Towards a Real-Time Data Driven Wildland Fire Model Jan Mandel, Jonathan D. Beezley, Soham Chakraborty, Janice L. Coen, Craig C. Douglas, Anthony Vodacek and Zhen Wang University of Colorado National Center for Atmospheric Research University of Kentucky Rochester Institute of Technology Supported by the NSF under grants CNS-0325314, 0324989, 0324988, 0324876, 0324910, 0719641, 0719626, and 0720454
Goals � The model � faster than real time � Coupled weather-fire � calibrated from measurements � Data assimilation: incorporate real data while the model is running � sparse data (weather stations) � large image datasets (aerial photographs) � data acquisition steering � data arriving delayed and out of order � capable of adjusting a highly nonlinear model � Real-time visualization over the internet in the field
Wildfire DDDAS Structure Forecast Observation function Model Synthetic data Interpret Weather Data Acquisition Data Assimilation Real data pool Adjust Compare Fire Real time data Initial conditions Aerial imaging Map sources (GIS) Sensors, telemetry Fuel Data Weather data
The coupled weather-fire model
Coupled weather - fire spread model WRF-SFIRE ATMOSPHERE: WRF MODEL Weather: atmospheric dynamics and parametrized physics (clouds, rain,…) Wind Heat, water vapor 2D fire propagation FIRE
SFIRE: Spread fire model by a level set method � Burning area: f(x,y)<0, fireline f(x,y)=0 � Spread rate R in normal direction from fuel, slope ∂ + f ∇ = � Level set function evolves by the PDE R f 0 ∂ t � Solved numerically by RK of order 2 (Euler too biased), f must always decrease (stabilizing effect) � Coupled with the Weather Research and Forecasting Model (WRF) � Suitable for data assimilation
Level set function representation of the burning area
Coupled WRF-SFIRE simulation The fire propagates from two line ignitions and one circle ignition, in the process of merging. The arrows are the horizontal wind at the ground level. The false color is the fire heat flux.The fire front on the right has an irregular shape and is slowed down because of air being pulled up by the heat created by the fire. This kind of fire behavior cannot be modeled by empirical spread models alone and requires the two-way interaction with the atmosphere.
SFIRE software structure and atmosphere-fire coupling � Model itself independent of WRF � Coupled by single driver module as a surface physics process � SFIRE input: wind velocities u,v, fuel data, ignition locations and time � Interpolate winds to fire grid, advance fire one time step, compute fuel burned assuming exponential decay from ignition � Submesh granularity: fireline is a straight line crossing fire cells, computation of fuel burned by approximate integration, can be done exactly too � SFIRE output: add up heat flux from fuel burned over atmospheric cells, translate into temperature and moisture tendencies (with assumed exponential decay over several layers, WRF cannot take flux boundary conditions)
WRF-SFIRE status and immediate goals � Now works with OpenMP parallelization and made- up data � In progress now: read real data in ad-hoc fashion � Summer 08: DM parallel (our version of WRF with fire mesh refinement won’t run with DM now), read data in WRF-approved manner � Summer 08: add crown fire (its own level set function) � Fall 08: ready for release, merge with the current WRF version � End 08: code freeze � Spring 09: release as part of WRF download
Data Assimilation
Data assimilation a.k.a. statistical estimation Model state, including Model state with the Synthetic data uncertainty data assimilated n o i a t v r e s b O n o Advance time i t Bayes thm c n u Advance time f Data Balances the uncertainty in the model and in the data � Use of new data reduces the uncertainty in the model state � Gaussian probability distributions → Kalman filter � Uncertainty represented by an ensemble � ensemble Kalman filters (still Gaussian) � particle filters (need huge ensembles) �
Parallel software structure Ensemble member 1 Synthetic Synthetic CPU1 Fire - Fire - Observa- Observa- State State State State Morphing … … atmosphere atmosphere tion tion ensemble data data model model function function CPU n Kalman filter … Ensemble member N Parallel Synthetic Synthetic linear CPU k Fire - Fire - Observa- Observa- State State State State algebra … … atmosphere atmosphere tion tion data data model model function function CPU m Real data Advancing the ensemble in time Data assimilation pool
But Ensemble Kalman Filter fails for fire models Probability distributions are strongly non-gaussian � (burning/not burning) Discrepancies are in the fire position as well as in � the intensity Unlike features in other problems (atmosphere, � ocean,…) fire in the wrong location will not conveniently dissipate, but grows instead, and soon the whole domain is burning
Dealing with position errors: Morphing Ensemble Filters
Image registration and morphing = + λ interpolate between two maps: ( ) f x f x ( Tx ) λ = = given f f and g f , how to find ? T 0 1 solve minimization problem for registration distance = + − + + � � d f g ( , ) min f ( I T ) g T T T automatically, by multilevel optimization The transformation is found automatically without any human input. (Picture Gao and Sederberg, 1998)
Automatic morphing combines fire positions and intensities in one shot
Morphing Ensemble Filter � Represent the ensemble members u i as morphs of one fixed state plus a residual : ( ) ( = + + � u u r I T ) i i i � Apply data assimilation to the extended state mapping T i, + residuals r i instead of the states u i � After the members are advanced in time, use the previous morph mappings as a good initial guess. � Now the ensemble Kalman filter can move the fireline easily!
