globalchange  > 气候变化与战略
DOI: 10.1016/j.atmosenv.2020.117507
论文题名:
Forecasting PM10 and PM2.5 in the Aburrá Valley (Medellín, Colombia) via EnKF based data assimilation
作者: Lopez-Restrepo S.; Yarce A.; Pinel N.; Quintero O.L.; Segers A.; Heemink A.W.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 232
语种: 英语
英文关键词: Air quality ; Forecasting ; Landforms ; Stochastic models ; Stochastic systems ; Urban transportation ; Assimilation system ; Chemical transport models ; Data assimilation systems ; Emission uncertainties ; Ensemble Kalman Filter ; Environmental issues ; Particulate Matter ; Population expansion ; Population statistics ; air quality ; data assimilation ; environmental issue ; Kalman filter ; particulate matter ; spatiotemporal analysis ; aerosol ; air pollution ; air quality ; Article ; chemical reaction ; circadian rhythm ; Colombia ; dry deposition ; forecasting ; gas ; particulate matter ; priority journal ; stochastic model ; surface property ; time series analysis ; wet deposition ; white noise ; Aburra Valley ; Andes ; Antioquia [Colombia] ; Colombia
学科: Air quality modelling ; Chemical transport model ; Data assimilation ; Ensemble kalman filter ; Particulate matter
中文摘要: 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. © 2020 Elsevier Ltd
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被引频次[WOS]:20   [查看WOS记录]     [查看WOS中相关记录]
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160752
Appears in Collections:气候变化与战略

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作者单位: Mathematical Modelling Research Group at Universidad EAFIT, Medellín, Colombia; Biodiversity, Evolution and Conservation Research Group at Universidad EAFIT, Medellín, Colombia; Department of Applied Mathematics at TU Delft, Delft, Netherlands; TNO Department of Climate, Air and Sustainability, Utrecht, Netherlands

Recommended Citation:
Lopez-Restrepo S.,Yarce A.,Pinel N.,et al. Forecasting PM10 and PM2.5 in the Aburrá Valley (Medellín, Colombia) via EnKF based data assimilation[J]. Atmospheric Environment,2020-01-01,232
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