International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Climatological and statistical characteristics of the Haines Index for North America

Julie A. Winkler A E , Brian E. Potter B , Dwight F. Wilhelm A , Ryan P. Shadbolt A , Krerk Piromsopa C and Xindi Bian D
+ Author Affiliations
- Author Affiliations

A Department of Geography, Michigan State University, East Lansing, Michigan, USA 48824.

B USDA Forest Service, 400 N. 34th Street, Suite 201, Seattle, Washington, USA 98103.

C Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA 48824.

D USDA Forest Service, 1407 South Harrison Road, Suite 220, East Lansing, Michigan, USA 48823.

E Corresponding author. Email: winkler@msu.edu

International Journal of Wildland Fire 16(2) 139-152 https://doi.org/10.1071/WF06086
Published: 30 April 2007

Abstract

The Haines Index is an operational tool for evaluating the potential contribution of dry, unstable air to the development of large or erratic plume-dominated wildfires. The index has three variants related to surface elevation, and is calculated from temperature and humidity measurements at atmospheric pressure levels. To effectively use the Haines Index, fire forecasters and managers must be aware of the climatological and statistical characteristics of the index for their location. However, a detailed, long-term, and spatially extensive analysis of the index does not currently exist. To meet this need, a 40-year (1961–2000) climatology of the Haines Index was developed for North America. The climatology is based on gridded (2.5° latitude × 2.5° longitude) temperature and humidity fields from the NCEP/NCAR reanalysis. The climatology illustrates the large spatial variability in the Haines Index both within and between regions using the different index variants. These spatial variations point to the limitations of the index and must be taken into account when using the Haines Index operationally.


Acknowledgements

This project was funded by Joint Fire Science Program agreement 03-1-1-37. In-kind support was provided by Michigan State University and the United States Forest Service North Central Research Station. We thank the anonymous reviewers and the editor for their helpful suggestions.


References


Brotak EA (1976) Meteorological Conditions Associated with Major Wildland Fires. PhD Dissertation, Yale University, New Haven, Connecticut.

Brotak EA (1992–1993) Low-level conditions preceding major wildfires. Fire Management Notes  52–53, 23–26.


Croft PJ, Watts M, Potter B, Reed A (2001) The analysis of the Haines Index climatology for the Eastern United States, Alaska, Hawaii and Puerto Rico. In ‘Fourth symposium on fire and forest meteorology’. Paper No. 8.7. (American Meteorological Society: Boston, MA)

Elliott WP , Gaffen DJ (1991) On the utility of radiosonde humidity archives for climate studies. Bulletin of the American Meteorological Society  72, 1507–1520.
CrossRef |

Elliott WP, Ross RJ , Schwartz B (1998) Effects on climate records of changes in National Weather Service humidity processing procedures. Journal of Climate  11, 2424–2436.
CrossRef |

Gaffen DJ, Sargent MA, Habermann RE , Lanzante JR (2000) Sensitivity of tropospheric and stratospheric temperature trends to radiosonde data quality. Journal of Climate  13, 1776–1796.
CrossRef |

George JJ (1960) ‘Weather forecasting for aeronautics.’ (Academic Press: New York)

Haines DA (1988) A lower atmosphere severity index for wildland fires. National Weather Digest  13, 23–27.


Jenkins MA (2002) An examination of the sensitivity of numerically simulated wildfires to low-level atmospheric stability and moisture, and the consequences for the Haines Index. International Journal of Wildland Fire  11, 213–232.
CrossRef |

Jenkins MA (2004) Investigating the Haines Index using parcel model theory. International Journal of Wildland Fire  13, 297–309.
CrossRef |

Jones KM, Maxwell C (1998) A seasonal Haines Index climatology for New Mexico and the significance of its diurnal variations in the elevated Southwest. In ‘Preprints of the second symposium on fire and forest meteorology’. pp. 127–130. (American Meteorological Society: Boston, MA)

Kalnay E, Kanamitsu M, Kistler R, Collins W , Deaven D (1996) The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society  77, 437–471.
CrossRef |

Klein WH (1957) Principle tracks and mean frequencies of cyclones and anticyclones in the Northern Hemisphere. Weather Bureau Research Paper No. 40. US Department of Commerce, NOAA, Washington, DC.

