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.
W
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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%
N
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– …