United States Department of Agriculture

DEPAR TMENT OF AGRICULT URE

Forest 

Service

FireBuster—A Web Application 

for High-Resolution Fire Weather 

Modeling

Shyh-Chin Chen, John Benoit, Jack Ritchie, Yunfei Zhang, Hann-Ming Henry 

Juang, Ying-Ju Chen, and Tom Rolinski

Pacific Southwest 

Research Station

General Technical Report

PSW-GTR-264

July

2019
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Authors

Shyh-Chin Chen is a research meteorologist and John Benoit is a computer 

scientist, U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station, 4955 Canyon Crest Drive, Riverside, CA 92507; Jack Ritchie is 

an information systems analyst, Scripps Institution of Oceanography, University of 

California–San Diego, 9500 Gilman Drive, San Diego, CA 92093; Yunfei Zhang is 

a professor, National Marine Environment Forecasting Center, Ministry of Natural 

Resources, 100081 Beijing China; Hann-Ming Henry Juang is a research meteorologist, National Centers for Environmental Prediction, 5830 University Research 

Court, College Park, MD 20740; Ying-Ju Chen is a project assistant, National 

Central University, Taoyuan City, Taiwan 32001; Tom Rolinski is a meteorologist, 

U.S. Department of Agriculture, Forest Service, Pacific Southwest Region, 2524 

Mulberry Street, Riverside, CA 92501.

Cover: Huge smoke column on the Elk Complex in Idaho. Photo by Kari Greer.
FireBuster—A Web Application for 

High-Resolution Fire Weather Modeling 

Shyh-Chin Chen, John Benoit, Jack Ritchie, Yunfei Zhang, 

Hann-Ming Henry Juang, Ying-Ju Chen, and Tom Rolinski

U.S. Department of Agriculture, Forest Service

Pacific Southwest Research Station

Albany, California

General Technical Report PSW-GTR-264

Published in cooperation with:

U.S. Department of the Interior 

Bureau of Land Management

Abstract

Chen, Shyh-Chin; Benoit, John; Ritchie, Jack; Zhang, Yunfei; Juang, HannMing Henry; Chen, Ying-Ju; Rolinski, Tom. 2019. FireBuster—a web application for high-resolution fire weather modeling. Gen. Tech. Rep. PSW-GTR-264. 

Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 22 p.

Wind and weather in mountainous areas are complex because of the underlying 

terrain. Typically, regional computer models are needed with sufficiently high resolution to resolve such complex conditions. However, this high-resolution weather 

information usually becomes available only when the critical time in fighting a 

severe fire event is long past, thus the advantage of using high-resolution weather 

models for fire management seems limited. To address this problem, we have 

developed an experimental system called FireBuster that is designed to streamline 

and automate many intermediate processes. We are routinely producing forecasts 

at 5-km resolution over California and Nevada. A meteorologist can then select 

any part in the domain to request a special 1-km resolution 72-hour forecast. The 

resulting fire weather variables can be retrieved in a reasonable time through a web 

interface as each 6-hour increment is completed. Observed fire perimeters and nearsurface weather from the MesoWest observational network are also available for 

display and for future validation. In addition, 72-hour weather forecast time series 

anywhere in the domain can be retrieved simply by clicking on a map. This feature 

provides firefighters with detailed weather forecasts, including winds, at their location, improving their potential to save lives and property during wildfires. 

Keywords: Fire weather, meso-scale modeling, web application. 
Contents

1 1. Introduction

3 2. Components

3 2.1 GFS Input

5 2.2 Regional Model

5 2.3 Surface Station Observations

6 2.4 Fire Perimeters

7 2.5 FireBusterSim

7 3. Web Display

7 3.1 FireBuster Display

9 3.2 Example: Bernardo Fire

12 3.3 Example: Thomas Fire

14 3.4 Example: Powerhouse Fire

14 3.5 Example: Portable Spot Forecast

18 4. Summary

19 Acknowledgments

19 U.S. Equivalents

19 References
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FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

1. Introduction

Despite its ecological benefit, wildland fire continues to be a threat to natural 

resources and societies in the United States and worldwide. The escalation of fire 

management costs in recent decades to suppress the increasing extent of large wildland fires and fires in the wildland-urban interface has become a huge burden to 

the responsible firefighting agencies. Between 2008 and 2017, the U.S. Department 

of Agriculture Forest Service and agencies in the Department of the Interior spent 

more than $17 billion for fire suppression (National Interagency Fire Center 2017), 

prompted by the need to protect human lives as well as public and private property. 

