Toward a Dynamic Data Driven Application System for Wildfire Simulation

Jan Mandel, Lynn S. Bennethum, Mingshi Chen, Janice L. Coen, Craig C. Douglas, Leopoldo P. Franca, Craig J. Johns, Minjeong Kim,

Andrew V. Knyazev, Robert Kremens, Vaibhav Kulkarni, Guan Qin, Anthony Vodacek, Jianjia Wu, Wei Zhao, Adam Zornes

Presenter: Janice CoenNational Center for Atmospheric Research

Boulder, CO USA

ICCS ‘05May 23, 2005

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

The ProjectAn ongoing project to build a

DDDAS for short-range forecasts of wildfire behavior with models steered by real-time weather data, fire-mapping images, and sensor streams.

The TeamUniversity of Colorado at DenverDepartment of MathematicsJan Mandel (PI)Leo Franca (Co-PI)Tolya Puhalskii (Co-PI)Craig Johns (Co-PI)Mingshi Chen (postdoc)Keith Wojciechowski (graduate student)Bedrich Sousedik (graduate student)Mingeong Kim (graduate student)Vaibhav Kulkarni (graduate student)Jonathan Beezley (graduate student)

Rochester Institute of TechnologyCenter for Imaging ScienceAnthony Vodacek (PI)Robert Kremens (Co-PI)Ambrose Onoye (postdoc)Ying Li (graduate student)Zhen Wang (graduate student)Matthew Weinstock (undergrad. student)

University of KentuckyDept. of Computer ScienceCraig Douglas (PI)Deng Li (postdoc)Adam Zornes (graduate student)

Texas A&M UniversityDept. of Computer ScienceWei Zhao (PI)Guan Qin (PI)

National Center for Atmospheric ResearchJanice Coen (PI)

Other Collaborators:USDA Forest Service Missoula Tech. Development Center – UAVs, SAFE

Univ. of Montana (Natl. Cntr. Landscape Fire Analysis)Univ. of Utah - SCIRun enhancements

3 Environmental Factors that affect Wildland Fire Behavior

Fuel Moisture, mass/area, size, hardwood vs. conifer, spatial continuity, vertical arrangement

Weather wind, temperature, relative humidity, precipitation Weather CHANGES: fronts, downslopewinds, storm downdrafts, sea/land breezes, diurnal slope winds

TopographySlope, aspect towards sun, features like narrow canyons, barriers (creeks, roads, rockslides, unburnable fuel)

duff Surface litter, grass, shrubs, twigs, branches, logs

Tree crowns

The original (non-DDDAS) application

NCAR’s Coupled Atmosphere – Wildland Fire –Environment model (CAWFE)

FIRE

Atmospheric Dynamics

ATMOSPHEREHeat, water vapor, smoke

Fire Propagation

FIRE ENVIRONMENT

Fuel moisture

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

• 3-dim., time dependent• Nonhydrostatic, anelastic• Terrain-following coordinates,

vertically stretched grid• 2-way interacting nested

domains• Coarse grain parallelization

• Initialization of atmospheric environment using large-scale gridded weather forecast (RUC, MM5, ETA, etc.)

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

• Tracks “smoke” dispersion• Aspect-dependent solar heating of

ground

Fire ModelContains components representing:

1. Surface fire– Spread of “flaming front” depends on

wind, fuel, and slope. Based on Rothermel (1972) semi-empirical equations.

– Post-frontal heat/water vapor release

2. Crown fire– If the surface fire produces enough

heat, it heats, dries, and ignites the tree canopy.

3. Heat, water vapor, and smoke fluxes released by fire into atmosphere

Wildland Fire ModelingNCAR’s coupled atmosphere-fire model

simulates the spread of a fire, the impacts of the heat release on atmospheric motions, and the

feedback of fire-induced winds on the fire.

NBig Elk Fire (4400 acres)

Pinewood Springs, CO

17 Jul 2002.

4 hr simulation.

