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Towards more accurate predictions of
wildfire behavior
Modeling surface/atmosphere interactions
Mélanie ROCHOUX, Ph.D.
Pos...
①➀ INTRODUCTION: Regional-scale modeling viewpoint
• Fire danger
• Multi-physics multi-scale problem
• Regional-scale mode...
INTRODUCTION ● ● ●
Fire danger at operational level
• Global viewpoint: about 400 million hectares burnt every year
➙3 % o...
INTRODUCTION ● ● ●
Fire danger at operational level
Fine fuel moisture
code (FFMC)
Duff moisture
code (DMC)
Drought Code
(...
INTRODUCTION ● ● ●
Fire danger at operational level
Fine fuel moisture
code (FFMC)
Duff moisture
code (DMC)
Drought Code
(...
INTRODUCTION ● ● ●
Multi-physics multi-scale problem
2009 Black Saturday
bushfires
▶ 450 000 ha (1/3 of
the annual burnt a...
INTRODUCTION ● ● ●
Multi-physics multi-scale problem
• Complexity of wildfire behavior
• Wide range of length-scale (from ...
INTRODUCTION ● ● ●
Regional-scale modeling viewpoint
© FOREFIRE, Favone wildfire (30 ha)© FOREFIRE, Favone wildfire (30 ha...
①➀ INTRODUCTION: Regional-scale modeling viewpoint
• Fire danger
• Multi-physics multi-scale problem
• Regional-scale mode...
PH.D. RESEARCH ● ● ●
Modeling limitations
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
• ISSUE: How to accurately des...
PH.D. RESEARCH ● ● ●
Data assimilation for regional-scale forecast
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
11
En...
PH.D. RESEARCH ● ● ●
Data assimilation for regional-scale forecast
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
12
En...
PH.D. RESEARCH ● ● ●
Data assimilation for regional-scale forecast
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
13
En...
PH.D. RESEARCH ● ● ●
Data assimilation for regional-scale forecast
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
14
Co...
PH.D. RESEARCH ● ● ●
Data assimilation for regional-scale forecast
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
15
Co...
①➀ INTRODUCTION: Regional-scale modeling viewpoint
• Fire danger
• Multi-physics multi-scale problem
• Regional-scale mode...
➙ anridea.univ-corse.fr/
➙ OBJECTIVE: develop a
coupled fire-atmosphere
simulation prototype
Biomass thermal
degradation
V...
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
FIRE/ATMOSPHERE MODEL ● ● ●
Coupling strategy 18
• Lagrangian representa...
INTRODUCTORY SEMINAR May 2014Mélanie ROCHOUX
FIRE/ATMOSPHERE MODEL ● ● ●
Coupling strategy 19
• Introduction of a burning ...
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
FIRE/ATMOSPHERE MODEL ● ● ●
Coupling strategy 20
• Limitations in surfac...
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
FIRE/ATMOSPHERE MODEL ● ● ●
Atmospheric chemistry 21
• Emission sources
...
INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
FIRE/ATMOSPHERE MODEL ● ● ●
Atmospheric chemistry 22
• Transport and che...
SOURCES
➙ High-resolution biomass fuel
➙ Combustion
➙ Emission models
ATMOSPHERE
➙ Dynamics of the
smoke plume
➙ Chemistry...
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Fire/atmosphere interactions

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The challenges found on the route to developing quantitative wildfire spread models are two-fold. First, there is the modeling challenge associated with providing accurate mathematical representations of the multi-physics processes that govern wildfire dynamics. Second, there is the data challenge associated with providing accurate estimates of the input data and parameters required by the models.

One recent strategy to better account for time-varying weather conditions near the flame and the smoke plume consists in coupling a cost-effective front-tracking simulator of surface fire spread with a meso-scale three-dimensional atmospheric model. This system relies on a two-way coupling between the surface and the atmosphere. It is a promising strategy to predict fine-scale features of wildfire behavior as well as the atmospheric behavior (in terms of plume size, transport dispersion and smoke concentration) as demonstrated by the ANR-IDEA project.

