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International Journal of Wildland Fire Scientific Journal of IAWF

Volume 11, 2002 © International Association of Wildland Fire 2002

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International Journal of Wildland Fire, 2002, 11, 183–191

WF020 3 JOetak.Dlh.CoamralsForineDangreModel

The Oklahoma Fire Danger Model: An operational tool for mesoscale fire danger rating in Oklahoma J. D. CarlsonA, Robert E. BurganB, David M. EngleC and Justin R. GreenfieldD

A

Biosystems and Agricultural Engineering, Oklahoma State University, 214 Ag Hall, Stillwater, OK 74078, USA. Corresponding author: Telephone: +1 405 744 6353; fax: +1 405 744 6059; email: [email protected] B USDA Forest Service, Fire Sciences Laboratory, Rocky Mountain Research Station, Missoula, MT 59807, USA. (retired) C Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078, USA. D Oklahoma Climatological Survey, University of Oklahoma, Norman, OK 73019, USA. This paper is derived from a presentation at the 4th Fire and Forest Meteorology Conference, Reno, NV, USA, held 13–15 November 2001

Abstract. This paper describes the Oklahoma Fire Danger Model, an operational fire danger rating system for the state of Oklahoma (USA) developed through joint efforts of Oklahoma State University, the University of Oklahoma, and the Fire Sciences Laboratory of the USDA Forest Service in Missoula, Montana. The model is an adaptation of the National Fire Danger Rating System (NFDRS) to Oklahoma, but more importantly, represents the first time anywhere that NFDRS has been implemented operationally using hourly weather data from a spatially dense automated weather station network (the Oklahoma Mesonet). Weekly AVHRR satellite imagery is also utilized for live fuel moisture and fuel load calculations. The result is a near-real-time mesoscale fire danger rating system to 1-km resolution whose output is readily available on the World Wide Web (http://agweather.mesonet.ou.edu/models/fire). Examples of output from 25 February 1998 are presented. The Oklahoma Fire Danger Model, in conjunction with other fire-related operational tools, has proven useful to the wildland fire management community in Oklahoma, for both wildfire anticipation and suppression and for prescribed fire activities. Instead of once-per-day NFDRS information at two to three sites, the fire manager now has statewide fire danger information available at 1-km resolution at up to hourly intervals, enabling a quicker response to changing fire weather conditions across the entire state. Additional keywords: fire danger; model; automated weather station networks; Oklahoma Mesonet; remote sensing; fuels; fuel moisture; National Fire Danger Rating System.

Introduction With more than half of its 17.5 million ha consisting of wildlands, the importance of fire in Oklahoma (USA), both natural and prescribed, is apparent. During a typical year about 450 000 ha of Oklahoma land is burned: 364 000 ha by prescribed fire and another 86 000 ha by wildfire. During the severe 1995–1996 fire season, over 263 000 ha of Oklahoma land was burned by wildfire alone. With a view toward user-friendly dissemination of weather-based tools for wildland fire management in Oklahoma, Oklahoma State University (OSU) and the University of Oklahoma (OU) have been cooperating over the past 6 years in developing various fire-related operational tools for dissemination over the World Wide © IAWF 2002

Web. In place of hourly synoptic-scale weather information, 15-min mesoscale weather information is now available from over 110 Oklahoma sites and, in place of once-per-day fire danger information at two to three sites, statewide colorized fire danger maps are available at 1-km resolution at up to hourly intervals. The most important factor behind the development of these weather-based, fire-related management tools has been the Oklahoma Mesonet, the state’s automated weather monitoring station network which became operational in 1994 (Brock et al. 1995). A joint project of OSU and OU, the network currently consists of 115 stations having an average spacing of 30 km. Weather and soil observations are transmitted every 15 min, with the data being available on the Web typically within 30 min after being reported. 10.1071/WF02003

