Sensitivity of the Plume Rise Model in the estimation of biomass burning plume injection heights in South America

• Ano de defesa: 2016
• Autor(a) principal: Gonzalo Andrés Guajardo Ferrada
• Orientador(a): Saulo Ribeiro de Freitas
• Banca de defesa: Nilton Manuel Évora do Rosário
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Instituto Nacional de Pesquisas Espaciais (INPE)
• Programa de Pós-Graduação: Programa de Pós-Graduação do INPE em Meteorologia
• País: BR
• Link de acesso: http://urlib.net/sid.inpe.br/mtc-m21b/2016/07.04.16.18

This study had the aim to evaluate the new developments on the Plume Rise Model (PRM), embedded into the BRAMS model. PRM computes the biomass burning plume injection heights and returns that information to the host model. Then, the atmospheric model releases all the fire emissions at this height. New developments are based on the initialization data used by the PRM, using fire size and fire radiative power (FRP) from remote sensing. The main difference between the two new versions is the conversion parameter ($\beta$) used to convert from FRP to the plume convective flux. In addition, a new scheme to generate daily fire emission fluxes is implemented, using the fire radiative energy (computed from remote sensing) in the Brazilian Biomass Burning Emission Model (3BEM-FRE). Model results using the three versions of the PRM are compared with observed airborne CO and O$_{3}$ data from the South American Biomass Burning Analysis (SAMBBA) campaign, which took place in southern Amazonia and Cerrado regions in September 2012. Results show that improvements in both 3BEM-FRE and PRM models, have a better performance in the vertical and horizontal reproduction of CO and O$-{3}$ than the original versions of both models, especially in the middle and upper troposphere, specially, reproducing fires over the Cerrado region. Nevertheless, new versions of both models have some difficulty to reproduce the emissions by the end of the campaign, probably due to the cumulus parameterization used, which overestimated the precipitation in the region of study.