Forecasting PM10 and PM2.5 in the Aburrá Valley (Medellín, Colombia) via EnKF based data assimilation. (1st July 2020)

• Record Type: Journal Article
• Authors: Lopez-Restrepo, Santiago; Yarce, Andres; Pinel, Nicolas; Quintero, O.L.; Segers, Arjo; Heemink, A.W.
• Is Part Of: Atmospheric environment. Volume 232(2020)
• Publication Date: 2020-07-01
• Subjects: Data assimilation -- Air quality modelling -- Chemical transport model -- Ensemble kalman filter -- Particulate matter; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2020.117507
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: A data assimilation system for the LOTOS-EUROS chemical transport model has been implemented to improve the simulation and forecast of PM10 and PM2.5 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. The data assimilation system is an Ensemble Kalman filter with covariance localization based on specification of uncertainties in the emissions. Observations assimilated were obtained from a surface network for the period March–April of 2016, a period of one of the worst air quality crisis in recent history of the region. In a first series of experiments, the spatial length scale of the covariance localization and the temporal length scale of the stochastic model for the emission uncertainty were calibrated to optimize the assimilation system. The calibrated system was then used in a series of assimilation experiments, where simulation of particulate matter concentrations was strongly improved during the assimilation period, which also improved the ability to accurately forecast PM10 and PM2.5 concentrations over a period of several days. Higlights: First application of Data Assimilation in Air Quality over Colombia. A High-Resolution Chemical Transport Model was implemented over the Aburrá Valley. Emissions inventories are the main uncertainty source in Air Quality studies. Data Assimilation reduced uncertainty in PM10 and PM2.5 representation. Forecast of PM10 and PM2.5 is possible by Ensemble-based Data Assimilation.