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Another major factor has been the cooperation between OSU, OU, and the Fire Sciences Laboratory of the USDA Forest Service in Missoula, Montana. This collaboration, which began in late 1993, resulted in the development of the Oklahoma Fire Danger Model, which was implemented in its original form on the World Wide Web in March 1996. This particular timing was motivated by an extreme fire season in Oklahoma which began in the fall of 1995 and lasted through the spring of 1996; in particular, a 2-day statewide wildfire emergency during 22–23 February 1996 provided even more impetus for making the model accessible. The Oklahoma Fire Danger Model The Oklahoma Fire Danger Model (OKFD) is an adaptation of the USDA Forest Service’s National Fire Danger Rating System (Deeming et al. 1977; Bradshaw et al. 1983; Burgan 1988) to Oklahoma but implemented, for the first time anywhere, to 1-km resolution using hourly weather data from a spatially dense automated weather station network (Carlson and Engle 1998). Weekly satellite imagery for live fuel moisture and fuel load calculations are also utilized, which also represents a departure from past applications of NFDRS. Currently the OKFD model is run 11 times each day (0000, 0500, 0700, 0900, 1100, 1300, 1400, 1500, 1600, 1700, 1900 local standard time), with greater frequency (hourly) during the afternoon times of peak fire danger. Output is available at the following World Wide Web address generally within 1 h of the valid run time (http://agweather.mesonet.ou.edu/models/fire). Model output (examples to be shown later) include colorcoded maps to 1-km resolution of the following National Fire Danger Rating System (NFDRS) components: burning index, spread component, energy release component, and ignition component. In addition, interpolated maps of 1-h dead fuel moisture and the Keetch-Byram drought index (KBDI) are available, the latter map being updated once daily. A table of these and other fuel/weather variables at each automated weather monitoring station site used in the model is also provided. In addition to the model output, the Web site features weekly color-coded maps of visual and relative greenness (Burgan and Hartford 1993), examples of model output from wildfire episode days, and KBDI comparisons of each month in the current year to the corresponding month 1 year ago. OKFD model components To obtain the best assessment of fire danger at any scale requires information about the three components of the fire environment: weather, fuels, and topography. The first component of this ‘fire environment triangle’ is the most dynamic and the one used in all operational fire danger systems, whereas the third one is the least dynamic.

J. D. Carlson et al.

Weather Although not all weather monitoring stations are automated, automated weather monitoring stations are now the norm and their number continues to increase. Such stations provide the capability to assess fire danger more than once daily if appropriate revisions in the fire danger model being used can be made to take advantage of the increased weather data flow. An outstanding example of such an automated weather station network is the Oklahoma Mesonet (Carlson et al. 1994; Elliott et al. 1994), consisting currently of 115 stations and reporting data every 15 min. It is from this network that the Oklahoma Fire Danger Model gets its weather information. An example of a typical fire weather map that is available from the Oklahoma Mesonet is shown in Fig. 1. One can easily locate the position of a strong cold front on this afternoon, with strong south-west winds ahead (east) of the front and lighter north-west winds behind it. For our Oklahoma clientele, temperatures are shown in degrees Fahrenheit (oF), relative humidity in %, and wind speed in miles per hour (mph). Note that such a map is updated every 15 min, allowing for near-real-time monitoring of fire weather conditions. With respect to the OKFD Model itself, the weather variables utilized include 1.5-m air temperature and relative humidity, 10-m wind speed, solar radiation, and precipitation. Before the implementation of this model in 1996, NFDRS computations in Oklahoma were performed using once-a-day weather data from only two or three locations: two automated sites (one in south-west Oklahoma and one in eastern Oklahoma) and periodically from one manual site in north-east Oklahoma (McDowell, personal communication). These three site locations are superimposed upon Fig. 1 and depicted by solid triangles. The resulting map shows the obvious superiority of the Oklahoma Mesonet with respect to spatial density. Temporal superiority over once-a-day data is provided by using hourly data (although the fire weather maps themselves are updated every 15 min). In the present NFDRS application, hourly data are utilized from 115 stations of the Oklahoma Mesonet. Fuels Fuel models from the 1988 NFDRS are utilized to represent Oklahoma’s land surface. With the aid of 1-km resolution AVHRR satellite imagery (Loveland et al. 1991), a fuels map for Oklahoma was developed for us by the Fire Sciences Laboratory in Missoula in consultation with other fire scientists in Oklahoma. The result is that each square kilometer pixel in Oklahoma has been assigned one of five NFDRS fuel models: Model P for pine forests; Model R for deciduous forests; Model T for tallgrass prairie and eastern/central cropland; Model L for mixed prairie and western cropland; and Model A for shortgrass prairie. As

Oklahoma Fire Danger Model

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Fig. 1. A sample fire weather map from the 110+ station Oklahoma Mesonet at 1 p.m. CST (1300 local time) on 17 January 2000. Note the existence of a strong cold front lying south-west to north-east across the state. The three NFDRS locations used exclusively before 1996 are also shown (solid triangles).