∆x=∆y = ~50 m.

Red: 10 oC buoyancy

White: smoke

Frame each 30 sec.

W

Coen (2005) Intl. J. Wildland Fire

6 nested domains:

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

Example: Configured for a research problem

InputsAtmosphere• Initialize atmosphere & provide later

BCs with large-scale weather forecast

Topography• US 3 sec topography

Fuel - Surface and canopy fuels.• Mass/area • Physical characteristics

Fuel moisture

Domain 6

6.7 km

6.7 km

Making it a DDDAS…• Research model -> real-time application• Forecast should be based on all available data:

– fuel maps, airborne images, internet weather data, field data sensor streams, and raw weather station streams

• The modeling paradigm must change.– Initialize & let run -> Assimilation of data -> Assimilation of

out of sequence (delayed) data• Forecast should change quickly when data arrives• The system should provide measurement steering• The system should provide animated visualization

and user steering over the internet

Dynamic Data Driven Application System: Wildfire

Weather data

Fuel map sources (GIS) Weather model

Fire model

Dynamic Data Assimilation

Airborne multispectralimages, satellite fire maps

Surface fire/atm. sensors, telemetryVisualization

SupercomputingCommunication Software engineering

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

N

RT 20

RT 63

WASP project

Base map sources• Aerial photos (Nat’l High Alt.)• Gridded orthoquads• SRTM (terrain elevation)• Satellite (Landsat, QuickBird)• WASP (multispectral imaging

camera)• Fuels (AVHRR, GAP)

Data sources• Fire (GeoMAC satellite + sit.

reports/WASP/others)• Terrain (Shuttle Radar Topographic

Mission)• Meteorology - MADIS (surface

meteorology data and fuel moisture) and gridded weather forecasts

• AEDs (Temperature, winds, humidity, radiation, etc. Autonomous Environmental Detectors)

Spatial Data Sources for the Model

D. McKeownB. KremensM. Richardson

Wildfire Airborne Sensor Program (WASP)

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

High Performance Position Measurement System•Position 5 m•Roll/Pitch 0.03 deg•Heading 0.10 deg

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

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• Derived temperatures • Direction fire is spreading• Derived fuels? (NDVI)

Autonomous Environmental Detectors (Primarily for local weather… but some burnovers)

We have developed a versatile electronic acquisition package ideally suited to field data collection

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

Data logger and thermocouples

0

100

200

300

400

500

600

700

800

11250 11750 12250 12750 13250

seconds after ignition

tem

pera

ture

, C

Time (sec. after ignition)

T (oC)

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

GeoMAC Fire perimeter datahttp://www.geomac.gov

ArcIMS web application displaying current fire location.

Based on Terra MODIS fire detection products and Incident Management team uploads to Web

The Big PictureENKF 1D fire

Matlab

New fire model Matlab

PDE solv. fireFortran

Base ENKF

DONE

Fire data assimilation

NCAR interface

NCAR atmos. assim, simul. data

Out of order data assimilation

Fire FE interpolation

NCAR atmos+fire assim, simul. data

Fire Image assimilation

Complete dataset from same fire

Atmos. sensor stream assimilation

Input sensor streams

NCAR code in MPIPrototype DDDAS

Input map data (GIS)

Input image data

NCAR DDDAS, stored real data

NCAR DDDAS real-time, MPI

Auto map retrieval

Auto web weather data

Auto image & sensor data

Web visualizationBase fire, param.

est. ENKF

Hidden modelupdate

PROGRESS THINKING TO DOStatus:

Weather data input

NCAR simpleatmos. assim.

PDE Finite element fire model

Parallel ENKF, MPI

Reconcile geo coords model/data

New fire PDE model

Create new hooks to model interface

Research and Education Accomplishments

Method: When a fire ignites…• Gather ignition location/time.• Initialize a 15-km domain coupled atmosphere-

fire model centered on fire location using current large-scale gridded weather forecast, fuel datasets, and fuel moisture.

