A Note on Dynamic Data Driven Wildfire Modeling

Jan MandelUniversity of Colorado at Denver

Janice L. Coen, Craig C. Douglas, Leopoldo P. Franca, Craig Johns, Robert Kremens, Anatolii Puhalskii, Anthony

Vodacek, Wei Zhao

ICCS ‘04June 7, 2004

Krakow, Poland

Supported by NSF under grants ACI-0325314, ACI-0324989, ACI-0324988, ACI-0324876, and ACI-0324910

Dynamic Data Driven Application System: Wildfire

Weather model

Fire model

Dynamic Data Assimilation

Weather data

Map sources (GIS)

Aerial photos, fuel

Sensors, telemetry

SupercomputingCommunication

Visualization

Software engineering

Clark-Hall Atmospheric Model

• 3-dim., time dependent

• Nonhydrostatic, anelastic

• Terrain-following coordinates, vertically stretched grid

• 2-way interacting nested domains

• Coarse grain parallelization

• Coupled with an Empirical fire model (based on BEHAVE)

• Large-scale initialization of atmospheric environment using RUC, MM5, ETA, etc.

• Models formation of clouds, rain, and hail in “pyrocumulus” clouds over fires

• Short and long wave atmospheric radiation options

• Tracks “smoke” dispersion

• Aspect-dependent solar heating

Solve prognostic fluid dynamics equations of motion for air momentum, a thermodynamic variable, water vapor and precipitation on a finite difference grid.

InputsAtmosphere• Initialize atmosphere & provide

later BCs with MM5 forecastTopography• US 3 sec topography

Fuel - Surface and canopy fuels.Loading & Physical characteristics

assoc. with Fuel Model.Fuel moisture.

6 nested domains:

10 km, 3.3 km, 1.1 km, 367 m, 122 m, 41 m atm. grid spacing. (Fuel grids can be much finer.). Timestep in finest domain < 1 sec.

Example: Experimental set-up

Domain 6

6.7 km

6.7 km

Big Elk Fire SimulationPinewood Springs, CO 17 July 2002

Red:

10 oC buoyancy

White: smoke

Frame each 30 sec.

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T• Strike a balance between too simple and too slow• Fuel is consumed and generates heat• Heat diffuses, is carried by wind, and radiates into

the atmosphere• Embers are carried randomly into distance, cause a

local rise of temperature and ignition

Fire Jumping Road

Max Elevation 5,215’Max Grade 20%Average Grade 12%

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RT 20

RT 63

WASP project

Base map sources• Aerial photos (Nat’l High Alt.)• SRTM (terrain)• Digital orthoquads• Satellite (Landsat, QuickBird)• WASP (color camera)• Fuels (AVHRR, GAP)

Data sources• Fire (GeoMAC/WASP/others)• Terrain (Shuttle Radar Topographic Mission, SRTM)• RAWS and other Met data• AEDs (Temperature, winds, humidity, radiation, etc. Autonomous Environmental Detectors)

Spatial Data Sources for the Model

Fuel Type

National database.

Overwrite with finer scale where available.

Example fire perimeter data

Fire Perimeter data (on site measurement)

Wildfire Airborne Sensor Program (WASP)

High Performance Position Measurement System

Color or Color Infrared Camera • 4k x 4k pixel format• 12 bit quantization• High quality Kodak CCD

Fire Detection Cameras • 640 x 512 pixel format• 14 bit quantization• < 0.05K NEDT

•Position 5 m•Roll/Pitch 0.03 deg•Heading 0.10 deg

D. McKeownB. KremensM. Richardson

Time Sequence of Fire PropagationAerial Images from a Prescribed Burn

Image Processing Algorithms(AVIRIS Image from Vodacek et al. and Latham 2002, Int. J. Remote Sensing)

589 nm 770 nm/779 nm

Original image content• Pixel location• Spectral data• Algorithms to register to model grid

• auto extraction of tie points• affine transform

Reduced image content• Normalized Thermal Index?

(MWIR-LWIR)/(MWIR+LWIR) • Fire location only (model grid)• Derived temperatures?• Derived fuels?