will be explained later, these fuel models are not static in the OKFD model but, rather, dynamic, with some of the dead and live fuel loads being functions of satellite-derived information. Finally, these fuel models pertain only to surface fuels; consequently, the OKFD model, like the NFDRS, applies only to surface-based fires and not to crown fires. Dead fuel moisture (% dry weight basis) is calculated in a manner similar to that in the 1978 NFDRS model (Deeming et al. 1977; Bradshaw et al. 1983), but using hourly weather data instead of once-daily data. Because 10-h timelag fuel moisture sticks are not used, 1- and 10-h dead fuel moistures are calculated as 0.8 times the equilibrium moisture content (EMC) plus 0.2 times the 10- and 100-h dead fuel moistures, respectively, at the given time. EMC is a function of air temperature and relative humidity at the fuel–atmosphere interface. Fuel-level temperature (Tf) is modeled as a function of observed air temperature (T) and solar radiation (SRAD) at 1.5 m above the ground [TF = (SRAD/1353)*13.9 + T, where SRAD is in W m–2 and T is in oC]. Fuel-level relative humidity (RHf) is modeled as a function of observed 1.5 m relative humidity (RH) and solar radiation (SRAD) [RHF = (1–0.25*(SRAD/1353))*RH]. One-hundred hour and 1000-h dead fuel moistures are calculated using the same formulae as in the 1978 NFDRS. The 100-h value is a

function of the average EMC and precipitation duration over the past 24 h as well as yesterday’s 100-h value; the 1000-h value is based on the average EMC and precipitation duration over the past 7 days as well as the 1000-h value 7 days ago. Whereas 1-h and 10-h dead fuel moistures are updated hourly, the 100-h and 1000-h dead fuel moistures are updated once daily (at 1600 local time), although hourly data are used in calculating the input variables needed (average EMC and precipitation duration). The Keetch-Byram Drought Index (Keetch and Byram 1968) was added to the 1988 NFDRS as a means of increasing the dead fuel load under drought conditions (Burgan 1988). The KBDI system is based on an assumed 8 inch (203 mm) water-holding capacity of the soil and ranges from 0 to 800, where zero represents complete saturation and 800 total drought. In the OKFD model, KBDI is calculated once daily (at 1600 local time) and is a function of the current air temperature at 1.5 m, past 24-h rainfall, the annual average precipitation, and yesterday’s KBDI value. The 1600 hour is chosen because the additional observations permit it, and it is typically a much better time of day to capture the warmest temperature than is the typical 1300 local time used in the NFDRS. Drying takes place only on days having temperatures 10oC or higher. As in the 1988 NFDRS, the OKFD model progressively increases dead fuel

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loads (from a ‘drought’ dead fuel load reservoir) as KBDI values in a given 1-km pixel rise above 100 (Burgan 1988). In addition to using an automated weather station network, the OKFD model also relies on weekly satellite imagery for estimation of live fuel moisture. This represents another aspect in which the OKFD model differs from former applications of NFRDS. Since the early 1990s, biweekly and (in recent years) weekly composites of AVHRR-derived NDVI (Normalized Difference Vegetation Index) to 1-km resolution (Holben 1986; Goward et al. 1990) have been used in the USA to assess the status of live vegetation through derived variables called ‘visual greenness’ and ‘relative greenness’ (Burgan and Hartford 1993; Burgan et al. 1996). Both variables can range from 0 to 100%, but the latter is based on the historical maximum and minimum NDVI values for a given 1-km pixel. These three variables have proven useful in monitoring ‘greenup’ and senescence as well as showing departures in a given week from what is ‘normal’. The OKFD model utilizes only relative greenness (RG) derived for each 1-km pixel from NDVI data obtained through weekly composites of AVHRR data and each pixel’s NDVI historical maximum and minimum values. The relative greenness values are then employed to estimate live fuel moisture (% dry weight basis) for both herbaceous and woody fuel components. Live herbaceous moisture (%) is calculated as 2*RG + 29 (if this yields a value less than 30%, the live fuel moisture is set to the 1-h dead fuel moisture value). Live woody moisture (%) is calculated as 1.5*RG + 50 + 10*CLIM, where CLIM is the NFDRS ‘climate class’ (1, 2, or 3 in Oklahoma). In the OKFD model the fuel loadings for both live and 1-h dead fuels are dynamic. The herbaceous fuel load varies in direct proportion to the weekly RG value [(RG/100)*(fuel model herb load)], while the part not considered live [(1–RG/100)*(fuel model herb load)] is added to the fuel model’s specified 1-h dead fuel load. The woody fuel load in pixels with deciduous understories (fuel models R and P) is also a function of the weekly RG value, with woody fuel load being set to [(RG/100)*(fuel model woody load)] and the remaining ‘non-live’ portion [(1–RG/100)*(fuel model woody load)] being transferred to the 1-h dead fuel load. Finally, it has already been noted that the dead fuel loads in all timelag categories are also functions of KBDI, with increasing proportional amounts of dead fuel added from the fuel model’s ‘drought’ load as KBDI values rise above 100. Topography With respect to topography, all slopes calculated in Oklahoma to 1-km resolution from a GIS system yielded values within NFDRS slope class 1 (i.e. slopes from 0 to 25%). Thus, each square kilometer pixel in the model has


