Intermediate National Fire Danger Rating System S491
Unit 5 Decisions and Applications
Lesson 5F – Related Products
OBJECTIVE:
Provide information on products that support fire program decisions. 5F.1-3
I. DISPLAYING FIRE DANGER AND FIRE WEATHER
5F.4
A. Wildland Fire Assessment System
The Wildland Fire Assessment System (WFAS) has been developed by
the Fire Behavior Research Work Unit of the Rocky Mountain Research
Station and is now supported and maintained by NITC. Some of its
products are in the form of national maps emphasizing selected fire
weather and fire danger components. WFAS is a dynamic system as it
continues to develop into the next generation fire danger and fire
behavior systems.
WFAS currently offers point-based map data in a Google Earth
compatible format.
WFAS developers have been working on a process to use seven day
forecasts from the National Weather Service to estimate future fire
danger. These fire danger forecasts are being produced at 6:00 AM
Mountain Time each day for the current day and the next six days.
Images of the forecast Energy Release Component for Fuel Model G are
currently being provided and future enhancements will offer more
indices, components and fuel moistures as well as more comprehensive
analysis tools.
For more information go the following web link www.wfas.net
Examples of products at WFAS:
• AVHRR NDVI (Greenness)
• National Fuel Moisture Database (Season trends of live fuel
moisture from sampled sites)
5F.1
• WFAS Map Server (applet with interface to point and click station
data)
• Next Day Gridded Forecasts
• 7 Day NDFD Forecasts
• Google Earth Map Data
• Map Archives
• Potential Lightning Ignitions
• Dry Lightning Maps
• Large Fire Potential & Fire Potential Indexes
WFAS is a dynamic system that continues to develop into the next
generation fire danger and fire behavior systems.
B. ROMAN 5F.5-7
ROMAN has been designed for use by fire weather professionals and
others requiring access to current fire weather conditions around the
nation. Comments and suggestions for improvement are encouraged
and can be sent to atmos-mesowest@lists.utah.edu.
Fire weather information is accessible in several different ways:
National Weather Service County Warning Area maps
National Weather Service Fire Weather Zone maps
Geographic Coordinating Areas (GCAs) maps
U.S. State maps
MODIS Imagery maps
pull down menus
fire weather in the vicinity of fires
Documentation
Horel et al. (2004) IIPS Conference. January 2004
5F.2
C. Haines Index 5F.8-11
1. Background
Research has long shown that atmospheric moisture and instability play
significant roles in fire growth. In 1988, research meteorologist Donald
Haines completed a study that correlated large fire growth with lower
atmospheric temperature lapse rates and moisture. The study looked at
over 70 fires, in different areas of the county that showed significant
growth not associated with wind events.
From the results of the study, Haines developed the Lower Atmosphere
severity Index (LASI), which later commonly because known as the
Haines Index (HI).
2. Features of Haines Index
a. The Haines Index has correlated well with large fire growth.
b. It gives equal weight to lower atmospheric lapse rates (temperature
change with altitude) and moisture.
c. It may be applied to low, mid, and high elevation portions of the
country.
(1) Low elevation HI uses the layer of the atmosphere from
950mb (~2000 ft) to 850mb(~5000 ft). Generally used in the
eastern and southern states with the exception of the
Appalachian Mountains.
(2) Mid elevation HI uses the layer from 850mb to 700mb
(~10,000 ft). This is used in the Appalachian Mountains and
the Great Plains from the Dakotas to portions of west Texas.
(3) High elevation HI uses the layer from 700mb to 500mb
(~18,000 ft). The high elevation HI is used throughout the
western states.
(4) The HI is computed twice daily across the country using
upper air soundings. It is displayed for the contiguous
United States, and for North America on the Wildland Fire
Assessment System web site.
5F.3
(5) The Haines Index ranges from two to six:
2&3 Very low potential for large fire
growth
4 Low potential
5 Moderate potential
6 High potential
3. Haines Index Considerations
a. This index pertains to the potential for a going fire to become large
and/or erratic. It does not attempt to address the threat of new
ignitions.
b. It does not include wind; another weather parameter that can
greatly affect fire growth.
c. The Haines Index can have significant local variability. The same
HI value may indicate different large fire growth potential in
different parts of the country. Research continues toward the goal
of making regional variations to the Haines Index.
d. The Haines Index alone will not give the fire manager a complete
assessment of the potential for large fire growth. Like most other
indices, it is a tool that should be used in conjunction with other
parameters.
