forecasting wind-driven wildfires using an inverse modelling approach

7/30/2019 Forecasting Wind-Driven Wildfires Using An Inverse Modelling Approach

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Oriol Rios, Wolfram Jahn, Guillermo Rein

Forecasting Wind-Driven WildfiresUsing An Inverse Modelling Approach

Cargse, 16-5-2013Numerical 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 dataassimilation in wildfires

La Riba, 2011La Jonquera, 2011


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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 anassimilation window to identify the invariants

min.

Data

Forward model

Invariants


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Source of data

6La Jonquera, 2011

CSIRO-UPC, 2008

FuSE project - Bushfire CRC

Ngarkat CP experimental burnings (SAus)

Airborne and Satellite imaged

Pliades SATMODIS/Google


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Rate of spread (surface fire)

11 variables (7+4)

variables

parameters

The forward model: Rothermels+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: Rothermels+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

Rothermels+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 example


What if the invariants evolution is known?

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Convergence to true value


Influence initial guess

Divergence correction17


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2nd cast of invariants: 3 invariants & wind speed



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,)



Wind directionInput Data

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3rd cast of invariants: 3 invariants & wind (U,)

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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 alreadypossible 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 ofinvariants to several dozen by

means ofadjoint modeling approach

Assimilate more input data (fire intensity, flame height...)

Move to more powerful optimization routine that requireHigh 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 Rothermels variables (20000 sets) within the

range established by Scott and Burgan 2005.

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Invariants influence

Base value and varying range forRothermels variables

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forecasting wind-driven wildfires using an inverse modelling approach

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