slopes assumed to be in the 0–25% range. For locally greater slopes, the model cannot be relied upon for accurate output. OKFD model methodology As with any model involving near-real-time data, the problem of missing data comes into play. Missing station data in the hourly Mesonet data files are estimated by objective analysis using a Barnes interpolation scheme (Barnes 1964) with a cutoff radius of 130 km. Weights are exponentially damped by the square of the distance. This interpolation occurs before the model calculations are performed, ensuring observed or interpolated values at each of the Mesonet sites. The OKFD model consists of two major parts. The first part, using hourly station data since the last OKFD run, calculates the four timelag dead fuel moistures and KBDI values (as well as some other needed weather-related variables) at the 110+ Mesonet sites. These variables are then interpolated via the Barnes scheme to a 10-km square resolution rectangular grid covering Oklahoma. This particular resolution is chosen because it lies at the lower limit of recommended grid spacing for an average Mesonet station spacing of 30 km (Koch et al. 1983). Decreasing the grid size further would be computationally more intensive while gaining nothing in terms of accuracy. The second part of the OKFD model utilizes the 10-km gridded output in combination with already available 1-km pixel data to calculate the live fuel moistures and NFDRS fire danger indices on a 1-km pixel scale. Certain variables, such as the fuel model and relative greenness value, are already available on a pixel basis. Other variables for the pixel calculations (e.g. precipitation, wind speed, dead fuel moistures, KBDI) are obtained via bilinear interpolation to the pixel in question using the four nearest grid point values from the 10-km grid square. The objective analysis grid was chosen such that all pixels within Oklahoma fall within the external boundaries of the grid (i.e. every pixel has four surrounding grid points from which to interpolate). If the pixel contains a Mesonet site, the actual calculated data for that site from the first part of the model is utilized, however, rather than interpolated data. Even though no station data from other states are used, interpolation results near state boundaries are still within the range of acceptability, given the high spatial density (30 km) of the Oklahoma Mesonet. Examples of OKFD model output In this section we present examples of OKFD model output from 25 February 1998. Even though no unusual wildfire activity was reported on this day, the maps to be shown serve as excellent examples of the capability of the OKFD model in assessing fire danger in the mesoscale range. Figure 2 shows the Mesonet fire weather map from 2 p.m. Central Standard Time (1400 local time). Note the existence of a ‘dry