➞ ANR-IDEA, see https://www.youtube.com/watch?v=C1-cfMmS1WM

Publié dans : Sciences
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Fire/atmosphere interactions

  1. 1. Towards more accurate predictions of wildfire behavior Modeling surface/atmosphere interactions Mélanie ROCHOUX, Ph.D. Postdoctoral fellow Meteorological Research Division Introductory seminar 13 June 2014 Environnement Canada Environment Canada
  2. 2. ①➀ INTRODUCTION: Regional-scale modeling viewpoint • Fire danger • Multi-physics multi-scale problem • Regional-scale modeling viewpoint ➁ Ph.D. RESEARCH: How to address modeling uncertainties? • Data assimilation at regional scale • Multi-physics simulation at flame scale ➂ STATE-OF-THE-ART: Fire/atmosphere coupling • Coupling strategy • Atmospheric emissions • Offline simulation case OUTLINE Introductory seminar 2 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
  3. 3. INTRODUCTION ● ● ● Fire danger at operational level • Global viewpoint: about 400 million hectares burnt every year ➙3 % of the Earth’s vegetation area © NASA Active fire map by MODIS over a 10-day period 9-18 August 2013 • Canadian Forest Fire Weather Index System • Fire Weather Index (FWI) ➙ numerical rating of fire potential intensity • Daily maps of fire weather • Main limitation ➙ meteorological index Canadian Wildland Fire Information System, NRCan INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX ➙ modis-fire.umd.edu/ 3
  4. 4. INTRODUCTION ● ● ● Fire danger at operational level Fine fuel moisture code (FFMC) Duff moisture code (DMC) Drought Code (DC) FUEL MOISTURE CODES FIRE WEATHER OBSERVATION S FIRE BEHAVIOR INDICES Air temperature and humidity, wind, 24-h rainfall Wind Air temperature and humidity, 24- h rainfall Air temperature, 24-h rainfall Initial spread index (ISI) Buildup index (BUI) Fire weather index (FWI) 4 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX • Canadian Forest Fire Weather Index System • Fire Weather Index (FWI) ➙ numerical rating of fire potential intensity • Daily maps of fire weather • Main limitation ➙ meteorological index ➙ cwfis.cfs.nrcan.gc.ca/home
  5. 5. INTRODUCTION ● ● ● Fire danger at operational level Fine fuel moisture code (FFMC) Duff moisture code (DMC) Drought Code (DC) FUEL MOISTURE CODES FIRE WEATHER OBSERVATION S FIRE BEHAVIOR INDICES Air temperature and humidity, wind, 24-h rainfall Wind Air temperature and humidity, 24- h rainfall Air temperature, 24-h rainfall Initial spread index (ISI) Buildup index (BUI) Fire weather index (FWI) 5 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX • Canadian Forest Fire Weather Index System • Fire Weather Index (FWI) ➙ numerical rating of fire potential intensity • Daily maps of fire weather • Main limitation ➙ meteorological index DMC: loosely compacted decomposing matter -nominal depth 7 cm DC: deep compact organic matter -nominal depth 18 cm FFMC: litter and other fine fuels -nominal depth 1.2 cm From Chelene Krezek-Hanes, Great Lakes Forestry Centre, Canadian Forest Service ➙ cwfis.cfs.nrcan.gc.ca/home
  6. 6. INTRODUCTION ● ● ● Multi-physics multi-scale problem 2009 Black Saturday bushfires ▶ 450 000 ha (1/3 of the annual burnt area in Australia) ▶ 173 people died ▶ strong fire/atmosphere coupling 300 km © NASA Fire/Surface ➙ Vegetation ➙ Topography Fire/Atmosphere ➙ Dynamics ➙ Chemistry Turbulence Gases & Aerosols Wind Biomass combustion Surface- to-canopy • Complexity of wildfire behavior • Wide range of length-scale (from mm to km) • Poorly-defined biomass fuels • Atmospheric external forcing • Climate change (megafires) ➙ CHALLENGE: How to accurately represent surface/atmosphere interactions in wildfires? 6 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
  7. 7. INTRODUCTION ● ● ● Multi-physics multi-scale problem • Complexity of wildfire behavior • Wide range of length-scale (from mm to km) • Poorly-defined biomass fuels • Atmospheric external forcing • Climate change (megafires) ➙ CHALLENGE: How to accurately represent surface/atmosphere interactions in wildfires? Radiation Wind Flame Pyrolysis Heat/mass fluxes Terrain topography EMISSIONS PYROLYSI S COMBUSTION FLUID DYNAMICS HEAT TRANSFER ATMOSPHERIC PHYSICS 7 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