II. DROUGHT INDEXES 5F.12
A. Standardized Precipitation Index
1. Purpose
The Standardized Precipitation Index (SPI), formulated at the Colorado
Climate Center in 1993, was designed to express the fact that it is
possible to simultaneously experience wet conditions on one time scale
and dry conditions at another scale. In other words, an area may
5F.4
receive above normal precipitation over a three month period, but still
be well below normal for the past 12 months. The purpose of the SPI is
to assign values to the precipitation which can then be compared across
regions with markedly different climates.
2. How the SPI works
a. The United States has been divided into 344 climate divisions,
with no more than 10 per state.
b. Historical precipitation data back through 1895 has been
normalized over the climate divisions.
c. Five quantities are computed based on these climate divisions.
(1)Accumulated Precipitation – The total precipitation that has
fallen during the selected number of months.
(2)Accumulated Precipitation Departure – The amount by which
the Accumulated Precipitation varies from the long-term
average for the same time period.
(3) Precipitation Percentile – How often a value of the magnitude
observed has been measured during this same time period
throughout the historical record. In other words, its degree of
unusualness. A value of five means that only five percent of
the values in the climatological record were lower than the
value in question. A value of 90 indicates that the value in
question is in the top 10%, or that 90% of all the values in the
climatological record were drier than the value in question.
(4) Standardized Precipitation Index – The SPI is the number of
standard deviations that the observed value would vary from
the long-term mean for that time period. It is designed to be a
single-value representation that can be compared across
regions with distinct climates. Positive SPI values indicate
greater than median precipitation, while negative values
indicate less than median precipitation. A separate SPI value is
calculated for a selection of time scales covering the last 1
through 12 months, as well as the last 15, 18, 24, 30, 36, 48,
60, and 72 months.
5F.5
The SPI and associated values can be viewed for the various
time periods at several climate-related web sites such as the
Western Region climate Center.
For more information go to the following web link:
http://drought.unl.edu/monitor/spi.htm
B. Palmer Drought Index
1. Purpose of the Palmer Drought Index
The Palmer Drought Index (PDI) was originally developed in 1965 in
order to measure the departure of the moisture supply from the
climatological normal. The objective was to provide measurements of
moisture conditions that were standardized so that comparisons using
the index could be made between locations and between months. It is
effective measuring impacts sensitive to soil moisture conditions, such
as agriculture. Drought monitoring is another major use of the PDI.
2. Inputs of the Palmer Drought Index include:
a. Precipitation
b. Air Temperature
c. Available water content of the soil
3. Benefits of the Palmer Drought Index
a. PDI provides decision makers with the abnormality of recent
weather for a region.
b. Long-term archives of the PDI values for every climate division in
the United States are kept at the National Climatic Data Center.
c. Ideally, the PDI is designed so that a -3.0 in Georgia has the same
meaning in terms of moisture departure from a climatological
normal as a -3.0 in Montana.
5F.6
4. Drawbacks of the Palmer Drought Index
a. The values signaling the beginning and end of a drought or wet
spell were arbitrarily selected based on a study of central Iowa and
western Kansas.
b. The PDI works best in large areas of uniform topography. It does
not perform as well in areas with mountainous terrain.
c. Snowfall, snow cover, and frozen ground are not included in the
index, with all precipitation being treated as rain.
d. Because one of the major inputs to PDI is soil moisture, it may not
be a good indicator of the drying experienced by the larger dead
fuels.
(1) The PDI is designed for agriculture, and may not accurately
represent impacts resulting from longer droughts.
(2) A national map of the Palmer Drought Index is produced by
the National Oceanographic and Atmospheric
Administration‟s Climate Prediction Center (CPC). This map
may be viewed on CPC‟s web site, or on numerous other
climate-related web sites.
For more information go to the following web link:
www.drought.noaa.gov/index.html
Also check www.drought.gov for more information
III. NATIONAL WEATHER SERVICE 5F.13
Precipitation Analysis (Spatial Precipitation)
These pages graphically show the short-term observed and climatic trends of
precipitation across the conterminous United States and Puerto Rico.
