Wildfires. Chun-Lung Lim and Charles Erwin

Transcription

1 Wildfires Chun-Lung Lim and Charles Erwin

2 THE WEATHER RESEARCH AND FORECAST MODEL (WRF)

3 What makes WRF next-generation mesoscale forecast model? Advance the understanding and the prediction of mesoscale precipitation systems and to promote closer ties between the research and operational forecasting communities. Efficiency, portability, maintainability, and extensibility.

4 SOFTWARE ARCHITECTURE

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7 Runge-Kutta Method

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9 Driver Layer Handles run-time allocation and parallel decomposition of model domain data structures. Organization, management, interaction, and control over nested domains, including the main time loop in the model. High level interfaces to I/O operations on model domains. It is interface to other components when WRF is part of a larger coupled system of applications.

10 Mediation Layer Encompasses one time-step of a particular dynamical core on a single model domain. The current WRF implementation uses the Message Passing Interface (MPI) communication package. Shared-memory parallelism over tiles in the solve routines is using OpenMP.

11 ICC s s High Performance Computing

12 Model Layer Comprises the actual computational routines that make up the model: advection, diffusion, physical parameterizations, and so forth. The subroutines are called through a standard Model Layer Interface. The interface ensures that a Model Layer package incorporated into WRF will work on any parallel computer. Model layer routines have data dependencies rely on the mediation layer to perform the necessary interprocessor communication.

13 The Registry It is a concise database of information about WRF data structures and a mechanism for automatically generating large sections of WRF code. The Registry data base is a collection of tables that lists and describes the WRF state variables and arrays with their attributes such as dimensionality and so fourth. Registry generates code for interfaces between layers of the infrastructure, packing and unpacking code for communication and nesting, and field-by-field calls to routines for model I/O.

14 Moving Nests

15 PERFORMANCE

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17 Implementation of Wildland Fire Model Component in the WRF model

18 Functionalities of the Wildland Fire Model Component Extension of the Clark-Hall atmospheric model. Atmosphere-wildland fire simulation model has been developed to represent a complex interactions between fires and local winds. Helps to track atmospheric wind velocities and calculate fire spread more precisely. Handles large releases of buoyancy and accurately represent finescale motions in complex terrain. Ingest large-scale gridded data to incorporate a changing mesoscale atmospheric environment. Telescope down to the meter-sized fine dynamic scales of vortices in the fire line through horizontal and vertical grid refinement.

19 Benefits The availability of a coupled atmosphere-fire model in a wellsupported community model for eventual community research. Can build a stable framework in other scientific components related to the Wildland Fire Research and Development Collaboratory. Provide eventual operational capability with operational applications like smoke management A test of WRF with strong forcing at small scales. Reduction in the redundancy of effort developing needed capabilities within the Clark-Hall model.

20 Numerical model Background - 3-D nonhydrostatic atmospheric prediction model coupled with an empirical fire spread model with sensible and latent heat flux from the fire feed back to the atmosphere to produce fire winds. - The atmospheric winds drive the fire propagation. Wildfire simulation model represents the complex interactions between a fire and local wind.

21 BURNUP algorithm Characterizes how the fire consumes fuels of different sizes over time. Equation : 1-F = exp(-t/w)

22 Implementation of Fire Module in WRF Fire module will be implemented as an added physics option in WRF. Fire-atmosphere coupling will occur through passing winds from the lowest WRF level to the fire module. The fire module will use those winds to predict the fire spread and subsequent heat and water vapor emissions. Heat and water vapor emissions from the fire will be passed back to WRF and distributed vertically through an assumed extinction depth.

23 Initialization of Fire Environment Three environmental factors that influence fire behavior are : 1. Topography 2. Weather 3. Fuel

24 Topography Fires spread much faster in upslope than flat ground. Fine-scale topography features as a factor in local airflows plays a role in fire behavior.

25 Weather Weather impacting the fire. The weather impacts on the fire can be obtained by the winds model in WRF. Winds model could be used to simulate changes in dead fuel moisture which responds with time lags corresponding to the size of the fuel particles. - A second feedback loop between the fire and the environment.

26 Fuel Not all vegetation is burnable. Live vegetation may be dried and ignited by fire (live fuel). Forest floor needles, cured grasses and branches (dead fuel) play more important role in fire behavior.

27 Links with other modules WRF-Chem is an atmospheric chemistry package linked to WRF for simulation of atmospheric chemistry and aerosols. The combined application of WRF-Chem and WRF-Fire will allow the user to create a simulation of Wildland fire.

