首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Monitoring the transport of biomass burning emissions in South America
Authors:Saulo R Freitas  Karla M Longo  Maria A F Silva Dias  Pedro L Silva Dias  Robert Chatfield  Elaine Prins  Paulo Artaxo  Georg A Grell  Fernando S Recuero
Institution:(1) Center for Weather Prediction and Climate Studies, CPTEC/INPE, Brazil;(2) University of São Paulo, Brazil;(3) NASA Ames Research Center, U.S.A.;(4) NOAA/NESDIS/ORA, Madison, WI, U.S.A.;(5) Cooperative Institute for Research in Environmental Science (CFRES), University at Colorado and NOAA Research — Forecast Systems Laboratory, Boulder, CO, U.S.A.
Abstract:The atmospheric transport of biomass burning emissions in the South American and African continents is being monitored annually using a numerical simulation of air mass motions; we use a tracer transport capability developed within RAMS (Regional Atmospheric Modeling System) coupled to an emission model. Mass conservation equations are solved for carbon monoxide (CO) and particulate material (PM2.5). Source emissions of trace gases and particles associated with biomass burning activities in tropical forest, savanna and pasture have been parameterized and introduced into the model. The sources are distributed spatially and temporally and assimilated daily using the biomass burning locations detected by remote sensing. Advection effects (at grid scale) and turbulent transport (at sub-grid scale) are provided by the RAMS parameterizations. A sub-grid transport parameterization associated with moist deep and shallow convection, not explicitly resolved by the model due to its low spatial resolution, has also been introduced. Sinks associated with the process of wet and dry removal of aerosol particles and chemical transformation of gases are parameterized and introduced in the mass conservation equation. An operational system has been implemented which produces daily 48-h numerical simulations (including 24-h forecasts) of CO and PM2.5, in addition to traditional meteorological fields. The good prediction skills of the model are demonstrated by comparisons with time series of PM2.5 measured at the surface.
Keywords:aerosol transport  air pollution  atmospheric modeling  biomass burning  climate change  long-distance transport  weather forecast
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号