Coupled fire-atmosphere-fuel moisture-smoke online modeling with - - PowerPoint PPT Presentation

Coupled fire-atmosphere-fuel moisture-smoke online modeling with WRF-SFIRE

Jan Mandel, University of Colorado Denver Adam K. Kochanski, University of Utah Sher Schranz, NOAA/CIRA Martin Vejmelka, AVAST Supported by NASA grant NNX13AH59G and NSF grant DMS-1216481 September 30, 2017 The 3rd Annual Meeting of SIAM Central States Section Colorado State University, Fort Collins, CO

Range of scales affecting fires

• Atmospheric and fire scales

Global weather model Mesoscale weather model

Navier-Stokes (DNS)

Large Eddy Simulator (LES)

1 m 10 cm

Wildland Fires Flames Flamelets Structural Fires

boundary conditions boundary conditions boundary conditions

Range of scales in WRF

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!SFIRE Atmosphere!model!WRF Surface!fire!spread!model

Wind Heat!and! vapor! fluxes

Fuel!moisture!model

Surface!air! temperature,! rela?ve! humidity, rain

Chemical!transport! model!WRFBChem

Fire! emissions! (smoke)

RAWS fuel moisture stations VIIRS/MODIS fire detection HRRR forecast Data assimilation

WR WRF-SF SFIRE comp mponents

Data assimilation Satellite moisture sensing Data assimilation

WRF-SFIRE origins and sources

• USDA Forest Service wildfire modeling system: BEHAVE -

fire properties at one point, FARSITE - surface fire spread

• NCAR’s Coupled Atmosphere-Wildland Fire Enviroment

(CAWFE), based on the Clark-Hall research weather code and fire propagation by tracers

• The Weather Research and Forecasting model (WRF),

a standard supported community weather code, free download, widely used

• Level set method
• In the US, government data is free: fuel maps from

LANDFIRE, weather data from NOAA, satellite fire detection from NASA, high resolution terrain from USGS,…

A Brief History of WRF-SFIRE

• 2004: Connection of fire model from CAWFE and WRF proposed

(Patton and Coen)

• 2006: Fire propagation by tracers connected to WRF and support of

refined surface fire mesh (Patton, Michalakes)

• 2007: Level set method
• 2008: Real data (Beezley)
• 2009: Distributed memory parallelism from WRF
• 2010, 2011: Versions of the model included in WRF release as WRF-

Fire

• 2012: Integrate fuel moisture model
• 2013: Coupling with chemical transport by WRF-Chem for smoke
• 2013: Operational Israel National wildfire system MATASH
• 2017: NCAR selects the version from WRF release as the foundation
• f the operational Colorado Fire Prediction System (CO-FPS)

Representation of the fire area by a level set function

• The level set function is given on center nodes of the fire mesh
• Interpolated linearly, parallel to the mesh lines
• Fireline connects the points where the interpolated values are zero

Evolving the fireline by the level set method

Level set function L Level set equation Fire area: L<0 Right-hand side < 0 → Level set function goes down → fire area grows

∂L ∂t = −R ∇L

The fire model: fuel consumption

ignition fuel time

Time constant of fuel:

30 sec - Grass burns quickly 1000 sec – Dead & down branches(~40% decrease in mass over 10 min)

Integrating fuel consumption over mesh cells, with submesh fire region representation

Coupling with WRF-ARW

• WRF-ARW is explicit

in time

• Physics packages

including fire are called only in the last Runge-Kutta substep

• Fire module inputs

wind, outputs heat and vapor flux

dΦ dt = R Φ

( )

Φ* = Φt + Δt 3 R Φt

( )

Φ** = Φt + Δt 2 R Φ*

( )

Φt+Δt = Φt + ΔtR Φ**

( )

Runge-Kutta order 3 integration in time

The fire model is running on a finer mesh than the atmosphere model

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Wind interpolation

• Spread rates for different fuels depend on wind

at “midflame” height given by the fuel time

• Linear interpolation of wind as a function of

log(height/roughness height). Exact if the wind profile is exactly logarithmic (just like piecewise linear interpolation is exact for linear functions) independently of the vertical mesh spacing

• If there are no WRF nodes under 6m,

mathematically equivalent to the BEHAVE wind reduction factors.