  8. 8. INTRODUCTION ● ● ● Regional-scale modeling viewpoint © FOREFIRE, Favone wildfire (30 ha)© FOREFIRE, Favone wildfire (30 ha) Regional-scale viewpoint FIRE FRONT Burnt area Fire front Unburnt vegetation TOPOGRA PHY Terrain slope BIOMASS FUEL Moisture content Packing ratio Layer thickness WEATHER Wind magnitude Wind direction Rate Of spread • Front-tracking simulator • Prediction of the location of the surface fire front on horizontal plane • Local evaluation of the ROS along the normal direction to the fire front ➙ ROTHERMEL [1] ➙ BALBI [2] • Examples of simulator ➙ FOREFIRE [3], asynchronous Lagrangian solver ➙ FIREFLY [4], level-set solver [1] Rothermel, USDA Forest Service (1972) [2] Balbi et al. Comb. Flame (2004) [3] Filippi et al. Proc. Comb. Inst. (2013) [4] Rochoux, Ph.D. Ecole Centrale Paris (2014) 8 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
  9. 9. ①➀ INTRODUCTION: Regional-scale modeling viewpoint • Fire danger • Multi-physics multi-scale problem • Regional-scale modeling viewpoint ➁ Ph.D. RESEARCH: How to address modeling uncertainties? • Data assimilation at regional scale • Multi-physics simulation at flame scale ➂ STATE-OF-THE-ART: Fire/atmosphere coupling • Coupling strategy • Atmospheric emissions • Offline simulation case OUTLINE Introductory seminar 9 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
  10. 10. PH.D. RESEARCH ● ● ● Modeling limitations INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX • ISSUE: How to accurately describe wildfire spread? 10 Rochoux M.C., Ph.D. Ecole Centrale Paris (2014) • OBJECTIVE: Quantify and reduce uncertainty in ROS modeling Data assimilation for REGIONAL-SCALE wildfire spread forecast Environmental conditions Fire front locationPhysics-based modeling FLAME-SCALE multi-physics CFD simulations
  11. 11. PH.D. RESEARCH ● ● ● Data assimilation for regional-scale forecast INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX 11 Environmental conditions Fire front location • Anisotropy in wildfire spread ➙ complex spatial correlations along the fireline • Highly non-linear observation operator ➙ complex relationship between ROS model parameters and model counterparts of observations Simulated front Observed front (x1 , y1 ) (x2 , y2 ) (x3 ,y3 ) (x4 , y4 ) (x1 O ,y1 O ) (x2 O ,y2 O ) Distance between fronts FIREFLY wildfire spread simulator Parameters Initial condition Boundary conditions Comparison Simulated fronts Observations
  12. 12. PH.D. RESEARCH ● ● ● Data assimilation for regional-scale forecast INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX 12 Environmental conditions Fire front location • Parameter estimation ➙ temporal variability of the errors in the ROS model parameters (mid- to long-term) ➙ reduced-cost approach (Polynomial Chaos) • State estimation ➙ persistence of the correction of the model initial condition (short-term) Simulated front Observed front (x1 , y1 ) (x2 , y2 ) (x3 ,y3 ) (x4 , y4 ) (x1 O ,y1 O ) (x2 O ,y2 O ) FIREFLY wildfire spread simulator Parameters Initial condition Boundary conditions Comparison Simulated fronts Observations Ensemble Kalman filter Parameter estimation State estimation Analysis
  13. 13. PH.D. RESEARCH ● ● ● Data assimilation for regional-scale forecast INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX 13 EnKF analysis of synthetic observations + of fire front FORECAST True location Ensemble members ANALYSIS True location Ensemble members observations Ensemble generation for state estimation ➙ Anisotropy in wildfire spread ➙ Selection of spatially-distributed biomass fuel properties and distinct wind conditions between the ensemble members. Approach relevant for incomplete observations due to: ➙ opacity of the thermal plume ➙ limited monitoring
  14. 14. PH.D. RESEARCH ● ● ● Data assimilation for regional-scale forecast INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX 14 Controlled grassland fire experiment x [m] y [m] Extraction of the 600 K iso-temperature at 14-s time intervals 1min32s50s 1min46s1min04s 1min28s Time FRONT Wind 1m/s