The observed data is derived from output from 12 NWS River Forecast
Centers (RFC‟s) and is displayed as gridded field with a spatial resolution of
4x4 km.
5F.7
http://water.weather.gov/precip/
IV. OPERATIONAL APPLICATIONS OF NFDRS 5F.14
A. Geographic Area
Situational analysis
Assist setting preparedness levels
Pre-position resources
Prioritize areas for resource allocation
http://gacc.nifc.gov
5F.15
B. National
– Seasonal assessment
• Potential Workload Forecast
• Resource allocation priorities
• Setting national preparedness levels
– Severity approval
– Fire policy administration
5F.8
Here is one of the products produced by the national group:
http://www.nifc.gov/nicc/predictive/fuels_fire-danger/fuels_advisories.htm
5F.16
C. ERC Percentile Mapping 5F.17-23
• Derive climatology for each PSA once using Fire Family Plus
• Update the index in a database
• Query the average index by PSA
• Calculate and store the percentile by ERC
5F.9
5F.24
D. Appropriate Management Response
NFDRS can be used to help AMR decisions
E. WFDSS – General Overview 5F.25
1. The Wildland Fire Decision Support System (WFDSS) project was
developed to streamline and improve wildland fire decision-making
processes, as well as take advantage of improvements in technology,
fire modeling, and geospatial analysis. In 2005 the National Fire and
Aviation Executive Board chartered WFDSS to replace the legacy
Wildland Fire Situation Analysis (WFSA), Wildland Fire
Implementation Plan (WFIP) and Long-Term Incident Planning
(LTIP) processes with a system that:
- Develops a scalable decision support system for agency
administrators
- Uses appropriate fire behavior modeling, economic principles, and
information technology
- Supports effective wildland fire decisions consistent with Resource
and Fire Management Plans
5F.10
Key attributes of WFDSS include:
- Enables spatial data layering
- Reduces text, increases use of map displays
- Reduces input requirements
- Begins process at time of discovery
- Removes alternative comparison and decision tree development
- Pre-loads information from Fire Management Plans
- Provides scalability for incident complexity (WFDSS contains
three Response Levels: RL1, RL2, RL3, which equate to changing
complexity)
2. WFDSS California Example: 5F.26-27
Southern California example . . .
As part of the WFDSS incident “Analysis,” an option is available to
retrieve the ERC climatology graph of the nearest RAWS based on a
user-defined coordinate. The graph will display the maximum,
minimum, average, and current calendar year ERC based on all years
available for the respective RAWS.
The graph is intended to provide the user with a “snapshot” of the
current fire danger. It provides the manager with a relative
comparison of the current ERC (yellow line) with respect to the
station‟s historical ERC. In addition, an algorithm ingests forecasted
weather inputs to display a five day forecast of ERC (green line).
What potential decision traps do we encounter with this product?
a. Anchoring Trap: If this is the fire (and perhaps only) information
regarding current and forecasted fire danger, the mind will give
disproportionate weight to this initial impression. If the manager is
under pressure to make a quick decision, the anchoring trap
becomes particularly acute as there is little patience to wait for
alternative information.
b. Framing Trap: At first glance, a manager‟s cognitive shortcuts
will attempt to make sense of the complex fire danger information.
Frames help us to interpret the world around us and represent that
5F.11
world to others. However, you should ask a few questions to deal
with any hidden framing traps. For example:
• What is the intent of viewing “Fire Danger?” Hopefully, the
user is not using the information for fire behavior purposes; one
of the assumptions of NFDRS is that it should represent the
potential for an initiating fire. Also, ERC is not a function of
wind; wind is a critical input for fire behavior.
• Is the data at this station representative of the fire location (i.e.,
elevation, distance, etc.)? How do adjacent stations compare?
• Are the climatological breakpoints (which are displayed at 90th
and 97th percentiles) representing a pre-conceived perspective
regarding fire danger?
• Are there gaps in the historical weather data? How much is this
affecting the way the graph is displayed?
• If the incident occurs early in the calendar year (e.g., January or
February), what was the relative fire danger prior to January
1st?