Improvements in fire weather information, and hence an improved fire management 

system, would help reduce the loss of life and property.

Although weather information has been recognized as a critical parameter in 

developing and evaluating fire management strategies, its effectiveness in actual 

firefighting is poorly understood and not well studied (Hesseln et al. 2010). One 

constraint is a scarcity of available surface weather observations, while another is a 

limitation in the spatial resolution of available numerical weather prediction models. 

Both components have reduced the quality of weather information available to fire 

managers (Headley 1927). The first limitation has been greatly mitigated recently 

with the installation of many Remote Automated Weather Stations (RAWS) (https://

raws.nifc.gov), adding to the network of existing surface stations, throughout 

the wildland areas of the United States. However, the spatial resolution problem 

remains, partly because of the limited availability of official high-resolution 

weather information in the vicinity of fire incidents. The current operational Global 

Forecasting System (GFS) (National Modeling Center 2003) of the National Centers 

of Environmental Prediction (NCEP) (e.g., Han and Pan 2011, Juang and Hong 

2010, Kleist et al. 2008, Zheng et al. 2012) provides weather forecasts four times 

a day at the meteorological Z times of 00z, 06z, 12z, and 18z, with a base resolution in the tens of kilometers over southern California. For the contiguous United 

States (CONUS), NCEP also provides forecasts via the North American Mesoscale 

(NAM) Forecast System, which uses the Weather Research and Forecasting (WRF) 

regional model (https://www.mmm.ucar.edu/weather-research-and-forecastingmodel) at 12-km (7.5-mi) resolution (Rogers et al. 2009). Recently, the National 

Oceanic and Atmospheric Administration (NOAA) began producing a cloudresolving and convection-allowing High-Resolution Rapid Refresh (HRRR) (https://

rapidrefresh.noaa.gov/hrrr/) 36-hour atmospheric model forecast system initialized 

by 3-km grid with radar assimilation for the CONUS area. For additional highresolution forecasts, National Weather Service (NWS) forecasters can incorporate 

all or part of these model outputs by using the Graphical Forecast Editor (GFE) of 
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GENERAL TECHNICAL REPORT PSW-GTR-264

the Interactive Forecast Preparation System (IFPS) to create and maintain a 2.5-

km gridded forecasts of surface-sensible elements out to 7 days. These forecasts 

comprise the NWS National Digital Forecast Database (NDFD) (Glahn and Ruth 

2003). Also available are some other higher resolution regional weather predictions 

for a few selected experimental regions; however, the fire-prone mountainous areas 

of California are not included.

Weather forecasts with spatial resolutions higher than the official predictions 

described above have been available for many years (e.g., Liu et al. 2013, Mölders 

2008, Mölders et al. 2014, Pennelly and Reuter 2017). However, these studies use 

high-resolution models either to test downscaling suitability or to produce future 

regional climate assessments. Applications of high-resolution weather models to 

actual wildland firefighting on an operational basis are still scarce. Although these 

modeling capabilities can be applied to support wildland fire events, delays in getting 

model output to fire meteorologists and firefighters prevent such detailed fire weather 

information from being used in actual firefighting decisionmaking. In the past, we 

have been asked a few times to provide high-resolution fire weather information to 

fire practitioners. However, when we finally completed the computation, the event 

was long past, and our intelligence could, at best, be used only for postfire evaluation.

The California and Nevada Smoke and Air Committee (CANSAC) (https://

cansac.dri.edu) is one of the few organizations that provides operational-like forecasts with three resolution settings more detailed than are available in current official forecasts for the Western United States. The CANSAC uses the WRF model for 

these downscaled nesting forecasts, with the innermost domain covering California 

and Nevada at 2-km resolution. To complete desired forecasts over such a big area at 

such high resolution, the required computing resources are overwhelming. Still, for 

firefighting and fire management support, the need remains for fire weather forecasts at spatial resolutions beyond what HRRR or CANSAC can currently provide. 

