Genetic Algorithms
Efficient Matrix-Free Ensemble Kalman Filter Implementations: Accounting for Localization
This chapter discusses efficient and practical matrix-free implementations of the ensemble Kalman filter (EnKF) in order to account for localization during the assimilation of observations. In the EnKF context, an ensemble of model realizations is utilized in order to estimate the moments of its underlying error distribution. Since ensemble members come at high computational costs (owing to current operational model resolutions) ensemble sizes are constrained by the hundreds while, typically, their error distributions range in the order of millions. This induces spurious correlations in estimates of prior error correlations when these are approximated via the ensemble covariance matrix. Localization methods are commonly utilized in order to counteract this effect. EnKF implementations in this context are based on a modified Cholesky decomposition. Different flavours of Cholesky-based filters are discussed in this chapter. Furthermore, the computational effort in all formulations is linear with regard to model resolutions. Experimental tests are performed making use of the Lorenz 96 model. The results reveal that, in terms of root-mean-square-errors, all formulations perform equivalently.
Part of the book: Kalman Filters
Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries
Particulate matter (PM) is one of the most problematic pollutants in urban air. The effects of PM on human health, associated especially with PM of ≤2.5μm in diameter, include asthma, lung cancer and cardiovascular disease. Consequently, major urban centers commonly monitor PM2.5 as part of their air quality management strategies. The Chemical Transport models allow for a permanent monitoring and prediction of pollutant behavior for all the regions of interest, different to the sensor network where the concentration is just available in specific points. In this chapter a data assimilation system for the LOTOS-EUROS chemical transport model has been implemented to improve the simulation and forecast of Particulate Matter in a densely populated urban valley of the tropical Andes. The Aburrá Valley in Colombia was used as a case study, given data availability and current environmental issues related to population expansion. Using different experiments and observations sources, we shown how the Data Assimilation can improve the model representation of pollutants.
Part of the book: Environmental Sustainability
Medellin Air Quality Initiative (MAUI)
This chapter book presents Medellín Air qUality Initiative or MAUI Project; it tells a brief story of this teamwork, their scientific and technological directions. The modeling work focuses on the ecosystems and human health impact due to the exposition of several pollutants transported from long-range places and deposited. For this objective, the WRF and LOTOS-EUROS were configurated and implemented over the región of interest previously updating some input conditions like land use and orography. By other side, a spinoff initiative named SimpleSpace was also born during this time, developing, through this instrumentation branch a very compact and modular low-cost sensor to deploy in new air quality networks over the study domain. For testing this instrument and find an alternative way to measure pollutants in the vertical layers, the Helicopter In-Situ Pollution Assessment Experiment HIPAE misión was developed to take data through the overflight of a helicopter over Medellín. From the data obtained from the Simple units and other experiments in the payload, a citogenotoxicity analysis quantify the cellular damage caused by the exposition of the pollutants.
Part of the book: Environmental Sustainability