Data Assimilation by Morphing EnKF 500 500 500 400 400 400 300 300 300 Y (m) Y (m) Y (m) 200 200 200 100 100 100 0 0 0 0 100 200 300 400 500 0 100 200 300 400 500 0 100 200 300 400 500 X (m) X (m) X (m) Forecast fire Analysis fire Data position (model position (data output) accounted for, continue running Instead of having linear combinations of the the model) states create a number of smaller fires, linear combinations of the transformed states move a single fire around.
Data assimilation in coupled atmosphere/fire model WRF/SFIRE Standard EnKF Simulated data Morphing EnKF
Observation function
Observation function and data sources Create synthetic data from the � model state to be matched against Determine the real data sensor Synthetic weather station data: � location reports location, timestamp, wind velocity, temperature, and humidity. Must determine which cell in grid, where, and if fire is present in cell or a neighboring cell. Fire in Synthetic infrared aerial images in multiple � several frequencies bands. Needs cells ray tracing and computation of the flame height and direction
Data Acquisition
Autonomous Environmental Detectors Primarily for local weather… but some burnovers Data logger and thermocouples 800 700 600 T ( o C) 500 temperature, C 400 300 200 100 0 11250 11750 12250 12750 13250 seconds after ignition Reconfigure to rapidly deploy Time (sec. after ignition) GPS - Position Aware Versatile Data Inputs Kremens, et al. 2003. Int. J. Wildland Fire Voice or Data Radio telemetry Inexpensive
Autonomous Environmental Sensorss positioned so as to provide weather � conditions near a fire, are mounted at various heights above the � ground on a pole with a ground spike will survive burnovers by low intensity � fires the temperature and radiation � measurements provide a direct indication of the fire front passage and the radiation � measurement can also be used to determine the intensity of the fire the sensors transmit data and can be � reprogrammed by radio
Wildfire Airborne Sensor Program (WASP) D. McKeown B. Kremens M. Richardson High Performance Position Measurement Color or Color Infrared System Camera • 4k x 4k pixel format • Position 5 m • 12 bit quantization • Roll/Pitch 0.03 deg • High quality Kodak CCD • Heading 0.10 deg Fire Detection Cameras • 640 x 512 pixel format • 14 bit quantization • < 0.05K NEDT
Next: Put it All Together and Test on a Real Fire � The morphing EnKF method works reliably now – integrate it into our production quality data assimilation framework � Integrate the data assimilation code with the real wildfire-atmosphere code � Connect the input with real-time data acquisition, under development separately � Integrate the output with Google Earth visualization � Test on reanalysis of the Esperanza 2006 fire
Data assimilation status and software goals � Morphing EnKF now works, a research code � Now data = whole array in state � Need to speed up the automatic registration � Summer 08: image data, one observation function as a template � Fall 08: station data (will require different algorithms… more math) � Spring 09: a presentable code
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