To provide timely fire weather information at a spatial resolution relevant to 

firefighting operations, we developed an experimental system called FireBuster, 

bringing effective support to fire practitioners in California and Nevada. The idea is 

to first provide twice-daily fire weather forecasts at 5-km resolution similar to those 

in CANSAC. During an actual fire incident anywhere in California or Nevada, an 

authorized user can submit an online request to initiate automated 1-km-resolution 

weather model computations as well as to display weather information on a designated web page for the area of the targeted fire. Our current system takes less than 

50 minutes to complete a 72-hour fire weather forecast at 1-km spatial resolution.

FireBuster components and weather model specifications are described in 

section 2 of this report. Section 3 demonstrates how fire weather variables can be 

displayed in a few sample fire events. Summary and future developments are then 

given in section 4. 
3

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

2. Components

The FireBuster system integrates three components: NCEP’s global forecast or 

analysis as input; the Regional Spectral Model (RSM) (Juang and Kanamitsu 1994) 

for downscaling forecasts; and model outputs as well as data layers of surface station observations and fire perimeters. Fire weather variables from these components 

are displayed with a Google MapTM (https://developers.google.com/maps/documentation/) interface that provides a fire weather intelligence system for firefighting 

support and fire science research. When the global forecast input to the system 

is replaced by global analysis, the forecast-oriented FireBusterProg transforms 

to FireBusterSim. The difference in these two systems is simply one of forecasts 

versus simulations.

2.1 GFS Input

The Global Forecasting System is the current operational global analysis and 

forecast modeling system used by NCEP. The four-time-daily (00z, 06z, 12z, and 

18z) forecasts are produced at model triangular truncation of 1534 (TL382), which 

resolves to 13-km grid spacing at the equator. In addition to this control run forecast, there are also 20 ensemble runs with slightly perturbed initial conditions with 

a coarser horizontal resolution for each increment forecast. For our FireBusterProg 

(https://fwxfcst.us/firebuster), we use only the control runs (72 hours forecast) 

initialized at 00z and 12z, because there are relatively small ensemble spreads for 

the initial 72 hours. The GFS analyses at 06z and 18z are also retrieved for FireBusterSim integration.

All GFS products can be accessed through the NCEP server NOMADS (NOAA 

Operational Model Archive and Distribution System) (http://nomads.ncep.noaa.

gov/). Although the global GFS data are available at NOMADS, the lengthy data 

transfer time prevented us from fetching this global dataset within the required 

time period. Instead, we accessed a subsection of GRIB (GRIdded Binary) (https://

en.wikipedia.org/wiki/GRIB) data over the Western United States using a GRIB 

filter available at NOMADS (http://nomads.ncep.noaa.gov/cgi-bin/filter_gfs_0p50.

pl). GFS model variables of temperature, u- and v-wind, temperature, and humidity at 47 vertical standard pressure levels, and all model initialization required 

surface variables, such as surface pressure, soil temperature, soil moisture, plants 

resistance, skin temperature, etc. (at 0.5-degree grid spacing) are downloaded and 

prepared to initialize and to force the regional model runs. We refer to this input as 

the RSM0 dataset. It covers the mountainous West in a 65 × 65 grid at 40-km grid 

spacing as shown in figure 1. 
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GENERAL TECHNICAL REPORT PSW-GTR-264

48N

46N

44N

42N

40N

38N

36N

34N

32N

30N

28N

129W 126W 123W 120W 117W 114W 111W 108W 105W 102W

RSMO Data Domain

Figure 1—RSM0 data domain over the Western United States. A sample of the grid is shown in the lower left corner. The entire domain 

has 65 × 65 points with 40-km grid spacing.
5

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

2.2 Regional Model

The GFS outputs from NCEP provided initial and lateral boundary conditions for 

the 5-km resolution Mesoscale Spectral Model (MSM) (Juang 2000) runs. The 

MSM is the non-hydrostatic version of the RSM, which has been used in much 

of our previous regional modeling work (e.g., Chen 2001; Chen et al. 1999, 2008; 

