@Article{PaugamWoAtFrScKa:2015:Pa2,
author = "Paugam, R. and Wooster, Martin and Atherton, Jonathan and Freitas,
Saulo Ribeiro de and Schultz, M. G. and Kaiser, J. W.",
affiliation = "{King’s College London} and {King’s College London} and {King’s
College London} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Institute for Energy and Climate
Research-Troposphere} and {Max Planck Institute for Chemistry}",
title = "Development and optimization of a wildfire plume rise model based
on remote sensing data inputs - Part 2",
journal = "Atmospheric Chemistry and Physics Discussion",
year = "2015",
volume = "15",
number = "6",
pages = "9815--9895",
abstract = "Biomass burning is one of a relatively few natural processes that
can inject globally significant quantities of gases and aerosols
into the atmosphere at altitudes well above the planetary boundary
layer, in some cases at heights in excess of 10 km. The injection
height of biomass burning emissions is therefore an important
parameter to understand when considering the characteristics of
the smoke plumes emanating from landscape scale fires, and in
particular when attempting to model their atmospheric transport.
Here we further extend the formulations used within a popular 1D
plume rise model, widely used for the estimation of landscape
scale fire smoke plume injection height, and develop and optimise
the model both so that it can run with an increased set of
remotely sensed observations. The model is well suited for
application in atmospheric Chemistry Transport Models (CTMs) aimed
at understanding smoke plume downstream impacts, and whilst a
number of wildfire emission inventories are available for use in
such CTMs, few include information on plume injection height.
Since CTM resolutions are typically too spatially coarse to
capture the vertical transport induced by the heat released from
landscape scale fires, approaches to estimate the emissions
injection height are typically based on parametrizations. Our
extensions of the existing 1D plume rise model takes into account
the impact of atmospheric stability and latent heat on the plume
up-draft, driving it with new information on active fire area and
fire radiative power (FRP) retrieved from MODIS satellite Earth
Observation (EO) data, alongside ECMWF atmospheric profile
information. We extend the model by adding an equation for mass
conservation and a new entrainment scheme, and optimise the values
of the newly added parameters based on comparison to injection
heights derived from smoke plume height retrievals made using the
MISR EO sensor. Our parameter optimisation procedure is based on a
twofold approach using sequentially a Simulating Annealing
algorithm and a Markov chain Monte Carlo uncertainty test, and to
try to ensure the appropriate convergence on suitable parameter
values we use a training dataset consisting of only fires where a
number of specific quality criteria are met, in-cluding local
ambient wind shear limits derived from the ECMWF and MISR data,
and steady state plumes and fires showing only relatively small
changes between consecutive MODIS observations. Using our
optimised plume rise model (PRMv2) with information from all
MODIS-detected active fires detected in 2003 over North America,
with outputs gridded to a 0.1\◦ 5 horizontal and 500m
vertical resolution mesh, we are able to derive wildfire injection
height distributions whose maxima extend to the type of higher
altitudes seen in actual observation-based wildfire plume datasets
than are those derived either via the original plume model or any
other parametrization tested herein. We also find our model to be
the only one tested that more correctly simulates 10 the very high
plume (6 to 8 km a.s.l.), created by a large fire in Alberta
(Canada) on the 17 August 2003, though even our approach does not
reach the stratosphere as the real plume is expected to have done.
Our results lead us to believe that our PRMv2 approach to
modelling the injection height of wildfire plumes is a strong
candidate for inclusion into CTMs aiming to represent this
process, but we note that significant advances in the
spatio-temporal resolutions of the data required to feed the model
will also very likely bring key improvements in our ability to
more accurately represent such phenomena, and that there remain
challenges to the detailed validation of such simulations due to
the relative sparseness of plume height observations and their
currently rather limited temporal coverage which are not
necessarily well matched to when fires are most active (MISR being
confined to morning observations for example).",
doi = "10.5194/acpd-15-9815-2015",
url = "http://dx.doi.org/10.5194/acpd-15-9815-2015",
issn = "1680-7367",
label = "lattes: 9873289111461387 4 PaugamWoAtFrScKa:2015:Pa2",
language = "en",
urlaccessdate = "25 abr. 2024"
}