• Does NFDRS Fuel Model G correlate well with Energy Release
Component in the area around the incident? Remember that
ERC does not represent the effect of wind.
c. Overconfidence Trap: Remember that we tend to be
overconfident about the accuracy of our forecasts. When managers
look at a graphic such as this, they tend to put too much emphasis
on the forecasted weather and resulting fire danger. Consequently,
be cautious of the validity of the input data which will affect the
NFDRS outputs.
d. Prudence Trap: When faced with high-stakes decisions, we tend
to adjust our predictions or forecasts “just to be on the safe side.”
Looking at this graphic, the fire danger during the previous week
was approaching the 90th percentile; would you tend to say that the
current dip in ERC is short-term; consequently, you lean toward
decisions to be “on safe side” due to the proximity to the urban
interface (Santa Barbara).
5F.12
2. WFDSS Florida Example: 5F.28-29
Florida example . . .
Note: due to much higher minimum Relative Humidity in the
Southeast, ERC (because of the high influence of 1000-hr fuel
moisture) may not be the best indicator of fire danger. The range of
ERC is relatively shorter (smaller decision space).
F. Fire Program Analysis (FPA)– General Overview 5F.30-31
1. The Fire Program Analysis (FPA) system is a common interagency
application for wildland fire planning and budgeting. This tool enables
the five federal fire management agencies (the USDA Forest Service
and the Department of the Interior‟s Bureau of Indian Affairs, Bureau
of Land Management, National Park Service and US Fish and
Wildlife Service) to plan jointly. FPA is also designed to encourage
nonfederal wildland fire partners' participation. The 2001 Federal
Wildland Fire Management Policy, a revision of the 1995 Federal Fire
Management Policy, requires standardized training, data, data
collection and a standard interagency planning and budgeting process.
Additionally, in 2001, Congress directed the Departments of
Agriculture and the Interior to develop a coordinated and common
system to determine readiness and improve the allocation of fire
resources to improve effectiveness and efficiency. FPA is that system.
Fire Planning Units develop investment alternatives using FPA for the
preparedness and fuel treatment components of the fire program
(preparedness component consists for initial response organizations
and prevention programs). Once completed, the Fire Planning Units
submit their alternatives to the national level for interagency review
and analysis. Results from the national-level analysis provide input to
fire program planning and budget development, thus FPA „informs the
budget.‟
• Five federal fire management agencies (Forest Service, Bureau
of Indian Affairs, Bureau of Land Management, National Park
Service and US Fish and Wildlife Service) are encouraged to
plan jointly.
• The use of Burning Index and Energy Release Component are
referenced within the Dispatch Logic portion of the program.
5F.13
Refer to FPA TechNews:
http://www.fpa.nifc.gov/Library/TechNews/Docs/FPA_2/Tech
News_12_19_08_final.pdf
• The FPA whitepaper entitled Validating the Initial Response
Simulator (IRS)
(http://www.fpa.nifc.gov/Library/Papers/Docs/FPA_2/WP_Val
idating_IRS_12_02_2008_final.pdf it states
"Using Fire Family Plus, or your knowledge of fire
occurrence in your FPU, analyze your modeled fire event
distributions. Planners may need to adjust dispatch-level
breakpoints in the FPU‟s dispatch logic so the breakpoints
correspond with the historical breakpoint distribution. The
objective is to ensure the output represents the typical initial
response production capability for each dispatch level as
occurred during the historical period.“
Although FPA will encourage the use of FireFamily Plus to analyze
fire event distributions to establish “Dispatch Logic” classes, be
careful not to associate the FPA‟s “derived BI” with the NFDRS
calculated BI. The values are significantly different.
This Fire Program Analysis (FPA) screen capture is loaded with
framing traps.
2. Within the FPA Fire Event Scenario process, fire behavior is
calculated for each fire event. The fire behavior attributes calculated
are Rate of Spread (ROS) and Flame Length. The Flame Length is
used to calculate a Burning Index (BI) by multiplying it by 10 to
provide a “derived BI”. BI as found on FPA reports is not a
NFDRS calculated Burning Index. FPA is attempting to model
potential crown fire with a derived BI; consequently, Burning Index
calculated by the NFDRS model will not match the “derived Burning
Index” in FPA. Given the same inputs, the difference between the two
values will be drastically different.