Kochtubajda B, Flannigan MD, Gyakum JR, Stewart RE (2001) The influence of atmospheric stability on fire behavior in the Northwest Territories, Canada. In ‘Preprints of the Fourth symposium on fire and forest meteorology’. (American Meteorological Society: Boston, MA)

Miller RC (1972) Notes on analysis and severe storm forecasting procedures of the Air Force Global Weather Central. Headquarters, Air Weather Service, United States Air Force Technical Report 200(R). (Omaha, NE)

Potter BE (2001) How and why does the Haines Index work? Energy and dynamics considerations. In ‘Preprints of the Fourth symposium on fire and forest meteorology’. Paper No. 8.8. (American Meteorological Society: Boston, MA)

Potter BE, Borsum D , Haines D (2002) Keeping Haines real – or really changing Haines? Fire Management Today  62, 41–46.


Potter BEWinkler JAWilhelm DFShadbolt RP (2005) Computation of the low elevation Haines Index. In ‘Sixth fire and forest meteorology symposium/19th interior west fire council meeting’. Paper No. P1.3. (American Meteorological Society: Boston, MA)

Potter BE, Winkler JA, Wilhelm DF , Shadbolt RP (2007) Computing the low elevation Haines Index. Fire Management Today, ,


Werth P , Ochoa R (1993) The evaluation of Idaho wildfire growth using the Haines Index. Weather and Forecasting  8, 223–234.
CrossRef |

Werth J , Werth P (1998) Haines Index climatology for the western United States. Fire Management Notes  58, 8–17.


Winkler JA (2004) The impact of technology on in situ atmospheric observations and climate science. In ‘Geography and technology’. (Eds S Brunn, S Cutter, JW Harrington) pp. 461–490. (Kluwer Academic Publishers: New York)




Appendix 1. Calculating the Haines Index

The Haines Index was first proposed in 1988 as an indicator of dry, unstable air. The concept behind the index formulation, based on earlier work by Brotak (1976), is that dry, unstable air increases the likelihood of large and/or erratic wildfires. The index includes a stability (A) component and a humidity (B) component. The A component is the environmental temperature difference between two fixed pressure surfaces, and measures the potential buoyancy of air parcels or, in other words, the potential for atmospheric mixing. The B component is the dewpoint depression at a fixed pressure level.

The major consideration in the selection of the pressure levels used to compute the index is that they be high enough in the atmosphere to not be overly influenced by diurnal variations of surface temperature or by surface inversions. Haines divided the United States into three regions based on surface elevation and developed separate variants of the index, referred to as the ‘low’, ‘mid’ and ‘high’ Haines Index, each calculated from a different pair of fixed pressure levels (Table 1). Haines did not use specific elevations as the boundaries between the three regions but rather placed USA climatological divisions into the three regions based on the general elevation above sea level. As a result, the elevation of the boundaries between the low and mid regions and between the mid and high regions is not constant and varies unsystematically. An important consideration when using and interpreting the Haines Index is that the index regions, particularly the high region, encompass complex physiography and a range of surface elevations. For example, coastal California with elevations near sea level falls within the high variant region.

For the mid and high variants, the index uses temperature and humidity measurements from mandatory pressure levels (the 850 and 700-hPa levels for the mid variant and the 700 and 500-hPa levels for the high variant), required by the World Meteorological Organization in all upper-air observations. On the other hand, the low variant uses observations from a mandatory level (850 hPa) and a non-mandatory pressure level (950 hPa). When the index was originally formulated, measurements at 950 hPa, although not mandatory, were often reported. However, in the 1990s a new mandatory level at the 925 hPa level was established, and since then observations at the 950 hPa level are uncommon. No standardised adjustment to the calculation of the low variant of the Haines Index has been made to account for this change in data availability. However, Potter et al. (2005, 2007) showed, for 18 rawinsonde locations in the low Haines region, that estimating the 950-hPa temperature using a log–pressure interpolation of the values for the surrounding 1000 and 925-hPa mandatory levels replicates the original Haines Index value on over 90% of all observations times. The one exception is during autumn at locations that experience frequent inversions. In contrast, directly substituting the 925-hPa temperature for that of 950 hPa, which is a method for calculating the low variant index that is frequently used operationally, underestimates the original Haines Index value between 20 and 70% of the time, depending on location.

To ensure that the stability and humidity components are weighted equally in the index, the temperature and humidity differences are converted into ordinal values (1, 2 or 3). To select threshold values demarcating the ordinal values for the A and B categories, Haines subjectively compared lapse rates and dewpoint depressions at one to three radiosonde stations closest in location to 74 wildland fires that occurred over a 20-year period. The ordinal values for the A and B components are then summed, and the resulting Haines Index ranges from 2 (very low potential of large or erratic plume-dominated behaviour) to 6 (very high potential).



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