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A Note on Dynamic Data Driven Wildfire Modeling Jan Mandel University of Colorado at Denver Janice L. Coen, Craig C. Douglas, Leopoldo P. Franca, Craig.

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Presentation on theme: "A Note on Dynamic Data Driven Wildfire Modeling Jan Mandel University of Colorado at Denver Janice L. Coen, Craig C. Douglas, Leopoldo P. Franca, Craig."— Presentation transcript:

1 A Note on Dynamic Data Driven Wildfire Modeling Jan Mandel University of Colorado at Denver Janice L. Coen, Craig C. Douglas, Leopoldo P. Franca, Craig Johns, Robert Kremens, Anatolii Puhalskii, Anthony Vodacek, Wei Zhao ICCS ‘04 June 7, 2004 Krakow, Poland Supported by NSF under grants ACI-0325314, ACI-0324989, ACI- 0324988, ACI-0324876, and ACI-0324910

2 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

3 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.

4 Inputs Atmosphere Initialize atmosphere & provide later BCs with MM5 forecast Topography 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

5 Big Elk Fire Simulation Pinewood Springs, CO 17 July 2002 Red: 10 o C buoyancy White: smoke Frame each 30 sec. W N

6 A Stochastic Reaction-Diffusion Equation Fire Model 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

7 Fire Jumping Road

8 Max Elevation5,215’ 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

9 Fuel Type National database. Overwrite with finer scale where available.

10 Example fire perimeter data

11 Fire Perimeter data (on site measurement)

12 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 Position5 m Roll/Pitch 0.03 deg Heading0.10 deg D. McKeown B. Kremens M. Richardson

13 Time Sequence of Fire Propagation Aerial Images from a Prescribed Burn

14 Image Processing Algorithms (AVIRIS Image from Vodacek et al. and Latham 2002, Int. J. Remote Sensing) 589 nm770 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)

15 Autonomous Environmental Detectors (Primarily for local weather) We have developed a versatile electronic acquisition package ideally suited to field data collection Major Features Reconfigure to rapidly deploy? Position Aware Versatile Data Inputs Voice or Data Radio telemetry Inexpensive Kremens, et al. 2003. Int. J. Wildland Fire Data logger and thermocouples

16 Dynamic Data Assimilation Reality Continously Updated Time-Space Model Data Present Time Data acquisition steering Prediction error Estimation of model state and parameters from data Prediction

17 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

18 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)

19 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

20 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

21 Standard Approach to Data Assimilation Simulation time Analysis cycle Data Bayesian update Advance time

22 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

23 Assimilating Out of Sequence Data (if we can store all time-steps) Simulation time Analysis cycle Advance time Bayesian update Data Advance time

24 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

25 Assimilating Out of Sequence Data (re-create time-steps as needed) Simulation time Analysis cycle Advance time-step + to data time Bayesian update Data Advance time Data

26 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% probability Does not burn: 30% probability Least squares solution: does not burn Temperature Ignition temperature

27 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

28 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

29 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 –…


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