Monitoring the transport of biomass burning emissions in South America

被引:171
作者
Freitas, SR
Longo, KM
Diasb, MAFS
Diasb, PLS
Chatfield, R
Prins, E
Artaxo, P
Grell, GA
Recuero, FS
机构
[1] NOAA, Res, Forecast Syst Lab, Boulder, CO USA
[2] Univ Sao Paulo, BR-05508 Sao Paulo, Brazil
[3] NASA, Ames Res Ctr, Moffett Field, CA 94035 USA
[4] NOAA, NESDIS, ORA, Madison, WI USA
[5] Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA
基金
巴西圣保罗研究基金会; 美国海洋和大气管理局; 美国国家航空航天局;
关键词
aerosol transport; air pollution; atmospheric modeling; biomass burning; climate change; long-distance transport; weather forecast;
D O I
10.1007/s10652-005-0243-7
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:135 / 167
页数:33
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