Roads et al. 2010). NCEP’s RSM/MSM was developed using similar physical 

parameterizations to those of the global model in the GFS. Therefore, except for a 

slight difference in the form of governing equations, RSM theoretically provides 

a seamless transition of horizontal resolution from global to regional scales (Chen 

et al. 1999). This RSM downscaling system thus avoids possible simulation drift 

or bias resulting from a mismatch in model physics modules between the imposing global and the forced regional models (Chen 2001). RSM has been tested in 

many intercomparison regional climate model downscaling projects, and it showed 

comparable, if not superior, climate downscaling skill (e.g., Roads et al. 2003, 

Tackle 1999). The model also demonstrated mesoscale simulation ability that is 

absent in the current global model. Anderson et al. (2000) showed that the model 

quite realistically captured the low-level jet over the Gulf of California during the 

Southwest United States monsoon season.

The regional model computations are done using in-house high-performance 

computers (HPC) at Pacific Southwest Research Station’s (PSW) Riverside laboratory. The 5-km domain run requires slightly less than 2 hours wall-clock time for 

a 72-hour forecast. Runs at this resolution are done twice daily, initialized at 00z 

and 12z. The former run completes around 0045 Pacific Standard Time (PST), 

while the latter completes around 1145 PST. Basically our runs are 9 hours behind 

the initialization Coordinated Universal Time (UTC) time. The 1-km run with a 

much smaller domain needs about 50 minutes to complete a 72-hour downscaling 

forecast. Running at this resolution is available upon request. The location and the 

initialization time can be determined by the user, as will be shown later.

2.3 Surface Station Observations

A concern of the first-line fire managers and fire meteorologists is the accuracy of 

these high-resolution models in predicting near-surface fire weather variables, the 

wind field in particular; this is especially true over complex terrain such as that in 

California and Nevada. Although high-resolution mesoscale weather models have 

been around for many decades, most of the model validation studies had been accomplished with ground-based variables such as precipitation or surface temperature (e.g., 

Saito et al. 2006) or for extreme weather events during special field experiments (e.g., 

Lopez et al. 2003). A systemwide evaluation of the adequacy of using a high-resolution 
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GENERAL TECHNICAL REPORT PSW-GTR-264

mesoscale model for wildland fire has yet to be conducted before land process management tools can confidently incorporate information from such models. 

In preparation for future validation of our fire weather forecast system, observed 

hourly surface weather variables (wind and temperature) from the National Mesonet 

Program (https://nationalmesonet.us) are also archived for comparison alongside 

model simulations and forecasts. To cover the area in the current version of FireBuster, we are archiving all available surface station data in California and Nevada. 

These data were converted to JSON (JavaScript Object Notation) format so that they 

can be readily used by the Google Maps API (Application Programming Interface) 

for display. The availability of these data not only provides users a preliminary 

evaluation during the first few hours of each forecast, but also provides ultimate 

validation of the downscaled simulations or forecasts of the system.

Data from the National Mesonet have many sources. For example, the stations 

(numbers in parentheses) in the domain include the NWS’s Weather Forecast Office 

Network (90), NWS/Federal Aviation Agency Network (134), NOAA National 

Water Level Observation Network (5), NOAA Coastal Stations (4), California Data 

Exchange Center (7), California Hydroelectric Power Network (43), NOAA California Nevada River Forecast Center (34), U.S. Climate Reference Network (10), 

Desert Research Institute (47) and other universities (23), MTR Weather Forecast 

Office (28), Orange County Public Network (59), Caltrans stations (78), California 

Irrigation Management Information System (149), northern California NOAA 

Automatic Weather Observation System (14), Remote Area Weather Stations (460), 

NWS Hydrometeorological Automated Data System (460), AirNow Aire Quality 

stations (27), California Air Resources Board (173), San Diego Gas & Electric Inc. 

(174), Automated Position Reporting System (977), and other regional but fewer 

numbered stations, for a total of 3,265 stations. These hourly data can be displayed 

in their pre-assigned Google Maps zoom level, which is subjectively predetermined 

by their level of reliability and significance. This procedure avoids overcrowding 

the display and lessens the computing burden on the client browser. 