28 Numerical Simulation of Wildfires

29 References URLs Research Papers The Weather Research and Forecast Model: Software Architecture and Performance by J Michalakes, J.Dudhia, D.Gill, T.Henderson, J.Klemp, W.Skamarock, W.Wang Implementation of Wildland Fire Model Component in the Weather Research and Forecasting (WRF) model by Janice Coen (MMM/RAP) and Ned Patton (MMM) WRF-Fire: A Coupled Atmosphere-Fire Module for WRF by Ediward G.Patton and Janice L.Coen. National Center for Atmospheric Research, Boulder, CO

30 Wildfire Simulation Software Charles Erwin

31 Simple Wildfire Simulator from NOVA (requires Flash) Wildfire Simulator is a simple computer simulation that predicts the behavior of fire in a wildland environment. Not meant for research, only to demonstrate some basic ideas about wildfire simulation. Programming for this feature is derived from FARSITE. CS 521: Computational Science 2

32 EMBYR: Ecological Model for Burning the Yellowstone Region Created by William W. Hargrove and Robert H. Gardner Designed to simulate wildfires, the subsequent pattern of vegetation, and then the next generation of burn patterns. While the EMBYR model parameters could be adjusted to reproduce a particular historical wildfire exactly, it is more important to reproduce any wildfire relatively well on average. EMBYR can generate "Risk Maps", which are constructed from many replications of a single simulated fire. Cells which burned in many of the replications are colored black, while cells which burned in only a few simulations are colored white, with gray levels in intermediate cases. CS 521: Computational Science 3

33 EMBYR Fire Model The fire model, EMBYR, depicts the landscape as a grid in which the dimension of each cell is 50 m (2500 m2). Diffusive Spread: Fire spreads from each ignited cell to any of eight unburned neighbors (the four adjacent cells and four diagonal cells) as an independent stochastic event with probability I, where I may range from 0 to 1. Each cell burns for a single time step of variable length, and the fire goes out if new sites are not ignited at each time step. Theoretical studies have demonstrated that if I is less then a critical value, i fires are unlikely to propagate across the c landscape CS 521: Computational Science 4

34 EMBYR Fire Model (cont) They estimated by performing 50 simulations for each value of I (0.245! I! in increments of 0.001) on a 300x300 grid. The proportion of simulations with fires reaching the top edge of the map after the entire bottom edge was ignited was 38% for I = and 60% for I = Since i c is the threshold at which 50% of the fires reach the opposite edge of the map, these results indicated that i c i c lies between and CS 521: Computational Science 5

35 EMBYR Fire Model (cont) Simulating multiple fuel classes: EMBYR explicitly simulates multiple classes of fuel by varying the probability of fire spread as a function of fuel type. The fuel classes considered are four successional stages of lodgepole pine forest, nonforested regions such as meadows, and nonflammable areas such as rock, roads, and water. Derived probabilities on fire spreading between different types of fuel. CS 521: Computational Science 6

36 EMBYR Fire Model (cont) Variation in fuel moisture: EMBYR uses a standard fire danger measure known as percent 1000-h time-lagged fuel moisture. In this measure, an assumption is made about how long fuel of a particular diameter would take to soak to the core, or to dry out once soaked. Current internal moisture in fuels of that diameter is modeled with appropriately time-lagged ambient atmospheric humidity. Obviously, if fuels are sufficiently wet, fires do not occur. CS 521: Computational Science 7

37 EMBYR Fire Model (cont) Simulating the effects of wind: Three classes of wind speeds (WS), measured at a standard height of 6.1 m (20 ft) above the surface, are considered: WS 0, with speeds ranging from 0 to 3.1 kph (5 mph) WS 1, moderate winds ranging from 3.1 to 21.7 kph (5 35 mph) WS 2, strong wind with speeds greater than 21.7 kph For each of the three wind speed classes, a bias value b is used to modify the probability of spread to each neighboring cell. CS 521: Computational Science 8

38 EMBYR Fire Model (cont) CS 521: Computational Science 9

39 EMBYR Fire Model (cont) Simulating the effects of firebrands: EMBYR simulates a second mechanism of fire spread the production of firebrands which are carried aloft in the rising convection column, and then drift and fall on remote sites. The spotting effect of firebrands is simulated by permitting each burning site to generate a fixed number of firebrands as a function of fuel type. CS 521: Computational Science 10