• It gets tricky
• The heights of the nodes are computed from the

geopotential, which is a part of the solution

• The geopotential varies a lot near the fire
• The atmospheric and fire mesh have different

resolutions

• The result depends on the roughness length.
• Take the roughness length from LANDUSE or fuels?

wind speed roughness height midflame height

Structure of the coupled WRF-SFIRE code

Core: time step for the level set equation, compute fuel loss. Dimensionless. Phys: sensible and latent heat fluxes from fuel loss, fire rate of spread Driver: get grid variables, get flags, interpolation calls, OpenMP loops, DM halos WRF: call sfire_driver Util: interpolation, WRF stubs, debug I/O,… Atm: one tile: temperature and moisture tendencies from heat fluxes wind Model: one time step, one tile: winds in, heat fluxes out WRF: error messages, log messages, constants,… WRF: add tendencies heat and moisture tendencies

Standalone Sfire code

Core: time step for the level set equation, compute fuel loss. Dimensionless. Phys: sensible and latent heat fluxes from fuel loss, fire rate of spread Util: interpolation, WRF stubs, debug I/O,… Model: one time step, one tile: winds in, heat fluxes out Wrf_fakes: error messages, log messages, constants,… MAIN

WRF parallel infrastructure - MPI and OpenMP

• Distributed memory (DM):

halo exchanges between grid patches: each patch runs in one MPI process; programmer only lists the variables to exchange

• Shared memory (SM):

OpenMP loops over tiles within the patch

• Computational routines are

tile callable.

• Fire model executes on the

same horizontal tiles as the atmosphere model, in the same threads

MPI OpenMP threads, multicore

Example: 2 MPI processes 4 threads each

The parallel infrastructure constrains the algorithms used.

patch

halo

tile

Parallelism in WRF-SFIRE: a PDE solver in WRF physics layer

(meant for pointwise calculations)

Summary of the model

• Atmosphere modeled by a standard numerical weather

prediction software (NWP)

• Fire is 2D, parameterized by Rate of Spread formula

(Rothermel),

• The fire Rate Of Spread (ROS) is a function of
• Fuel composition and fuel moisture
• Slope (fire spread uphill faster)
• Wind
• Heat and water vapor are released by the fire into the

atmosphere, the quantity decreases exponentially with time from start of burning

• Much simpler and cheaper than physics-based models,

faster than real time (making prediction possible)

• Can capture an important range of fire behavior

18

Idealized LES simulation of a small-scale prescribed burn (FireFlux experiment)

• FireFlux prescribed burn of 155 acres (0.63 km2) prairie
• Model setup:
• 1 domain, 1000m x 1600m, 10m horizontal resolution
• 80 vertical levels from 2-1200m AGL
• Fire grid resolution – 1m

FireFlux picture from Clements et al. 2008

MT ST

FireFlux Experiment Simulation (2010) (microscale)

Field experiment Craig Clements et al., 2011 Visualization by Bedrich Sousedik

20

Timing of the fire front passage through the towers (5m and 4.5m air temperature)

Wildland Fire Behavior and Risk Forecasting PI: Sher Schranz, CSU/CIRA

As of: March 1, 2016

Coupled atmosphere-fire model can capture an important range of fire behavior

2013 Patch Springs Fire, UT

Fuel Moisture Model

• 1st order time-lag:
• In time T, E-m(t) decreases by 1/e
• Equilibrium E depends on the current

atmosphere state in the surface layer (temperature, RH, pressure)

• Assimilation of Remote Automated

Weather Station (RAWS) 10h data

• Trend surface (regression) to extent

RAWS data to the whole domain

• Extended Kalman filter on a coarse mesh
• Mix T =1h, 10h, 100h moisture at

every location with proportions from actual fuel data

Fuel Moisture Nowcasting

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Simulated fire area and fuel moisture for Barker Canyon fire 2012

in-plume concentration ~3000μg /m3 (3mg/m3)

2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% 20.0% 22.0% 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000

• 12

12 24 36 48 60 72 84 96 Fuel moisture Fire area (ha) Time since 09.09.2012 00:00 local (h)

Simulated fire area and fuel moisture

Simulated fire area Observed fire area Integrated fuel moisture simulated by the fuel moisture model

25

Example #1 Simulation of Barker Canyon Fire (smoke as a passive tracer)

in-plume concentration ~3000μg /m3 (3mg/m3)

Simplified approach – no chemistry 96h simulation done in 12h 52min on 640 CPUs, with the first 24h forecast ready in 3h 13min

Simulated fire perimeter Observed fire perimeter

26

Example #1 Simulation of Barker Canyon Fire (smoke as a passive tracer)

in-plume concentration ~3000μg /m3 (3mg/m3)

Fuel Moisture

The Online System

WRFXCTRL: Submit jobs WRFXPY: Retrieve data,run jobs, process

• utput

Online Data: NOAA, USGS, … Model: WRF-SFIRE WRFXWEB: Delivery to users

Run online

Delivery online

• http://www.openwfm.org/wiki/WRF-SFIRE_user_guide
• https://readthedocs.org/projects/wrfxpy
• https://github.com/openwfm - sources
• http://demo.openwfm.org/ - cloud visualization server
• http://www.openwfm.org/wiki/Publications

Data driven modeling - assimilation of fire detection data from satellites:

MS06 tomorrow 10:50am