2.4 Fire Perimeters

Available ground or aerial observed fire perimeter data are automatically downloaded from GeoMAC (https://www.geomac.gov/) on a daily basis. Perimeter files 

are converted to GeoJSON format, which can be displayed over a Google Maps 

background. A consecutive series of perimeters for a fire can be displayed simultaneously. Therefore, if a fire has been burning for a few days, the progression of the 

fire can be displayed on the same map with fire weather information; this can be 

very useful for fire meteorologists or fire managers in making timely decisions.
7

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

2.5 FireBusterSim

When the global forecast RSM0 input to the system is replaced by that of global analysis, the forecast-oriented FireBusterProg turns into FireBusterSim (https://fwxfcst.us/

firebustersim). FireBusterSim provides an excellent way to contrast the downscaling 

skill to forecast skill of FireBusterProg when the forecast uncertainty is removed.

3. Web Display

3.1 FireBuster Display

The portal for FireBusterProg is at https://fwxfcst.us/firebuster. Although all Forest 

Service security requirements have been met, we are still in the process of obtaining 

permission to be included within the agency’s domain (https://www.fs.fed.us). Figure 

2 displays the main web page of FireBusterProg. The colored area shows the current 

5-km resolution domain. We preserved the most basic features of Google Maps, 

including its zooming capability. The variable data layer is also synchronized with 

the zoom level of the map. A total of 11 near-surface scalar fire weather variables 

can be chosen for display. These include the 2-m temperature (°F), precipitation (in 

inches hr-1), relative humidity (percent), surface convective available potential energy 

(CAPE) (J kg-1), best lifted-index (K), planetary boundary layer (PBL) depth (m), 

windspeed (mph), Fosberg Fire Weather Index (FFWI), Large Fire Potential (LFP, F 

mph2), temperature tendency (°F day-1), and relative humidity tendency (% day-1), as 

well as vector winds for MSM and GFS forecasts. Temperature and humidity 24-hour 

tendencies are helpful for determining magnitudes in trends, while FFWI (Fosberg 

1978) and LFP (Rolinski et al. 2016) are used to reflect the level of fire danger. 

Hourly surface observations, including those from RAWS, can be codisplayed 

along with values forecasted by the models. For a typical forecast initialized at 00z 

(completed around 0045 PST), the observations for the first 8 hours of forecast are 

available. These observed surface variables can be used as a preliminary reference 

to judge the quality of the downscaled forecast. Fire perimeters, when they are 

available, are another useful observation dataset to be used with a forecast.

When the teardrop marker is dragged to a location on the map, or by manually 

inputting longitude and latitude (which is how the center of the desired 1-km forecast domain is defined), the request can be initiated. A request window will pop up 

(fig. 3). The latitude and longitude of the 1-km model domain center, as well as the 

initial date of the forecast, are also indicated. Naming of the incident and a password 

are required before the request can be submitted. Model initialization, integration, 

and display will be subsequently processed by PSW’s HPC system and a web server. 

The forecast results can be accessed from the menu under “Recent 5-km runs” and 

“Recent high-resolution runs” on each of the lower corners of the page. 
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GENERAL TECHNICAL REPORT PSW-GTR-264

Figure 2—The landing page of FireBuster, showing the 5-km resolution domain over California. The forecast initialization and 

the end of the forecast dates are shown in the upper right, followed by the displayable fire weather variables, cursor’s function, 

and color scale of the displayed variable. A time slider is directly below the map. Users can input latitude and longitude for the 

center of the desired 1-km run domain below the time slider, followed by two clickable buttons to redraw the map and to request 

a 1-km run. Historical 5-km and 1-km runs can be accessed near the bottom of the page.
9

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

3.2 Example: Bernardo Fire

We used the Bernardo Fire, which ignited on 1100 PST, May 13, 2014, about 10 km 

(6 mi) southwest of Escondido, California, to demonstrate the downscale ability of 

FireBusterProg. The 5-km FireBusterProg product at the time of ignition is shown 

in figure 4. A prevailing strong northeasterly wind, a signature of the extremely dry 

downslope Santa Ana winds (Rolinski 2016), provided favorable conditions for fire 

spread. Over the inland area, there was not much variation in windspeed and direction at such resolution. The coarse GFS wind provided even less spatial variation. 