line’ (depicted by the superimposed dashed line), which is a discontinuity in relative humidity. Behind the dry line (to the west) much lower relative humidities exist (in the 10–40% range); ahead of the dry line (to the east), relative humidities in the 60–90% range are common. Also note the change in wind direction from south and south-east ahead of the dry line (bringing up moisture from the Gulf of Mexico) to south-westerly behind it (bringing in much drier air from western Texas and New Mexico). Wind speeds are also higher behind the dry line, with gusts in the 30–40 miles per hour range (13–18 m s–1). The three NFDRS locations used exclusively before 1996 are again shown for purposes of comparison (solid triangles). The corresponding 1-h dead fuel moisture map calculated by the OKFD model is shown in Fig. 3. Note the strong discontinuity in moisture values (reds to greens) occurring along and just ahead of the dry line. Much lower fine-fuel moistures (3–8% range) exist west of the dry line, with much higher values (15–25% range) to the east. [The two localized regions of lower 1-h dead fuel moisture near the eastern border of Oklahoma are reflective of the lower relative humidities reported by the Mesonet sites in these areas (Fig. 2) as well as the local gradients in relative humidity utilized by the objective analysis scheme.] Figure 4 depicts the burning index across Oklahoma at the same time. Burning index (BI) is directly related to fireline intensity and is perhaps the most useful fire danger index from the NFDRS. It is scaled numerically such that the BI value divided by 10 equals the flame length (in feet) of the headfire. Note the increased fire danger to the west of the dry line (BI values in the 25–95 range); these values correspond to flame lengths of 0.8–2.9 m. To the east of the dry line, fire danger is much lower, with BI values in the 10–19 range immediately east of the line and even lower values (0–9) further east. Finally, Fig. 5 depicts the corresponding spread component map. The spread component (SC) is a measure of how fast the headfire is moving and is numerically equal to the rate of spread in feet per minute. Again, note the strong discontinuity along the dry line. With the exception of the winter wheat belts, which are green this time of year and whose mitigating effects on fire danger are easily seen in this map as well as in the BI map (the yellow and green areas west of the dry line), spread component values in the 40–130 category are common (spread rates of 0.2–0.7 m s–1) to the west of the dry line; at one of the Mesonet stations in the panhandle, an SC value as high as 177 was calculated (0.90 m s–1 spread rate). To the east of the dry line, spread rates are dramatically lower, with most of south-east Oklahoma in the 0–9 range. Through these examples one can easily see the importance of a mesoscale automated weather station network to fire danger assessment. The Mesonet station spacing density of 30 km allows one to see the exact

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locations of features such as dry lines and cold fronts, while the near-real-time availability of data (every 15 min in the case of the fire weather maps) allows one to follow the progress of such features across the area of concern. Most of Oklahoma’s fires involve only 1-h and 10-h fuels, which respond rapidly to changing weather conditions and features such as dry lines. Under the ‘old’ system of three once-a-day NFDRS sites, the exact location of the dry line in the example above along with its corresponding effects on fire danger would have been entirely missed, as would its movement over time (speed and direction). According to the fire chief of Oklahoma Forestry Services (a frequent user of the Oklahoma Mesonet and the OKFD model), firefighters in Oklahoma are interested in the fire danger indices nearest the location of the fire (not just at several sites, which may be far from the actual fire) and nearest the time of the fire, not at 1300 yesterday or earlier today (McDowell, personal communication). The Oklahoma Mesonet and the OKFD model have the capability of capturing the volatility of Oklahoma weather and its effects on fire danger, which can vary dramatically over space and time. Use of the OKFD model and other fire weather products In addition to the Oklahoma Fire Danger Model, other operational tools for wildland fire management have been developed in conjunction with programming support at OU (Carlson et al. 1998; Carlson 2001). These include: (1) Current and recent fire weather maps (going back 6 h in 15-min increments) and maps of recent rainfall over the past 1, 3, 6, 12, 24, 48, and 72 h (http://agweather.mesonet.ou.edu/current-recent-weather); (2) The Oklahoma Dispersion Model (Carlson and Arndt 1998), a tool for use in smoke management (http://agweather.mesonet.ou.edu/models/dispersion); and (3) 60-h MOS (Multiple Output Statistics) forecasts from the Nested Grid Model (http://agweather.mesonet.ou.edu/forecasts). Depending upon the product, the output is updated in time scales ranging from 15 min (in the case of Mesonet fire weather, rainfall, and dispersion maps) to as much as 12 h (in the case of the NGM MOS forecasts). All of these products can be accessed via the Oklahoma Mesonet AgWeather home page (http://agweather.mesonet.ou.edu). Since their inception, all the fire-related management tools have been popular products, used by a wide range of clientele in Oklahoma. Although recent statistics are not available, the number of Web page ‘hits’ through 12 June 2001 were as follows: Oklahoma Fire Danger Model (40 151 hits since 2 June 1997); NGM MOS forecasts (20 238 hits since 28 July 1997); and the Oklahoma Dispersion Model (3767 hits since 1 May 1998). Oklahoma Forestry Services consults these products, particularly the Oklahoma Fire Danger Model, on a regular basis to aid in wildfire preparedness levels and in issuance of Red Flag Fire Alerts and burning ban

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Fig. 2. Fire weather map from 2 p.m. CST (1400 local time) on 25 February 1998. Note the existence of a dry line oriented roughly north to south across central Oklahoma (depicted by the superimposed dashed line). The three NFDRS locations used exclusively before 1996 are again depicted (solid triangles).

