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! |
|