When the teardrop marker is moved to the nearby location of the fire event, the 

red box indicates the domain of the 1-km run that will be executed. After the request 

for the 1-km run is submitted, the results of the run will be available by selecting 

the name of the fire appearing on the lower right corner of the page. Comparing the 

windspeed and vector plot of the 1-km run in figure 5 to that of figure 4, strong northeasterly to easterly winds can be seen occurring over the higher terrain in the 1-km 

run, but they are not visible in the 5-km run. Cross sections of potential temperatures 

Figure 3—In this pop-up request 1-km run window, the center of the 1-km run domain is 

placed at the shown latitude and longitude. The initial date of the run will be as shown. The 

name of the run as well as an authenticated password are needed to submit the request.
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GENERAL TECHNICAL REPORT PSW-GTR-264

in this vicinity show a significant gravity wave breaking over the higher terrain upstream 

to the fire origin, with air parcels descending toward the coast (Fovell and Cao 2017). 

Areas of lower wind velocities immediately to the east and northeast of the fire origin 

appear to be indicative of a hydraulic jump, which is a feature of the downsloping nature 

of Santa Ana wind events in this part of southern California (Cao and Fovell 2013). The 

forecasted weather and wind validate well against those corresponding RAWS of Ramona 

Airport (right most of the map), McClellan-Palomar Airport (upper leftmost), and Torrey 

Pine (bottom), at this particular hour. However, the temperature observation from Escondido SPV (top right) seems to be spurious. Accurate wind information around a fire event 

is critical to fire managers in planning effective firefighting tactics.

Figure 4—The 5-km resolution FireBusterProg display of the 1400z May 14, 2014, forecast initialized at 00z May 13, 2014, 

with windspeed and vectors shown at 10-km intervals. The ignition point of the Bernardo Fire, which was about 10 km south of 

Escondido, California, is marked by the teardrop-shaped marker. 
11

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

Even more useful intelligence can be extracted from 5-km or 1-km forecasts. 

Figure 6 shows a feature called a fire weather “spot forecast.” By selecting “Spot 

Forecast” from the variable menu, a pop-up window shows a 72-hour forecast time 

series of fire weather variables of temperature, relative humidity, and wind at a 

desired location. These time series of the variables give a brief evolution display 

for the forecast period. The observations from a desired nearby surface station, as 

shown with the relative direction and the distance to the selected location, can be 

used as validation when they become available. For a real-time forecast, observations are available only for the first few hours in the forecast. Nevertheless, these 

observations can serve as a preliminary accuracy verification of the forecast.

Figure 5—A screen capture of FireBusterProg 1-km run results showing surface windspeed (in color) and wind vector forecast 

for area around the Bernardo Fire of May 13, 2014, about 8.0 km (5 mi) south of Escondido, California (marked with an asterisk). The map shows 38th-hour forecasted weather validated on May 14 at 0700 PST. 
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GENERAL TECHNICAL REPORT PSW-GTR-264

3.3 Example: Thomas Fire

The Thomas Fire (https://en.wikipedia.org/wiki/Thomas_Fire) started December 4, 

2017, in Ventura County, California, just south of Thomas Aquinas College. There 

had been no measurable precipitation since September 2017, causing fuel conditions 

to be abnormally dry. The fire exploded quickly owing to the combination of strong 

northeasterly winds and single-digit relative humidity. The Thomas Fire consumed 

a total of 281,893 ac (114 078 ha); at the time it was contained, it became California’s largest wildfire on record.

Figure 7 shows a 1-km FireBusterProg forecast snapshot for the Thomas Fire. 

