the sensitivity of fire-behavior and smoke-dispersion indices to the
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The sensitivity of fire-behavior and smoke-dispersion indices to the
diagnosed mixed-layer depth
Joseph J. Charney U.S. Forest Service, Northern Research Station, Lansing, MI
and
Daniel KeyserDepartment of Atmospheric and Environmental Sciences, University at Albany,
State University of New York, Albany, NY
1. Background
2. Objective
3. Double Trouble State Park (DTSP) Wildfire Event
4. WRF Model Configuration
5. Indices and Diagnostics
6. Results
7. Conclusions
Organization
Background
The goal of this project is to diagnose the spatial and temporal variability of meteorological quantities in the planetary boundary layer that can affect fire behavior and smoke dispersion.
Meteorologists and fire and smoke managers are currently debating the manner in which the mixed-layer depth is, and should be, diagnosed.
Background
While fire-behavior and smoke-dispersion indices are sensitive to diagnosed mixed-layer depth (MLD), the potential for sensitivities in the indices to affect fire- and smoke-management decisions is not well-understood.
A quantitative assessment of these sensitivities can help enable fire and smoke managers to anticipate whether the implementation of a given MLD diagnostic could affect their ability to fulfill burn program requirements.
We will assess the sensitivity of a fire-behavior index and a smoke-dispersion index to three MLD diagnostics using mesoscale model simulations of the 2 June 2002 DTSP wildfire event.
Indices: • fire-behavior index: downdraft convective available potential energy (DCAPE)
• smoke-dispersion index: Ventilation Index (VI)
MLD diagnostics:• surface-based buoyancy• potential temperature(z) = potential temperature(sfc)• potential temperature(z) = potential temperature(z/2)
z = height above ground level
Objective
DTSP Wildfire Event• Occurred on 2 June 2002 in east-central NJ• Abandoned campfire grew into major wildfire by 1800 UTC• Burned 1,300 acres• Forced closure of the Garden State Parkway• Damaged or destroyed 36 homes and outbuildings• Directly threatened over 200 homes• Forced evacuation of 500 homes• Caused ~$400,000 in property damage
References: Charney, J. J., and D. Keyser, 2010: Mesoscale model simulation of the meteorological conditions during the 2 June 2002 Double Trouble State Park wildfire. Int. J. Wildland Fire, 19, 427–448. Kaplan, M. L., C. Huang, Y. L. Lin, and J. J. Charney, 2008: The development of extremely dry surface air due to vertical exchanges under the exit region of a jet streak. Meteor. Atmos. Phys., 102, 63–85.
"Based on the available observational evidence, we hypothesize that the documented surface drying and wind variability result from the downward transport of dry, high-momentum air from the middle troposphere occurring in conjunction with a deepening mixed layer." "The simulation lends additional evidence to support a linkage between the surface-based relative humidity minimum and a reservoir of dry air aloft, and the hypothesis that dry, high-momentum air aloft is transported to the surface as the mixed layer deepens during the late morning and early afternoon of 2 June." (Charney and Keyser 2010)
DTSP Wildfire Event
• WRF version 3.4
• 4 km nested grid
• 51 sigma levels, with 21 levels in the lowest 2000 m
• NARR data for initial and boundary conditions
• Noah land-surface model
• RRTM radiation scheme
• YSU PBL scheme
WRF Model Configuration
DCAPEIndices and Diagnostics
For the starting level:
• Potter (2005) proposes 3000 m• We choose the top of the MLD
DCAPE is the maximum kinetic energy that can be realized by the parcel, which is proportional to the area indicated in brown on the diagram (Emanuel 1994, pp. 172–173).
DCAPE calculation:• Choose a starting level for the parcel• Saturate the parcel• Bring the parcel to the surface while maintaining saturation
DCAPE
DCAPE was originally formulated to estimate the maximum strength of evaporatively cooled downdrafts beneath a convective cloud (Emanuel 1994, p. 172-173). It has been suggested that DCAPE could be applied to wildland fires (Potter 2005).
We hypothesize that in the case of a mixed layer produced by dry convection, large DCAPE may correlate well with low surface relative humidity when the mixed-layer is deep and the top of the mixed layer is dry.
Indices and Diagnostics
Ventilation Index (VI)
Definition: the MLD multiplied by the “transport wind speed”
The transport wind speed can be interpreted in several different ways:
• mixed-layer average wind speed• surface wind speed (usually 10 m) • 40 m wind speed
For the purposes of this study, the mixed-layer averaged wind speed will be used.
Indices and Diagnostics
From Hardy et al. (2001)
Ventilation Index (VI) Indices and Diagnostics
MLD Diagnostics
1) MLD1 is diagnosed by determining the height to which near-surface eddies can rise freely.