3. Within the “Dispatch Logic” section of FPA, the user is instructed to
use “NFDRS breakpoints” to establish “Fire Dispatch Levels
(FDLs).” The template established for entering this data looks very
much like a pre-planned dispatch plan based on various decision
classes of Burning Index.
5F.14
4. The FPA whitepaper entitled Validating the Initial Response
Simulator (IRS)
(http://www.fpa.nifc.gov/Library/Papers/Docs/FPA_2/WP_Validating
_IRS_12_02_2008_final.pdf ) it states:
"Using Fire Family Plus, or your knowledge of fire occurrence in your
FPU, analyze your modeled fire event distributions. Planners may
need to adjust dispatch-level breakpoints in the FPU‟s dispatch logic
so the breakpoints correspond with the historical breakpoint
distribution. The objective is to ensure the output represents the
typical initial response production capability for each dispatch level as
occurred during the historical period.“Refer to FPA TechNews:
http://www.fpa.nifc.gov/Library/TechNews/Docs/FPA_2/TechNews_
12_19_08_final.pdf
V. NEW RESEARCH AND DEVELOPMENT 5F.32
A. Fire Danger Rating Characteristics Chart 5F.33-35
Allows multiple indices to be viewed at once. Can be used for briefings
or in Fire Danger Operating Plans. Future versions of FireFamilyPlus
will be able to produce this chart.
B. Nelson Dead Fuel Moisture Model 5F.36-38
The Nelson Dead Fuel Moisture Model is driven by hourly precipitation,
solar radiation, temperature and relative humidity and eliminates the
need for user-defined state of the weather (SOW) inputs. The model runs
on hourly data and can be defined for any size class of fuel. It is a
physical model of moisture gain and loss through a dead fuel and it has
been shown to be a better predictor of fine dead fuel moisture than used
in the original NFDRS logic. There are two options of the Nelson Dead
Fuel Moisture analysis in FFP 4: Hourly Values and Compare with daily
NFDRS. Currently work is being completed to implement the use of the
Nelson Model in WIMS.
1. Hourly Values
In FireFamily Plus the Hourly Values listing will produce a report of
the hourly fuel moistures for each of the four NFDRS dead fuel size
5F.15
classes. It includes the hourly data used to calculate the moistures as
well as a summary of the settings for that run. When choosing the
hourly values report from the menu the user will first be prompted for
startup values for each size class.
The dead fuel moisture model of NFDRS requires the user to enter a
state-of-the-weather (SOW) in order to estimate the fuel relative
humidity and temperature. SOW is based on sky cover and is related
to the amount of incident solar radiation. The Nelson model uses
physically-based models to describe the relationship of several
meteorological variables to the temperature and moisture content of
fuels. It quantifies the moisture gain or loss due to diffusive and
capillary processes. The hourly inputs to the Nelson model are:
temperature, relative humidity, hourly precipitation and solar
radiation. There are no user inputs to run the model and the model can
be configured to estimate the moisture content of any size of fuel
particle, from the smallest one hour fuels to the largest 1000 hour
fuels.
5F.16
2. Examples of mean hourly solar radiation for three time periods for a
weather station in central Washington for January (A), April (B) and
July (C). Notice that the maximum solar radiation in July is much
higher than the maximum solar radiation in January.
5F.17
3. Changes in stick average moisture content fraction versus hourly
captured rainfall (dt = 1 h) during field experiments in Burnsville,
N.C. (circles), and Mio, Mich. (triangles). Solid symbols, initial
moisture fraction smaller than 0.4; open symbols, initial fraction
greater than 0.4 (from Nelson, 2000).
Burnsville, N.C. moisture content fraction (average of five arrays of
10-h sticks) as observed (broken line) and predicted (solid line) versus
time expressed as calendar date (from Nelson, 2000).
5F.18
5F.39-46
C. Growing Season Index
1. The Growing Season or Live Fuel lndex
http://www.wfas.net/index.php/growing-season-index-experimental-
products-96
The live fuel moisture model in NFDRS is without a doubt the
weakest link in the entire system and it has been acknowledged as
such for many years. A simple model, suitable for application across a
range of landscapes and plant function types is desirable to replace the
NFDRS. The Growing Season or Live Fuel Index was designed to
replace the greenup and senescence processes of live vegetation and
may be used in the future to replace the greenup dates, senescence and
curing inputs of the 1978 NFDRS model and it will replace the season
codes and greenness factors in the 1988 Revision of the system.