NDVI (like AVHRR)

Autonomous Environmental Detectors (Primarily for local weather)

Major FeaturesReconfigure to rapidly deploy?Position AwareVersatile Data InputsVoice or Data Radio telemetryInexpensive

Kremens, et al. 2003. Int. J. Wildland Fire

Data logger and thermocouples

Dynamic Data Assimilation

Reality

Continously Updated Time-Space Model

Data

PresentTime

Data acquisition steering

Prediction error

Estimation of model state and parameters from data

Prediction

Ensemble Filter: Incorporating Data by a Bayesian Update

• Model state is a probability distribution represented as an ensemble of simulation states

• Data is a probability distribution represented as the measured values plus error bounds (or better error info)

• Observation function relates observations data and simulation states

Model State (Forecast Ensemble)

Data: Values, Observation Function

Updated Model State (Analysis Ensemble)

Bayes Theorem

Data Exchange and Formats

• Unified format for all data exchange– Observations– Ensemble members (simulation states)

• Must contain enough information to construct the observation function:

observation=function(simulation state) (from the physics, what the observation would have been in

the absence of simulation errors)• Data packets:

(coordinates, time-stamp, quantity name, scaling, values)

Dynamic Data Assimilation

Ensemble Filter Module

Driver Module

Model Module

Model•Weather-fire simulation•Postprocessing

•Initialize ensemble•Advance ensemble in time•Get observation function•Get observation data

•Adjust ensemble by a Bayesian update

Data Acquisition•Weather data•Image data•Sensor data

•Initialize•Export state and stop•Import state and restart

•Check for new data•Get data•Request data

Standard Approach to Data Assimilation by Ensemble Filter

1. Generate an initial ensemble by a random perturbation of initial conditions

2. Repeat the analysis cycle:i. Advance ensemble states to a target time by

solving the model PDEs in time

ii. Inject data with time-stamps equal to the target time: modify ensemble states by a Bayesian update

Standard Approach to Data AssimilationS

imul

atio

n ti

me

Analysis cycle

Data

Bayesian update

Advance time

Advance time

Assimilating Out of Sequence Data(if we can store all time-steps)

1. Generate initial ensemble by a random perturbation of initial conditions

2. Repeat the analysis cycle:i. Clone the ensemble at the initial time and

advance the ensembles except the clone to the next time-step

ii. Inject data into all time-steps: modify the ensemble with states at all time-steps as a single big state, by a Bayesian update

Assimilating Out of Sequence Data(if we can store all time-steps)S

imul

atio

n ti

me

Analysis cycle

Advance time

Bayesian update

Data

Advance time

Assimilating Out of Sequence Data(re-create time-steps as needed)

1. Generate initial ensemble by a random perturbation of initial conditions

2. Repeat the analysis cycle:i. Clone the ensemble at the initial time and other

times as needed, advance all ensembles except the clones to their target times, which should include the time-stamp(s) of the data

ii. Inject data into all time-steps: modify the ensemble of states for all stored time-steps as a single big state, by the Bayesian update

Assimilating Out of Sequence Data(re-create time-steps as needed)S

imul

atio

n ti

me

Analysis cycle

Advance time-step + to data time

Bayesian update

Data

Advance time

Data

Data

Least Squares Are No Good Here• Probability distributions (also of the solution) are too far

from Gaussian• The problem is too nonlinear

Probability density Burns: 70%

probabilityDoes not burn: 30% probability

Least squares solution: does not burn

Temperature

Ignition temperature

Visualization

• Platform independent: – Web, java based

– Browsing from anywhere: PDAs, cell phones,…

• Map or 3d terrain, flames• Scenario movies• Maps overlaid with various scenarios• Local outcome probabilities (burn or not)• Input of firefighting scenarios

Supercomputing Resources

• What resources needed– Multiple simulations (ensemble 50-500)– Multiple time steps (time-space 10-500)

• Actual time step 0.5s, f consists of multiple steps– Multiple interactive firefighting scenarios (1-3)– Mesh sizes

• Innermost, finest 200 by 200 by 60• Outermost, coarsest 50 by 50 by 60• Total grid point approx. innermost times 2• 12 fields

This is Work in Progress

• Existing:– Clark-Hall model with fire– Fire: stochastic-reaction-convection diffusion PDE

• In Progress:– Dynamic data assimilation by Ensemble Kalman Filter– Data conversion and formats

• Future:– Use Non-Gaussian Ensemble Filter (literature)– Dynamic data assimilation into the atmosphere-fire model– Real data sources– Visualization– Couple fire PDE model with the Clark-Hall atmosphere model– …