The FireFly prototype data-driven wildifre spread simulator has been extended to cases with complex terrain topography. The performance of the EnKF algorithm is shown to be preserved for synthetic cases with spatially-varying vegetation and wind conditions. This emphasizes the potential of data assimilation to dramatically increase wildfire simulation accuracy in real-world wildfire events.

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Ensemble-­‐based
data
assimila.on
for
regional-­‐scale
simula.ons
of
wildfire
spread
M.C.
Rochoux1,2,
S.
Ricci1,
B.
Cuenot1,
A.
Trouvé3
1CERFACS
/
CNRS-­‐URA1875,
Toulouse,
France.
An
eye
on
wildfire
spread
modeling
2Ecole
Centrale
Paris,
Châtenay-­‐Malabry,
France.
3Dept.
Fire
ProtecMon
Engineering,
University
of
Maryland,
USA.
➡
Fundings
➡
MulM-­‐physics
mulM-­‐scales
problem:
complex
interacMons
of
pyrolysis,
combusMon
and
flow
dynamics,
atmospheric
dynamics/chemistry.
•
PredicMve
capability
of
wildfire
spread
simulaMons
•
OBJECTIVE:
Reduce
uncertainMes
in
rate
of
spread
➡
Issues:
•
LEFE-­‐ASSIM
(INSU,
2011-­‐2013)
Data
assimilaMon
algorithm
➡
Principles:
integrate
observaMons
of
fire
front
locaMons
into
a
fire
spread
model,
while
accounMng
for
the
effects
of
both
observaMon
and
modeling
errors,
to
improve
forecast
capability.
2)
StochasMc
computaMon
of
the
Kalman
gain
matrix
(accounMng
for
non-­‐lineariMes
in
the
fire
spread
model).
4)
Next
assimilaMon
cycle:
random
walk
model
(PE)
or
reconstrucMon
of
the
fire
front
locaMon
(SE).
1)
Monte
Carlo
based-­‐technique:
ensemble
of
predicted
fire
front
posiMons
associated
with
perturbed
input
parameters
of
the
ROS
model
(members).
➡
Temporal
correcMon
of
the
uncertainMes
in
fire
spread
3)
CalculaMon
of
retrospecMve
posterior
esMmates
of
the
control
parameters
(PE)
or
the
locaMon
of
the
simulated
fire
fronts
(SE).
Results:
Data-­‐driven
fire
spread
model
➡
Step
1:
ValidaMon
for
a
flat,
small-­‐scale
(4m
x
4m),
controlled
grassland
fire
(front
locaMon
extracted
from
thermal
infrared
imaging
provided
by
R.
Paugam,
King’s
College
London)
‣
UncertainMes
in
wind
condiMons
and
biomass
fuel
properMes
➡
Preserved
EnKF
performance
for
complex
terrain
topography
‣
Polynomial
Chaos
strategy
to
reduce
the
computaMonal
cost
of
the
EnKF
•
ANR-­‐09-­‐COSI-­‐006
IDEA
(France,
2010-­‐2013)
Radiation
Wind
Flame Pyrolysis
Heat flux Pollutants
Biomass
Flame-­‐scale
viewpoint:
flame
front
Regional-­‐scale
viewpoint:
fire
front
‣
Detailed
simulaMons
of
the
mechanisms
underlying
fires
(ex:
AVBP-­‐PYROWO-­‐PRISSMA)
Burnt
area
Unburnt
area
Front
‣
Fire
spread
described
as
a
1-­‐D
fireline
propagaMon
‣
Semi-­‐empirical
ROS
model
due
to
Rothermel
(1972)
in
FIREFLY
Rate
Of
Spread
FIREFLY
wildfire spread
simulator
Parameters
Initial condition
Boundary conditions
Comparison
Simulated fronts
Observations
Ensemble Kalman filter
Parameter estimation
State estimation
ROS = f(uw, ↵sl, Mv, ⌃v, v, ...)
x
a,(k)
t = x
f,(k)
t + Ke
t
⇣
yo
t + ⇠(k)
Gt(x
f,(k)
t )
⌘
Analysis Forecast ObservaMons Model
predicMons
Kalman
gain
matrix
➡
Ensemble
Kalman
filter
(EnKF):
parameter
esMmaMon
(PE)
vs.
model
state
esMmaMon
(SE)
Comparison
between
simulated
and
observed
front
posi4ons
at
4me
t
=
106
s
on
the
horizontal
plane
(x0,
y0);
EnKF
experiment
from
t
=
50
s
to
t
=
106s
with
assimila4on
at
14-­‐s
intervals.
The
simulated
front
posi4on
is
the
mean
posi4on
calculated
as
the
average
of
the
EnKF
ensemble.
LeL:
Forecast
(with
an
EnKF
update
at
t
=
92
s).
Right:
Analysis
(with
an
EnKF
update
at
t
=
106
s).
FORECAST
(t
=
106
s) ANALYSIS
(t
=
106
s)
+ observaMons
‣
Small
observaMon
error
(5
cm)
related
to
camera
spaMal
resoluMon
-­‐
-­‐
PE
—
SE
+ observaMons
-­‐
-­‐
PE
—
SE
➡
Step
2:
Extension
of
the
data
assimilaMon
technique
to
wildfire
spread
over
complex
terrain
topography
See
Rochoux
et
al.,
Proc.
Combust.
Inst.
(2013),
Nat.
Hazards
Earth
Syst.
Sci.
Discuss
(2014a,
2014b)
‣
SyntheMc
EnKF
experiment
(known
true
trajectory)
over
[0,
750
s].
FORECAST
(t
=
750
s)
ANALYSIS
(t
=
750
s)
+ observaMons
-­‐
-­‐
PE
—
SE
+ true
locaMon
—
SE
/
PE
‣
5
sources
of
uncertainMes:
wind
condiMons,
biomass
fuel
properMes.
‣
320
members
in
the
ensemble.
•
Control
parameters
stay
within
physical
range
(PE).
•
Accurate
tracking
of
the
fire
front
topology
(SE).
•
Much
reduced
scaoer
around
the
true
locaMon
of
the
fire
front.
Ongoing
research
See
Rochoux
et
al.,
VII
Int.
Conf.
on
Forest
Fire
Research
(Nov.
2014).
➡
Dual
parameter-­‐state
esMmaMon
approach
to
improve
forecast
capability
of
wildfire
spread
➡
EvaluaMon
of
the
EnKF
algorithm
on
real-­‐world
fire
hazards
➡
Extension
of
the
EnKF
algorithm
to
a
coupled
fire-­‐atmosphere
simulator
to
account
for
fire
plume
dynamics.
Contact:
M.C.
Rochoux,
Ph.D.
melanie.rochoux@graduates.centraliens.net
See
Ph.D.
thesis
(2014),
hop://www.cerfacs.fr/
3-­‐25800-­‐Ph.-­‐D.-­‐Thesis.php