Toward a Dynamic Data Driven Application System for Wildfire Simulation

Size: px

Start display at page:

Download "Toward a Dynamic Data Driven Application System for Wildfire Simulation"

Phillip Dean
5 years ago
Views:

1 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 Coen National Center for Atmospheric Research Boulder, CO USA ICCS 05 May 23, 2005 Supported by NSF under grants ACI , ACI , ACI , ACI , and ACI

2 The Project An ongoing project to build a DDDAS for short-range forecasts of wildfire behavior with models steered by realtime weather data, fire-mapping images, and sensor streams.

3 University of Colorado at Denver Department of Mathematics Jan 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) Texas A&M University Dept. of Computer Science Wei Zhao (PI) Guan Qin (PI) The Team University of Kentucky Dept. of Computer Science Craig Douglas (PI) Deng Li (postdoc) Adam Zornes (graduate student) Rochester Institute of Technology Center for Imaging Science Anthony Vodacek (PI) Robert Kremens (Co-PI) Ambrose Onoye (postdoc) Ying Li (graduate student) Zhen Wang (graduate student) Matthew Weinstock (undergrad. student) National Center for Atmospheric Research Janice 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

4 3 Environmental Factors that affect Wildland Fire Behavior Fuel Moisture, mass/area, size, hardwood vs. conifer, spatial continuity, vertical arrangement Tree crowns Weather wind, temperature, relative humidity, precipitation Weather CHANGES: fronts, downslope winds, storm downdrafts, sea/land breezes, diurnal slope winds Topography Slope, aspect towards sun, features like narrow canyons, barriers (creeks, roads, rockslides, unburnable fuel) duff Surface litter, grass, shrubs, twigs, branches, logs

5 The original (non-dddas) application NCAR s Coupled Atmosphere Wildland Fire Environment model (CAWFE) Heat, water vapor, smoke ATMOSPHERE FIRE Atmospheric Dynamics Fire Propagation Fuel moisture FIRE ENVIRONMENT

6 Atmospheric Model Solve 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 Model Contains components representing: 1. Surface fire Spread of flaming front depends on wind, fuel, and slope. Based on Rothermel (1972) semi-empirical equations.

7 Fire Model Contains 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

the fire. W N Big Elk Fire (4400 acres) Pinewood Springs, CO 17 Jul 2002. 4 hr simulation.

8 Wildland Fire Modeling NCAR 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. W N Big Elk Fire (4400 acres) Pinewood Springs, CO 17 Jul hr simulation. x= y = ~50 m. Red: 10 o C buoyancy White: smoke Frame each 30 sec. Coen (2005) Intl. J. Wildland Fire

Example: Configured for a research problem 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.

9 Example: Configured for a research problem 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. Inputs Atmosphere Initialize atmosphere & provide later BCs with large-scale weather forecast Domain km Topography US 3 sec topography Fuel - Surface and canopy fuels. Mass/area Physical characteristics Fuel moisture 6.7 km

10 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

11 Dynamic Data Driven Application System: Wildfire Weather model Fire model Dynamic Data Assimilation Visualization Weather data Fuel map sources (GIS) Airborne multispectral images, satellite fire maps Surface fire/atm. sensors, telemetry Communication Supercomputing Software engineering

12 Spatial Data Sources for the Model Base map sources Aerial photos (Nat l High Alt.) Gridded orthoquads SRTM (terrain elevation) Satellite (Landsat, QuickBird) WASP (multispectral imaging camera) Fuels (AVHRR, GAP) RT 20 Max Elevation 5,215 Max Grade 20% Average Grade 12% RT 63 WASP project N 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)

High quality Kodak CCD High Performance Position Measurement System Position 5 m

13 Wildfire Airborne Sensor Program (WASP) D. McKeown B. Kremens M. Richardson 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

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

15 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) Data logger and thermocouples T ( o C) 800 We electronic ideally have developed suited collection acquisition to

16 Autonomous Environmental Detectors (Primarily for local weather but some burnovers) Data logger and thermocouples T ( o C) 800 We electronic ideally have developed suited collection acquisition to field a package versatile data temperature, C Major Features Reconfigure to rapidly deploy? GPS - Position Aware Versatile Data Inputs Voice or Data Radio telemetry Inexpensive seconds Time (sec. after ignition) Kremens, et al Int. J. Wildland Fire

GeoMAC Fire perimeter data http://www.geomac.

17 GeoMAC Fire perimeter data ArcIMS web application displaying current fire location. Based on Terra MODIS fire detection products and Incident Management team uploads to Web

18 The Big Picture ENKF 1D fire Matlab Hidden model update Out of order data assimilation Parallel ENKF, MPI New fire model Matlab PDE solv. fire Fortran Base fire, param. est. ENKF Web visualization Base ENKF Weather data input Fire data assimilation Prototype DDDAS NCAR code in MPI NCAR simple atmos. assim. NCAR atmos. assim, simul. data NCAR atmos+fire assim, simul. data NCAR DDDAS, stored real data NCAR DDDAS real-time, MPI NCAR interface Reconcile geo coords model/data Complete dataset from same fire Input map data (GIS) Auto map retrieval Create new hooks to model interface PDE Finite element fire model Fire Image assimilation Input image data Auto image & sensor data Fire FE interpolation New fire PDE model Atmos. sensor stream assimilation Input sensor streams Auto web weather data Status: DONE PROGRESS THINKING TO DO

19 Research and Education Accomplishments

1. Real-time application of NCAR model on real fires Large-scale gridded model forecast Apply NCAR coupled atmosphere-fire model to CO fires as real-time

Initialize a 15-km domain coupled atmospherefire model centered on fire location using current large-scale gridded weather forecast, fuel datasets, and fuel

20 1. Real-time application of NCAR model on real fires Large-scale gridded model forecast Apply NCAR coupled atmosphere-fire model to CO fires as real-time application Evaluate strengths/weaknesses of existing model Method: When a fire ignites Gather ignition location/time. Initialize a 15-km domain coupled atmospherefire 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 4 nested domains

21 Dynamic Data Assimilation 2. DDDAS Software Structure Driver Module Schedule state updates and calls to model functions Maintain space-time model state = ensemble of simulations 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 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

22 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 timestep 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

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

24 4. Data Assimilation The 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

25 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 Does not burn: 30% probability Burns: 70% probability Temperature Least squares solution: does not burn Ignition temperature

26 New EnKF technique for Fire Model Filter developed to control the solution s spatial gradient. Add quadratic form of difference between gradient of solution and gradient of ensemble mean before update solution. Example: 1D Fire Model i.e. Least squares fit of values and derivatives Johns and Mandel (2005) Envir. & Ecol. Statistics. (submitted)

27 5. Team Tutorials & Videoconferences Wildland Fire Modeling Forecasting and data assimilation Tutorial on the Ensemble Kalman Filter Tutorial on the NCAR atmospherefire 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,

28 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, multiinstitutional 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

Description of the fire scheme in WRF

Description of the fire scheme in WRF March 8, 2010 1 Introduction The wildland fire model in WRF is an implementation of the semi-empirical fire propagation model developed in Coen (2005) and Clark et