Fig. 3. Map of 1-h dead fuel moisture at 2 p.m. CST on 25 February 1998 from the Oklahoma Fire Danger Model. Note the corresponding discontinuity in fuel moisture along and just ahead (to the east) of the dry line.


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Fig. 4. Map of burning index at 2 p.m. CST on 25 February 1998 from the Oklahoma Fire Danger Model. Note the increased fireline intensity behind (to the west of) the dry line.

Fig. 5. Map of spread component at 2 p.m. CST on 25 February 1998 from the Oklahoma Fire Danger Model. Note the high spread rates behind the dry line.

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recommendations to the Governor. State emergency managers and local fire chiefs also utilize these operational tools to monitor fire danger. Finally, the fire management products provide guidance for prescribed burning activities and are used by federal and state agencies as well as by private and public landowners involved in this arena. Summary This paper has focused on the Oklahoma Fire Danger Model (OKFD), an operational mesoscale fire danger rating system for Oklahoma. A joint project of Oklahoma State University, the University of Oklahoma, and the Fire Sciences Laboratory of the USDA Forest Service in Missoula, Montana, the model is an adaptation of the National Fire Danger Rating System (NFDRS) to Oklahoma but, for the first time anywhere, using a spatially dense automated weather station network (the Oklahoma Mesonet) of 110+ stations for hourly weather data. Weekly AVHRR satellite imagery is also used for live fuel moisture and fuel load calculations, in contrast to earlier applications of NFDRS. Model output consists of colorized maps to 1-km resolution of four NFDRS components, as well as interpolated maps of 1-h dead fuel moisture and Keetch-Byram drought index. The OKFD model is currently run 11 times per day, with output available at http://agweather.mesonet.ou.edu/models/fire. In conjunction with fire weather maps updated every 15 min, the OKFD model constitutes a vast improvement over the situation in Oklahoma prior to 1996. Before then, only two to three weather stations were used in NFDRS fire danger calculations, and those calculations were based on once-per-day observations. This contrasts with the current situation, in which rapid access (via the World Wide Web) to 1-km resolution fire danger maps at time intervals as small as 1 h allows fire managers to visually analyse the effects of Oklahoma’s changing weather over space and time. Because most of Oklahoma’s fires involve only 1- and 10-h fuels, which respond rapidly to changing weather conditions, the current system allows fire managers to adjust their dispatching and safety procedures accordingly as conditions improve or deteriorate. Oklahoma fire managers now have access to fire danger information close to the location of the fire (not just at several sites, which may be far from the actual fire) and close to the time of the fire (not at 1300 the day before or earlier in the current day). In addition to the OKFD model, other products of relevance to fire management have been developed. These include current/recent fire weather maps (a 6-h archive is available) and rainfall maps (amounts over the past 1, 3, 6, 12, 24, 48, and 72 h); the Oklahoma Dispersion Model; and 60-h NGM MOS forecasts. These products have also proven useful to the wildland fire management community, both in terms of wildfire anticipation and suppression and for prescribed fire activities.


