new forecasting wind-driven wildfires using an inverse modelling...
TRANSCRIPT
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Oriol Rios, Wolfram Jahn, Guillermo Rein
Forecasting Wind-Driven Wildfires Using An Inverse Modelling Approach
Cargèse, 16-5-2013
Numerical Wildfires Workshop
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Outline
Background idea
Methodology
– Forward model
– Optimization. Tangent lineal model & automatic differentiation
– Synthetic validation
Cases explored
– Fire fronts
– Wind speed
– Wind speed and direction
– Fuel depth
Perturbed data
Conclusions & Further Work2
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Background Idea
Hard to gather information to initialize models in operative situations
Complex model require high computational capacity and time
Wildfire responders need forecasting tools
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M. Rochoux et al., J. Mandel et al. started using data assimilation in wildfires
La Riba, 2011La Jonquera, 2011
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Background Idea
Jahn et al. successfully used DA to forecast fire in compartments
The algorithm is based on the fact that invariants exists for a certain amount of time
W. Jahn et al. Forecasting fire dynamics using inverse computational fluid dynamics
and tangent linearisation, Advances in Engineering Software4
ex: Entrainment coefficient
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Background Idea
Invariants exists and represent one or more physical quantities (i.e. wind speed or fuel properties).
Use a simple yet reliable model to explore DA capacities for wind-driven wild fires.
Versatile DA algorithm regarding available data (invariants reversibility)
Ensure positive lead time
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Input fire fronts positions (airborne, satellite) during an assimilation window to identify the invariants
min.
Data
Forward model
Invariants
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Source of data
6La Jonquera, 2011
CSIRO-UPC, 2008FuSE project - Bushfire CRCNgarkat CP experimental burnings (SAus)
Airborne and Satellite imaged
Pléiades SATMODIS/Google
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Rate of spread (surface fire)
11 variables (7+4)
variables
parameters
The forward model: Rothermel’s+Huygens
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Huygens principle. Firelets expansion
G D Richards. The properties of elliptical wildfire growth for time dependent fuel and meteorological
conditions. Combustion science and technology,1993
+ Anderson length-to-breadth correlation
The forward model: Rothermel’s+Huygens
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Generates no trivial perimeters (fuel heterogeneity)
The forward model: Rothermel’s+Huygens
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Cost function
Distance between angular correspondent vertexes
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Optimization
Tangent linear model
How to calculate the gradient?
Automatic differentiation(forward or adjoint)
Program
dProgram
aProgram11
First Guess
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Optimization. Automatic differentiation
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sin(x1/x2)
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Algorithm
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Validation
I used synthetically data generated with Rothermel’s+Huygens model (without casting invariants) and initialized with parameters from Behave (Anderson)
We studied 4 different invariants cast
4 invariants
3 invariants
3 invariants
3 invariants
+ wind speed
+ wind direction
+ fuel depth14
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Casting the invariants. 1st cast. (4 invariants)
Moisture-fuel Invariant
Wind speed invariant
Wind factor invariant
Wind direction
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Step-to-step exampleCasting the invariants. 1st cast. (4 invariants)
What if the invariant’s evolution is known?
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Convergence to true valueCasting the invariants. 1st cast. (4 invariants)
•Influence initial guess•Divergence correction17
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2nd cast of invariants: 3 invariants & wind speed
Moisture-fuel Invariant
Wind factor invariant
Wind direction
Input Data
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2nd cast of invariants: 3 invariants & wind speed
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3rd cast of invariants: 3 invariants & wind (U,θ)
Moisture-fuel Invariant
Wind factor invariant
Wind directionInput Data
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3rd cast of invariants: 3 invariants & wind (U,θ)
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4th cast of invariants: 3 invariants & fuel depth (x,y)
RoS linear to fuel depth
Wind direction
Input Data
Length to breadth ratio (Anderson)
LBI
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4th cast of invariants: 3 invariants & fuel depth (x,y)
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Data with noise (4th case)
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Computing time
Positive lead time26
Windows of validity30 min forecast
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Conclusions
Formulation of the problem is general enough that is suitable to work with many observation (& data contexts).
Solution method is fast and positive lead times are already possible with desktop computer.
Invariants can be turned into input data for increased accuracy and speed if reliable data arrives
The proper invariant cast must be done according to the available data, otherwise multiplicity might be a problem.
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Further work
Challenge the algorithm with real data (cases needed)
Increase the number of invariants to several dozen by means of adjoint modeling approach
Assimilate more input data (fire intensity, flame height...)
Move to more powerful optimization routine that require High Performance Computing (eg, evolutionary algorithms)
Used more sophisticated forward models (i.e WFDS, FireFoam, ForeFire...)28
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Thank you!
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Forecast made in 1900 of the fire-fighting in the year 2000.
Villemard 1910, National Library of France
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Invariants range
Monte Carlo analysis varying 6 Rothermel’s variables (20000 sets) within the range established by Scott and Burgan 2005.
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Invariants influence
Cost function variation when the invariants are perturbed ±20% of its base value
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Invariants influence
Base value and varying range for Rothermel’s variables
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