• Spawn nest finer model domains: 5 km, 1.67 km, and 0.55 km domains.

• Fire ignites in finest domain at observed ignition time/place.

• Fire propagation modeled throughout the 48 hr forecast

Large-scale gridded model forecast

4 nested domains

• Apply NCAR coupled atmosphere-fire model to CO fires as real-time application

• Evaluate strengths/weaknesses of existing model

1. Real-time application of NCAR model on real fires

Dynamic Data Assimilation2. DDDAS Software Structure

Ensemble Filter Module•Adjust ensemble by a Bayesian update

Model Module•Initialize ensemble•Advance ensemble in time•Get observation data•Compare model prediction to observation

Driver Module•Schedule state updates and calls to model functions•Maintain space-time model state = ensemble of simulations

• Each module can be exchanged independently• Simple versions of the modules for R&D and testing, build

complexity gradually• Collaborative cvs repository used by everyone

3. Framework for Assimilating Out of Sequence Data

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

Need to maintain (implicitly) an ensemble of time-space state vectors

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

Advance time

Advance time

Bayesian update

Sim

ulat

ion

time

Analysis cycle

Data

4. Data AssimilationThe standard Ensemble Kalman Filter (EnKF)

approach:

• The model state is a probability distribution represented by an ensemble of simulations– Run ensemble of simulations from initial conditions with random

perturbations: how far they spread = uncertainty

• The data probability distribution is represented by the measurement values and the associated error estimates

• The model state is then updated using the Bayes theorem assuming that the probability distributions are approximately normal

• Works well in meteorology and oceanography but fails for assimilation of data about where the fire is

Assumption of normally distributed errors will not work here

• Probability distributions (also of the solution) are too far from Gaussian• The problem is too nonlinear

Probability density

Burns: 70% probability

Does not burn: 30% probability

Least squares solution: does not burn

Ignition temperatureTemperature

New EnKF technique for Fire ModelFilter developed to control the solution’s spatial gradient.

Example: 1D Fire ModelAdd quadratic form of difference between gradient of solution and gradient of ensemble mean before update solution.

i.e. Least squares fit of values and derivatives

Johns and Mandel (2005) –Envir. & Ecol. Statistics. (submitted)

5. Team Tutorials & Videoconferences• Wildland Fire Modeling• Forecasting and data assimilation• Tutorial on the Ensemble Kalman

Filter• Tutorial on the NCAR atmosphere-

fire model• Tutorial on Particle Filters and

Sequential Monte Carlo • Tutorial on Particle Filters and

Sequential Monte Carlo II• Observation of Fire Propagation in

LWIR• Walk Through the Prototype

Ensemble Kalman Filter Code• Introduction to the Software

Architecture of the NCAR Atmosphere-Fire Model

• Ensemble Kalman Filter (for the 1D fire model)

• An Introduction to Lagrangian and Eulerian Coordinates

• Walkthrough the Coupled Ensemble Kalman - Weather / Fire Code

• Coarse/Fine Mesh Averaging and Demonstration of Model in Real-Time

• Constrained Ensemble Kalman Filter for Data Assimilation in PDE

• Fire Image and Ground Sensor Content and Format

• Code management & Capturing online data• ODE integrators for discontinuous solutions• Code management and software

engineering• The Big Picture, or Who Is Doing What and

Why and When• Current code overview and what the

missing pieces are• Steps to creating synthetic images of

wildland fire• Where did all the cycles go?• Makefiles Primer• Analysis of the wildfire module, adaptation

for DDDAS

Summary• Work in progress• Accomplishments:

– First application of a coupled NWP: fire behavior model in real time

– Software architecture for collaborative development of a complex interdisciplinary, multi-institutional project in place & being used

– Methodology for unique and widely applicable data assimilation techniques developed & being applied to the model application

• out-of-order data arrival• non-Gaussian distributed errors

• Wide education & student participation

http://www-math.cudenver.edu/~jmandel/fires/