Future work with respect to the Oklahoma Fire Danger Model will include incorporating numerical weather forecast model output into the OKFD model to predict future fire danger conditions across Oklahoma. In addition, we plan to replace the 1-, 10-, 100-, and 1000-h dead fuel moisture algorithms with numerical models developed by Nelson (Carlson et al. 1996; Nelson 2000). Finally, we look to future developments in the remote sensing of live fuels to help us improve both our mapping and selection of fuel models as well as our estimation of live fuel moistures and loads. Acknowledgements The authors would like to thank a number of people without whom the Oklahoma Fire Danger Model and its associated products would not be possible. First, we are grateful to Larry Bradshaw and Bobbie Bartlette of the Fire Sciences Laboratory in Missoula for helping in the development and remote sensing aspects of the Oklahoma Fire Danger Model. We also thank those fire scientists in Oklahoma who helped us determine appropriate fuel models for the state: they include Terry Bidwell (OSU), Ron Masters (at the time, OSU), and Mark Moseley (USDA-NRCS). We are indebted to those at the Oklahoma Climatological Survey at OU who implemented the various fire-related products on Mesonet computer platforms; aside from the fourth author of this paper who implemented the OKFD model, we wish to thank Derek Arndt, who was responsible for implementing the other fire-related products. Finally, we thank Pat McDowell, fire chief of Oklahoma Forestry Services, for his constructive comments on our fire products and for his regular usage of them. References Barnes S (1964) A technique for maximizing details in numerical weather map analysis. Journal of Applied Meteorology 3, 396–409. Bradshaw LS, Deeming JE, Burgan RE, Cohen JD (1983) ‘The 1978 National Fire-Danger Rating System: technical documentation’. USDA Forest Service, Intermountain Forest and Range Experiment Station General Technical Report INT–169. 44 pp. Brock FV, Crawford KC, Elliott RE, Cuperus GW, Stadler SJ, Johnson HL, Eilts MD (1995) The Oklahoma Mesonet: a technical overview. Journal of Atmospheric and Oceanic Technology 12, 5–19. Burgan RE (1988) ‘1988 revisions to the 1978 National Fire-Danger Rating System’. USDA Forest Service, Southeastern Forest Experiment Station Research Paper SE–273. 39 pp. Burgan RE, Hartford RA (1993) ‘Monitoring vegetation greenness with satellite data’. USDA Forest Service, Intermountain Research Station General Technical Report INT–297. 13 pp. Burgan RE, Hartford RA, Eidenshink JC (1996) ‘Using NDVI to assess departure from average greenness and its relation to fire business’. USDA Forest Service, Intermountain Research Station General Technical Report INT-GTR–333. 8 pp. Carlson JD (2001) Operational wildland fire management systems: the Oklahoma example. Preprints, Fourth Symposium on Fire and Forest Meteorology, American Meteorological Society, 13–15 November 2001, Reno, NV, pp. 140–147.


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Carlson JD, Arndt DS (1998) The Oklahoma Dispersion Model: a Webbased management tool for agricultural practices associated with near-surface releases of gases and particulates. Preprints, 23rd Conference on Agricultural and Forest Meteorology, American Meteorological Society, 2–7 November 1998, Albuquerque, NM, pp. 337–340. Carlson JD, Engle DM (1998) Recent developments in the Oklahoma Fire Danger Model, a mesoscale fire danger rating system for Oklahoma. Preprints, Second Symposium on Fire and Forest Meteorology, American Meteorological Society, 11–16 January 1998, Phoenix, AZ, pp. 42–47. Carlson JD, Engle DM, Shafer MA (1994) Using the Oklahoma Mesonet as a fire management tool. Proceedings, 12th Conference on Fire and Forest Meteorology, Society of American Foresters, 26–28 October 1993, Jekyll Island, GA, pp. 31–37. Carlson JD, Engle DM, Greenfield JR, Arndt DS (1998) Development and dissemination of near-real-time, weather-based tools for fire management in Oklahoma. Preprints, Second Symposium on Fire and Forest Meteorology, American Meteorological Society, 11–16 January 1998, Phoenix, AZ, pp. 48–51. Carlson JD, Nelson RM Jr., Engle DM (1996) Field measurement of dead fuel moisture for model development and implementation on the Oklahoma Mesonet. Preprints, 22nd Conference on Agricultural and Forest Meteorology with

Symposium on Fire and Forest Meteorology, American Meteorological Society, 28 January–2 February 1996, Atlanta, GA, pp. 276–279. Deeming JD, Burgan RE, Cohen JD (1977) ‘The national fire-danger rating system–1978’. USDA Forest Service, Intermountain Forest and Range Experiment Station General Technical Report INT–39. 63 pp. Elliott RL, Brock FV, Stone ML, Harp SL (1994) Configuration decisions for an automated weather station network. Applied Engineering in Agriculture 10, 45–51. Keetch JJ, Byram GM (1968) ‘A drought index for forest fire control’. USDA Forest Service, Southeastern Forest Experiment Station Research Paper SE–38. 32 pp. Koch SE, DesJardins M, Kocin PJ (1983) An interactive Barnes objective map analysis scheme for use with satellite and conventional data. Journal of Climate and Applied Meteorology 22, 1487–1503. Loveland TR, Merchant JW, Ohlen DO, Brown JF (1991) Development of a land-cover characteristics database for the conterminous U.S. Photogrammetric Engineering and Remote Sensing 57, 1453–1463. McDowell P, personal communication (2002) Forestry Services, Oklahoma Department of Agriculture. Nelson RM Jr. (2000) Prediction of diurnal change in 10-h fuel stick moisture content. Canadian Journal of Forest Research 30, 1071–1087.

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