Relative humidity, which was in the range of 10 to 20 percent, and forecasted northeasterly winds, correlated well with those surface RAWS observations. FireBuster 

is also capable of superimposing a fire perimeter data layer on top of fire weather 

variables. Figure 7 overlays the fire agency’s observed fire perimeter for 0440 PST 

December 5 on the map. It demonstrates well how the fire spread from the ignition 

point southwest toward Ventura. In practice, when a fire manager is examining the 

Figure 6—Fire weather spot forecast at the fire incident site. Time series of temperature (F), relative humidity (percent), and wind (miles 

per hour and direction) are shown for a 72-hour forecast. The nearby surface station observations can be displayed when they become 

available. The yellow vertical line indicates current time when requested.
13

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

latest fire weather forecast such as that in figure 7, the fire perimeter data may not 

be ready for viewing yet because of its delayed availability on GeoMAC. Still, this 

functionality is useful for postfire evaluation. 

An interesting feature can be seen when comparing the fire perimeter with 

these downscaled fire weather forecasts. To the west of the western edge of the fire 

perimeter over Oak View, California, a relatively calm and moist area was encircled 

by surrounding taller hills. The final fire perimeter (not shown) shows split fire 

fronts to the north and the south of this area. This calm and wet pocket is not present in the 5-km forecast, let alone in the GFS forecast.

Figure 7—A screen capture of FireBusterProg 1-km run results showing surface relative humidity (in color) and wind vector 

(miles per hour) forecast over Ventura County area around the Thomas Fire, which ignited on December 4, 2017, at 1826 PST. 

The map shows 24th-hour forecasted weather validated on December 5 at 0400 PST. Available RAWS observation and the GFS 

forecasted wind are also shown in miles per hour.
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GENERAL TECHNICAL REPORT PSW-GTR-264

3.4 Example: Powerhouse Fire

The Powerhouse Fire (https://en.wikipedia.org/wiki/Powerhouse_Fire) in Los Angeles County, California, started at about 0230 PST on May 30, 2013. This fire burned 

more than 12 141 ha (30,000 ac) and destroyed 53 structures. This fire was different 

in nature than the previous two examples in that it was not associated with Santa 

Ana winds. In fact, possibly driven by changes in synoptic conditions as well as 

the diurnal variation in the mountain area, the wind changed directions a few times 

during the initial stage of the fire. We use this case to highlight the high-resolution 

forecast and simulation capability of FireBusterProg and FireBusterSim, respectively. 

Figure 8 shows the FireBusterSim simulation of the Powerhouse Fire which 

displays the simulated windspeed and wind vectors present on 2200 PST, June 1, 

2013. Also shown are the fire perimeter reported at 2211 PST and available surface 

station observations. Fire perimeters followed the simulated downscale wind 

direction initially toward the west and south before it turned northward around 1600 

PST. At 2200 PST, winds shifted to a southward direction over the fire’s northern 

flank (fig. 8), and halted its northward progression. The forecasted wind demonstrated a similar shift in wind direction (fig. 9). There is also a reversal of wind 

to northerly (southward), likely owing to a nocturnal downslope wind at the time 

when simulation revealed the same turn. A 1- to 2-hour delay for the time of wind 

direction change was present throughout the length of the forecast when results 

between FireBusterProg and FireBusterSim were compared. However, in general, 

the downscaled forecasts matched well with those of the simulation and the RAWS 

observations. 

3.5 Example: Portable Spot Forecast

The spot forecast of surface variables in FireBuster might be a potentially useful 

tool for ground crews. When the weather model resolution is coarse, the surface 

variables produced by the model can be far from realistic because of the unmatched 

terrain elevations to the real world. Vertical interpolation or extrapolation would 

be needed to substitute model values for actual surface values. FireBuster is relatively less affected by such a problem because the high-resolution model surface 

elevation is off only by a few meters from the true elevation. However, accessing 

these forecasts through our data-laden web page would be limited by one’s Internet 

connection speed in the field. Hence it is not very practical to operate the function 

onsite through the FireBuster web page. 
15

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

To circumvent the problem, we extricated the spot forecast feature from FireBuster and converted it to a standalone mobile web application (https://fwxfcst.us/

spot_forecast). Figure 10 shows the interface of this application. The device geolocation is retrieved using the geolocation service feature in HTML5. The user can 

also enter their desired location. Once “Get Forecast” is selected, the spot forecast 

of the current 5-km run will be displayed first. From there, a 1-km run forecast can 

be selected from the bottom menu if available. If this feature is proven useful to 

users, it can be easily converted to an application for mobile devices.

