Background Objectives Double Trouble State Park (DTSP) Wildfire Event WRF Model Configuration

1. The diagnosis of mixed-layer characteristics and their relationship to meteorological conditions above eastern U.S. wildland fires 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. Organization • Background • Objectives • Double Trouble State Park (DTSP) Wildfire Event • WRF Model Configuration • Ingredients and Indices • Results • Conclusions

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 mixed-layer characteristics that are important for understanding and predicting when dry air aloft might influence the evolution of a fire. Diagnostic methodologies are proposed that address variability in surface conditions over time scales of several hours that can be detected using mesoscale model simulations.

4. Objectives • Using mesoscale model simulations of the 2 June 2002 DTSP wildfire event, we will: • examine the Ventilation Index (VI) to assess whether it is sensitive to differences between the mixed-layer depth (MLD) and PBL depth (BLD) • determine whether Downdraft Convective Available Potential Energy (DCAPE) can diagnose the potential for low relative humidity to occur at the surface

5. 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.

6. DTSP Wildfire 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." "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)

7. WRF Model Configuration • WRF version 3.1 • 4 km nested grid • 51 sigma levels, with 21 levels in the lowest 2000 m • NARR data used for initial and boundary conditions • Noah land-surface model • RRTM radiation scheme • YSU and MYJ PBL schemes

8. Ingredients and Indices • Fire weather ingredients • wind speed • humidity (RH) • temperature • Meteorological variables • MLD: depth over which near-surface eddies rise freely • BLD: from the mesoscale model PBL parameterization • In a well-mixed boundary layer, the • MLD and the BLD would be expected • to be similar. (Potter 2002)

9. Ingredients and Indices Ventilation Index (VI) Definition: the MLD multiplied by the “transport wind speed” The VI can be calculated from mesoscale model data using either the MLD or the BLD. 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.

10. Results – VI Time series at the fire location of the components of the VI: • MLD • MLD-average wind speed • BLD • BLD-average wind speed Note: the fire started to exhibit rapid spread between 1700 and 1800 UTC.

11. Results – VI The YSU simulation usually produces MLDs and BLDs that are higher than those in the MYJ simulation.  YSU MLDs and BLDs track quite closely to each other, while MYJ MLDs and BLDs differ more from each other. 

12. Results – VI The YSU VIs are higher than the MYJ VIs wherever the MLDs/BLDs are higher. The dependence of the VI on average wind speed is weaker than on the MLD/BLD.

13. Ingredients and Indices DCAPE DCAPE was originally formulated to estimate the maximum potential strength of evaporatively cooled downdrafts beneath a convective cloud (Emanuel 1994). 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.

14. Ingredients and Indices DCAPE • DCAPE calculation: • choose a starting level for the parcel • saturate the parcel • bring the parcel to the surface while maintaining saturation • evaluate the negative buoyancy of the parcel as it passes the “level of free sink” and reaches the surface or the level of neutral buoyancy • The integrated energy of the negative buoyancy when the parcel reaches the surface is DCAPE. • For the starting level: • Potter (2005) proposes 3000 m • We choose the top of the MLD

15. Results – DCAPE Examine time series of simulated 3000 m and MLD DCAPE at the fire location using the YSU PBL scheme.

16. Results – DCAPE • The 3000 m DCAPE and the MLD DCAPE tend to vary together in that they both reach a maximum at 1700 UTC. • The MLD DCAPE varies more than the 3000 m DCAPE due to the variable starting level.

17. Results – DCAPE Examine an animation of horizontal plots of simulated MLD DCAPE using the YSU PBL scheme from 1300 UTC through 1800 UTC.

18. Results – DCAPE

19. Results – DCAPE

20. Results – DCAPE

21. Results – DCAPE

22. Results – DCAPE

23. Results – DCAPE

24. Results – DCAPE The DCAPE animation shows elevated values over south-central NJ and the Delmarva Peninsula.  (Charney and Keyser 2010)

25. Conclusions These results suggest that particular care should be taken when interpreting indices that depend upon the MLD and employ the BLD from the MYJ PBL scheme for this purpose. For the operational WRF (NAM), which employs the YSU PBL scheme, differences between the MLD and the BLD are less likely to be a problem when the MLD is well-mixed. While the 3000 m DCAPE and the MLD DCAPE exhibit similar temporal variability, the variable starting level in the MLD DCAPE gives it a stronger diurnal dependence. This sensitivity contributes to a higher peak in MLD DCAPE values where and when anomalously low surface relative humidity occurred during the DTSP wildfire event.

26. A Modest Proposal If the fire community comes to employ DCAPE as an index to diagnose processes that affect the evolution of a wildland fire, it might be beneficial to “rebrand” DCAPE due to its association with convective precipitation within the broader meteorological community.