The parcel exchange potential energy (PEPE) as proposed by Potter (2002) is employed.
The lowest height at which PEPE is zero is identified as the top of the surface-based mixed layer.
Indices and Diagnostics
MLD Diagnostics
LeMone and coauthors, in their presentation at the 12th Annual WRF Users’ Workshop (20–24 June 2011, National Center for Atmospheric Research, Boulder, CO), proposed a number of mixed-layer diagnostics for use with mesoscale model output.
Indices and Diagnostics
MLD Diagnostics
2) MLD2 is diagnosed by finding the highest level above the ground where the potential temperature equals the surface potential temperature.
Indices and Diagnostics
height
potential temperature
Ɵ
mixed-layer height
MLD Diagnostics
3) MLD3 is diagnosed by finding the highest level above the ground where the potential temperature equals the potential temperature at one half that height above the ground.
Indices and Diagnostics
height
potential temperature
mixed-layer height
zz/2
Time series of MLD1, MLD2, and MLD3 (m)
Results
Results
Skew T diagram showing the pressure at the top of the mixed layer for MLD1, MLD2, and MLD3 at 1300 UTC
Skew T diagram showing the pressure at the top of the mixed layer for MLD1, MLD2, and MLD3 at 1700 UTC
Results
Results
Skew T diagram showing the pressure at the top of the mixed layer for MLD1, MLD2, and MLD3 at 2100 UTC
Time series of DCAPE (J/kg) using MLD1, MLD2, and MLD3
Results
Time series of VI (m2/s) using MLD1, MLD2, and MLD3
Results
Variable Correlation
MLD1 0.815
surface relative humidity (RH) −0.936surface dewpoint depression (TDD) 0.914
mixed-layer average RH −0.511mixed-layer average TDD 0.509
Correlations of DCAPE using MLD1 with MLD1 and with surface and mixed-layer average moisture variables
from 1200 UTC to 2100 UTC 2 June 2002
Results
Variable Correlation
MLD2 0.808
surface relative humidity (RH) −0.945surface dewpoint depression (TDD) 0.927
mixed-layer average RH −0.540mixed-layer average TDD 0.539
Correlations of DCAPE using MLD2 with MLD2 and with surface and mixed-layer average moisture variables
from 1200 UTC to 2100 UTC 2 June 2002
Results
Variable Correlation
MLD3 0.797
surface relative humidity (RH) −0.911surface dewpoint depression (TDD) 0.887
mixed-layer average RH −0.583mixed-layer average TDD 0.576
Correlations of DCAPE using MLD3 with MLD3 and with surface and mixed-layer average moisture variables
from 1200 UTC to 2100 UTC 2 June 2002
Results
Time–height cross section of RH with time series of MLD1, DCAPE using MLD1, and surface RH
Results
MLD1
Time–height cross section of RH with time series of MLD2, DCAPE using MLD2, and surface RH
Results
MLD2
Time–height cross section of RH with time series of MLD3, DCAPE using MLD3, and surface RH
Results
MLD3
Time–height cross section of TDD with time series of MLD1, DCAPE using MLD1, and surface TDD
Results
MLD1
Time–height cross section of TDD with time series of MLD2, DCAPE using MLD2, and surface TDD
Results
MLD2
Time–height cross section of TDD with time series of MLD3, DCAPE using MLD3, and surface TDD
Results
MLD3
Time–height cross section of RH with time series of MLD1, DCAPE using MLD1, and mixed-layer average RH using MLD1
Results
MLD1
Time–height cross section of RH with time series of MLD2, DCAPE using MLD2, and mixed-layer average RH using MLD2
Results
MLD2
Time–height cross section of RH with time series of MLD3, DCAPE using MLD3, and mixed-layer average RH using MLD3
Results
MLD3
Time–height cross section of TDD with time series of MLD1, DCAPE using MLD1, and mixed-layer average TDD using MLD1
Results
MLD1
Time–height cross section of TDD with time series of MLD2, DCAPE using MLD2, and mixed-layer average TDD using MLD2
Results
MLD2
Time–height cross section of TDD with time series of MLD3, DCAPE using MLD3, and mixed-layer average TDD using MLD3
Results
MLD3
Conclusions
MLD diagnostics produce differences ~100-200 m in a simulation of the DTSP wildfire.
Differences in MLD diagnostics contribute to DCAPE values that differ by ~20-25%.
Differences in MLD diagnostics produce VI values that differ by 4000-6000 m2/s.
The diurnal variation in DCAPE is shown to correlate with MLD and with meteorological variables that diagnose low-level moisture.
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