Figure 8—FireBusterSim 1-km run results showing surface wind (miles per hour in color) and wind vectors over the Los Angeles County area around the Powerhouse Fire, which ignited at 0230 PST on May 30, 2013. The map shows simulated weather 

validated at 2200 PST on June 1, 2013. Fire perimeters reported for 2211 PST are also plotted.
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GENERAL TECHNICAL REPORT PSW-GTR-264

Figure 9—FireBusterProg 1-km run results showing surface wind (miles per hour in color) and wind vectors over the Los 

Angeles County area around the Powerhouse Fire, which ignited at 0230 PST on May 30, 2013. The map shows simulated 

weather validated at 2200 PST on June 1, 2013. Fire perimeters reported for 2211 PST are also plotted. The run was initialized 

at 1200 UTC on June 1, 2013. The background terrain has been removed to better view wind vectors.
17

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

Figure 10—Mobile web spot forecast interface page. The current geolocation is shown on the upper portion of the page. The spot 

forecast at current location from 5-km run will be shown in the bottom half of the page. If a 1-km run covers the current location, its 

spot forecast data can be retrieved with a click.
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GENERAL TECHNICAL REPORT PSW-GTR-264

4. Summary

To provide the wildfire-fighting community with higher resolution and timely fire 

weather information, we have developed a web-based experimental system called 

FireBuster, designed to streamline and automate many intermediate processes. 

The system uses the geographic information system in Google Maps to effectively 

display meteorological variables. We are routinely producing forecasts at 5-km (~3 

mi) resolution over California and Nevada. Users can then select any part in the 

domain to request a special 1-km- (~0.6 mi-) resolution, 72-hour forecast through a 

simple navigation process. All computations are done on HPCs at PSW Riverside 

with a turnaround time fast enough to support fire operations. The resulting fire 

weather variables can be retrieved within a reasonable time through a web interface 

as forecasting of each 6-hour increment is completed. Near-surface weather from 

the National Mesonest observational network is provided for future validation or 

preliminary verification for the initial few hours of a forecast during the operation. 

Fire perimeters of wildfires are also imported into the system daily to display along 

with the fire weather variables. In addition, 72-hour spot forecast time series of 

detailed surface wind, temperature, and humidity anywhere in the domain can be 

easily retrieved. 

We subsequently extricated the spot forecast function from the FireBusterProg 

system and made it into a standalone mobile web application with the ability to 

automatically input the user’s geolocation data. We believe that this feature has 

the potential to be a relevant tool for ground crews for the purpose of retrieving 

vital fire weather forecast information at their current location, and could potentially save lives and property in the event of wildfires. If this web application is 

found to be useful in the field, our plan is to convert this application into a mobile 

device app. Although FireBuster features one of the highest resolution mesoscale 

meteorological modeling frameworks for firefighting operational support, 1-km 

grid spacing is still a bit too coarse to match the actual fire scale. Work is also 

underway to improve the spatial resolution to <1 km, replacing the current 1-km 

module while retaining the computation time that is short enough for operational 

support. These enhancements will position FireBuster to be part of an integrated 

fire management kit.
19

FireBuster—A Web Application for High-Resolution Fire Weather Modeling 

Acknowledgments

The development of FireBuster and FireBusterSim was supported by U.S. Forest 

Service Research and Development, FS-NOAA Memorandum of Understanding 

funds, and awards from the Forest Service Western Wildland Environmental Threat 

Assessment Center, and Joint Fire Science Program. We are deeply indebted to our 

former colleague Dr. Francis Fujioka, who inspired us to examine improved uses of 

fire weather forecasting in the field and motivated us to develop this application. We 

also thank Mark Jackson, Peter Roohr, and Eric Knapp, who helped us improve the 

manuscript. Regional forecasts and simulations from these tools are experimental 

and are intended for research use, hence no endorsement by the U.S. Department of 

Agriculture is implied.

U.S. Equivalents

When you have: Multiply by: To get:

Meters (m) 3.8 Feet

Kilometers (km) .621 Miles

Hectares (ha) 2.471 Acres

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