  15. 15. PH.D. RESEARCH ● ● ● Data assimilation for regional-scale forecast INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX 15 Controlled grassland fire experiment state parameter state parameter state parameter • STATE ESTIMATION: non-uniform correction of the fire front • PARAMETER ESTIMATION: mean correction along the fireline ANALYSIS PERFORMANCE ANALYSIS FORECAST
  16. 16. ①➀ INTRODUCTION: Regional-scale modeling viewpoint • Fire danger • Multi-physics multi-scale problem • Regional-scale modeling viewpoint ➁ Ph.D. RESEARCH: How to address modeling uncertainties? • Data assimilation at regional scale • Multi-physics simulation at flame scale ➂ STATE-OF-THE-ART: Fire/atmosphere coupling • Coupling strategy • Atmospheric emissions • Offline simulation case OUTLINE Introductory seminar 16 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
  17. 17. ➙ anridea.univ-corse.fr/ ➙ OBJECTIVE: develop a coupled fire-atmosphere simulation prototype Biomass thermal degradation VEGETATION SCALES Combustion processes Surface/atmosphe re interactions FLAME SCALE ATMOSPHERIC SCALES Atmospheric dynamics and emissions FIRE/ATMOSPHERE MODEL ● ● ● Coupling strategy 17 INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX
  18. 18. INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX FIRE/ATMOSPHERE MODEL ● ● ● Coupling strategy 18 • Lagrangian representation of the subgrid-scale fire front Atmospheric model grid size (∼ 50 m) VEGETATION • Terrain data • Distribution: using CORINE ESA database and forest inventory (BDForêt) ➙ Sensible heat flux ➙ Water vapor flux ➙ Radiant temperature ➙WIND •Interpolation at the front markers •assumed constant over one model time step •1-D boundary layer model (mid- grid)
  19. 19. INTRODUCTORY SEMINAR May 2014Mélanie ROCHOUX FIRE/ATMOSPHERE MODEL ● ● ● Coupling strategy 19 • Introduction of a burning matrix • Burning ratio Rb = ratio of the burning area over the atmospheric grid area, ranging from 0 (no fire) to 1 (all burning). • 1 value of burning ratio per atmospheric model grid box SURFACE FLUXES REQUIRE: • Nominal convection heat flux • Nominal water loading • Nominal flame temperature Tn (1000 K) • Soil temperature from the atmospheric model Ts ➙ Sensible heat flux ➙ Water vapor flux ➙ Radiant temperature FORMULATION OF THE EQUIVALENT RADIANT TEMPERATURE Te: Te 4 = (1 - Rb) Ts 4 + Rb Tn 4
  20. 20. INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX FIRE/ATMOSPHERE MODEL ● ● ● Coupling strategy 20 • Limitations in surface wildfire spread modeling • Physical modeling inaccuracies ➙ Heat transfer to vegetation ➙ Local and steady-state assumption (canyons) • Absence of modeling for fire/atmosphere interactions Favone wildfire (30 ha) FOREFIRE Coupled Observed Two-way fire/atmosphere coupling FOREFIRE/MESO-NH
  21. 21. INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX FIRE/ATMOSPHERE MODEL ● ● ● Atmospheric chemistry 21 • Emission sources • Emissions during the biomass thermal degradation (e.g. soot particles) • Incomplete combustion ➙ Sensible heat flux ➙ Water vapor flux ➙ Radiant temperature ➙ Gas species emission • Species emitted in the surface layer (injection model) and mixed by the turbulence scheme (mainly CO) • Distinction of flaming and smoldering modes of combustion in the injection model • Emission factor database still incomplete, in particular for Mediterranean ecosystems
  22. 22. INTRODUCTORY SEMINAR June 13, 2014Mélanie ROCHOUX FIRE/ATMOSPHERE MODEL ● ● ● Atmospheric chemistry 22 • Transport and chemistry in the smoke plume • No parameterization of the smoke injection height ➙ explicit calculation • ReLACS chemical kinetic scheme used in MÉSO-NH (no aerosol scheme) Fire/Surface ➙ Vegetation ➙ Topography Fire/Atmosphere ➙ Dynamics ➙ Chemistry Turbulence Gases & Aerosols Wind Case study : Lançon de Provence (2005), Strada et al. 2012
  23. 23. SOURCES ➙ High-resolution biomass fuel ➙ Combustion ➙ Emission models ATMOSPHERE ➙ Dynamics of the smoke plume ➙ Chemistry (aerosols) ➙ Feedbacks on fire Overview of fire research OBSERVATIONS ➙ Detection ➙ Monitoring

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