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The diagnosis of mixed-layer depth above an eastern U.S. wildfire using a mesoscale numerical weather prediction model Joseph J. Charney USDA Forest Service,

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Presentation on theme: "The diagnosis of mixed-layer depth above an eastern U.S. wildfire using a mesoscale numerical weather prediction model Joseph J. Charney USDA Forest Service,"— Presentation transcript:

1 The diagnosis of mixed-layer depth above an eastern U.S. wildfire using a mesoscale numerical weather prediction model Joseph J. Charney USDA Forest Service, Northern Research Station, East Lansing, MI and Daniel Keyser Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY

2 1.Background 2.Methods 3.Double Trouble State Park (DTSP) Wildfire Event 4.WRF Model Configuration 5.Results 6.Conclusions Organization

3 Background The overall project goals address identifying dry air in the lower troposphere and processes that could transport this dry air to the surface. This presentation focuses on the vertical distribution of wind, temperature, and relative humidity in a well-mixed planetary boundary layer (PBL) above a wildland fire.

4 "The source of this material is the COMET® Website at http://meted.ucar.edu/ of the University Corporation for Atmospheric Research (UCAR)

5 Background The depth over which momentum, heat, and moisture can be mixed vertically within the PBL is an important parameter for understanding and predicting when the atmosphere can influence the evolution and behavior of a wildland fire. Since above-ground measurements of wind, temperature, and relative humidity are not commonly available in close proximity to most wildland fires, the ability to simulate the evolution of the well-mixed PBL (i.e., the mixed layer) in mesoscale numerical weather prediction (NWP) models is an important element of fire-weather forecasting and research.

6 Coupled fire-atmosphere simulations

7 NWS fire weather forecasts

8 IMET forecasts

9 Background The evolution of the mixed layer within a mesoscale NWP model can depend strongly upon the choices of model parameterization and grid configuration, and the effects of these choices on the evolution of the mixed layer during wildland fire events are not well understood.

10 The meteorological conditions associated with the Double Trouble State Park (DTSP) wildfire (New Jersey, 2 June 2002) are investigated by using the WRF (Weather Research and Forecasting) model to simulate the event. "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." (Charney and Keyser 2010) Methods

11 A parameterization-independent formulation that diagnoses the depth of the mixed layer is applied to WRF simulations of the DTSP wildfire event. The mixed-layer depth is compared with the PBL depths predicted by two PBL parameterizations available within the WRF model. The sensitivity of the Ventilation Index and the Haines Index to differences between the diagnosed mixed-layer depth and the model-predicted PBL depth is also examined. Methods

12 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, 3–85.

13 WRF version 3.4 36, 12, and 4 km two-way nested grids 51 sigma levels, with 20 levels in the lowest 2000 m NARR data used for initial and boundary conditions Noah land-surface model RRTM radiation scheme WRF Model Configuration

14 Two PBL parameterizations available within the WRF model are employed: Yonsei University (YSU) PBL Mellor–Yamada–Janjic (MYJ) PBL PBL height is an output variable from the WRF model. YSU: the lowest height where the Bulk Richardson number is zero MYJ: the lowest height where turbulent kinetic energy approaches a low threshold value WRF Model Configuration

15 The mixed-layer depth is diagnosed by determining the height to which near-surface eddies can rise freely. A PBL parameterization-independent formulation is used to calculate the parcel exchange potential energy (PEPE) as proposed by Potter (2002). The lowest height at which PEPE is zero is identified as the top of the surface-based mixed layer. WRF Model Configuration

16 1200 UTC 2 June 2002 Results MYJ YSU blh = PBL height mlh = height of the top of the mixed layer TemperatureDew point

17 Results 1500 UTC 2 June 2002 MYJ YSU blh = PBL height mlh = height of the top of the mixed layer TemperatureDew point

18 Results 1800 UTC 2 June 2002 MYJ YSU blh = PBL height mlh = height of the top of the mixed layer TemperatureDew point

19 Results 2100 UTC 2 June 2002 MYJ YSU blh = PBL height mlh = height of the top of the mixed layer TemperatureDew point

20 Results Time (UTC) Height (m) DTSP wildfire MLH and BLH

21 MYJYSU Results 1800 UTC 2 June 2002 MLD-BLD (m)

22 Results MYJYSU1800 UTC 2 June 2002 Ventilation Index differences (m 2 /s 2 )

23 Results MYJYSU1800 UTC 2 June 2002 Haines Index

24 Conclusions Simulations of the DTSP wildfire event suggest that differences can develop between the PBL depth and the mixed layer depth depending upon the choice of PBL parameterization. Differences between MLD and BLD area affected by prevailing meteorological conditions and physiographic characteristics. The Ventilation Index differs substantially depending upon whether we use the mixed layer depth or PBL depth. The Haines Index does not exhibit significant differences for this event.

25 Conclusions It is important to expose and address the dependence of the simulated PBL on model configuration when applying NWP models to meteorological conditions associated with wildland fires.


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