40 Simulation: homogeneous landscapes Area burned (in cells) in a 500x500 cell homogeneous fuel class landscape with a single fixed ignition as a function of the probability of fire spread, I, to the eight surrounding neighbors where (a) fire is allowed to propagate by adjacent spread only (no firebrands), and (b) fire is allowed to propagate by adjacent spread and by firebrands. The simulation was ended before fire could reach the edge of the map. Means and standard deviations are shown for five replications. CS 521: Computational Science 11

41 Simulation: Using actual Landscapes CS 521: Computational Science 12

42 Simulation: Using actual Landscapes The cumulative frequency of risk of fires of increasing size for four alternative weather conditions of (from left to right) (a) Scenario 1: moist with strong winds; (b) Scenario 2: dry weather with moderate winds; (c) Scenario 3: very dry weather with moderate winds; and (d) Scenario 4: very dry weather with strong winds CS 521: Computational Science 13

43 Examples of EMBYR In action CS 521: Computational Science 14

44 FARSITE: Fire Area Simulator Two Dimensional model of fire behaviour and growth simulator. A simple ellipse fit observed fire growth data as well as other shapes. Regardless of the correct shape (if a single one exists), the eccentricity of the fire is known to increase with increasing windspeed or slope steepness or both. Cellular Model: Simulate fire growth as a discrete process of ignitions across a regularly spaced landscape grid. In general, cellular models have had diminishing success in reproducing the expected twodimensional shapes and growth patterns as environmental conditions become more heterogeneous. CS 521: Computational Science 15

45 FARSITE Model Problems with cellular models are avoided by the vector or wave approach to fire growth modeling (Huygens principle). The fire front is propagated as a continuously expanding fire polygon at specified timesteps. Essentially the inverse of the cellular method, the fire polygon is defined by a series of two-dimensional vertices (points with X,Y coordinates). The number of vertices increases as the fire grows over time (polygon expands). The expansion of the fire polygon is determined by computing the spread rate and direction from each vertex and multiplying by the duration of the timestep. CS 521: Computational Science 16

46 FARSITE Model (cont) The reliance on an assumed fire shape, in this case an ellipse, is necessary because the spread rate of only the heading portion of a fire is predicted by the present fire spread model. Fire spread in all other directions is inferred from the forward spread rate using the mathematical properties of the ellipse. There are still many problems in accurately simulating fire with this approach, different methods, however, will probably be of little consequence to the practical application of a fire growth model until the greater uncertainties are resolved as to how wind, slope, and fuels affect fire shapes. CS 521: Computational Science 17

47 FARSITE Model (cont) CS 521: Computational Science 18

48 FARSITE Model: Richards Equations Xs, Ys! a, b, c The orientation of the vertex on the fire front in terms of component differentials. The direction of maximum fire spread rate. The shape of an elliptical fire determined from the conditions local to that vertex in terms of dimensions. CS 521: Computational Science 19

49 FARSITE Model (cont) CS 521: Computational Science 20

50 FARSITE: Transformations for Sloping Terrain Richards equations were originally developed for flat terrain. On flat terrain, a horizontal coordinate system remains unchanged when projected onto the ground surface. This is not the case for sloping terrain. This means that the inputs to equations [1] and [2] must be transformed from the horizontal to the surface plane, and outputs must be transformed from the surface plane back to the horizontal plane. CS 521: Computational Science 21

51 FARSITE Model (cont) CS 521: Computational Science 22

52 FARSITE Model (cont) CS 521: Computational Science 23

53 FARSITE Model (cont) CS 521: Computational Science 24

54 FARSITE Model (cont) Other models used include the Van Wagner crown fire model, and Albini s spotting model. For input, FARSITE uses GIS raster data in lieu of vector data. For fuel moisture, BEHAVE and NFDRS equations are used. CS 521: Computational Science 25

55 FARSITE Raster Landscape input layers required from the GIS for FARSITE simulation. CS 521: Computational Science 26

56 FARSITE Animation of a FARSITE v4.0x simulation in a 3D window. CS 521: Computational Science 27

57 FARSITE Screen shot of a FARSITE v4.00 simulation utilizing the postfrontal combustion model. CS 521: Computational Science 28

58 References Finney, Mark A FARSITE: Fire Area Simulator-model development and evaluation. Res. Pap. RMRS-RP-4, Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station Hargrove, W.W., R.H. Gardner, M.G. Turner, W.H. Romme, and D.G. Despain Simulating fire patterns in heterogeneous landscapes. Ecological Modelling 135(2-3): CS 521: Computational Science 29