TY - JOUR
T1 - Forecasting PM10 and PM2.5 in the Aburra Valley (Medellin, Colombia) via EnKF based data assimilation
AU - Lopez Restrepo, Santiago
AU - Yarce , Andrés
AU - Pinel , Nicolas
AU - Quintero , O.L.
AU - Segers, Arjo
AU - Heemink, A.W.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Air quality modelling
KW - Chemical transport model
KW - Data assimilation
KW - Ensemble kalman filter
KW - Particulate matter
UR - http://www.scopus.com/inward/record.url?scp=85084524191&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2020.117507
DO - 10.1016/j.atmosenv.2020.117507
M3 - Article
AN - SCOPUS:85084524191
SN - 1352-2310
VL - 232
SP - 1
EP - 16
JO - Atmospheric Environment
JF - Atmospheric Environment
M1 - 117507
ER -