@InProceedings{FreitasLoSiSiChPrSe:2004:MoTrBi,
author = "Freitas, Saulo Ribeiro de and Longo, Karla and Silva Dias, Maria
Assuncao Faus da and Silva Dias, Pedro Leite da and Chatfield,
Robert and Prins, Elaine and Setzer, Alberto",
affiliation = "Instituto Nacional de Pesquisas Espaciais, Centro de Previs{\~a}o
do Tempo e Estudos Clim{\'a}ticos (INPE.CPTEC)",
title = "Monitoring the transport of biomass burning emissions in South
America",
booktitle = "Anais...",
year = "2004",
organization = "Conferencia Cientifica do LBA, 3.",
abstract = "The atmospheric transport of biomass burning emissions in the
South American and African continents is monitored with the aid of
numerical simulation of air mass motions using the tracer
transport capability of the atmospheric model RAMS (Regional
Atmospheric Modeling System) coupled to an emission model. In this
application, the mass conservation equation is 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 are
parameterized and introduced into the model. The sources are
distributed spatially and temporally and assimilated daily
according to the biomass burning locations detected by remote
sensing. Advection 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, is also introduced. Sinks associated
with wet and dry removal of aerosol particles and chemical
transformation of gases are parameterized and introduced into the
mass conservation equation. An operational system was implemented
producing daily 48-hour numerical simulations (24-hour forecast)
of the mass concentrations for CO and PM2.5, in addition to
traditional meteorological fields. Time series of PM2.5 measured
at the surface are compared with the model results and demonstrate
the good forecasting ability of the model.",
conference-location = "Bras{\'{\i}}lia",
conference-year = "27-29 jul.",
copyholder = "SID/SCD",
language = "en",
organisation = "LBA",
targetfile = "Freitas_LBA_Monitoring_.pdf",
urlaccessdate = "08 maio 2024"
}