2. Background
a. Plant phenology is generally driven by three periodic climatic
factors - temperature, water and sunlight. Phenology is the study
of the seasonal cycles of plants. We will focus on foliar
phenology which looks at seasonal changes in green leaf cover.
These seasonal changes are related to changes in fuel moisture
content and are thus important to danger rating.
b. Constraints:
1) Hydroperiod – Seasonal changes in soil moisture (i.e.
Monsoonal climates)
2) Thermoperiod – Seasonal changes in temperature regimes (i.e.
warming spring, warm summers, cool falls and cold winters).
3) Photoperiod – Seasonal changes in the daylight period (i.e.
Short days in winter and long days in summer)
c. These environmental constraints are not independent; any of
these constraints can co-limit phenology throughout the year.
d. We know that these phenological constraints are different at
different places around the world. The global maps shows areas
5F.19
relative to their dominant constraint: red areas show water limits,
blue areas show temperature limits and green areas show
photoperiod limits.
e. Phenological studies can be grouped into two categories: Micro
and Macro
1) Microphenology refers to the study of individual plants or
species cohorts.
2) Macrophenology refers to the study of phenology at the
ecosystem level. It is more continuous and generalized and is
thus more applicable to systems such as NFDRS.
3. Model Introduction
The model is developed around three variables that serve as surrogates
for the three different periodic climatic factors: temperature, water and
light.
a. Temperatures limits are represented by minimum temperature.
b. Water limits are represented by vapor pressure deficit (VPD)
i. VPD is a simple recalculation of relative humidity and
can be expressed using fire weather data.
ii. VPD has a larger dynamic range and is more related to
the absolute drying capacity of the atmosphere.
c. Day length is used for the photoperiod control
i. Day length is a simple function of latitude and year/day.
4. The Indicators
a. GSI uses three indicators for each of the three model variables.
b. These indicators vary continuously from 0 to 1.
1) Each indicator has a minimum and maximum limiting value.
2) Values below the minimum are assumed to completely limit
plant functions and values above the maximum are assumed
to have no effect on plant activity.
c. 0 indicates that a particular variables completely constrains plant
functioning and 1 indicates that a variable does not limit plant
activity.
d. The respective upper and lower limits for each indicator are
shown on the slide.
5F.20
Indicators are calculated daily for minimum temperature, vapor
pressure deficit and day length and the product of these indicators
is the Growing Season Index. GSI is then averaged over 21 day
periods by default but this period length can be changed in
FireFamily Plus.
5F.21
5. The Daily Growing Season Index
GSI is calculated as the product of the three daily indicators for
minimum temperature, photoperiod and vapor pressure deficit. This
final index is continuous and varies from 0 to 1. The index is related
to the relative constraints of temperature, water and light limits on
plant activity. This mathematical formulation is important because it
allows ANY of the three variables to limit plant processes at any time.
For example, if any of the individual indicators is zero (0) then the
final product by definition must be zero. Also, if any of the individual
indicators values is one, then they have no effect on the index.
6. Final Growing Season Index
a. The daily index is calculated and used in a 21-day running
average.
b. This running average smoothes the seasonal signal and limits fast
increases and decreases in the index value due to discrete, short-
term weather events such as one or two warm days early in the
year.
7. GSI Example
This graph shows an example GSI trace for a single year compared to
the satellite-derived Normalized Difference Vegetation Index for the
same location in Alaska. The weather-derived Growing Season Index
closely mimics the changes in leaf area observed from the satellite.
5F.22
In FireFamily Plus, Growing Season Index is renamed to Live Fuel
Index because it is rescaled from 0 to 100 rather than from 0 to 1 as
follows: LFI = GSI * 100.
To set the parameters for LFI, chose the Options > LFI Options
menu items and the dialog box below will show.
Here you can adjust the upper and lower limits for each of the three
indicators, as well as the running average period and whether to use
average VPD or maximum VPD in the calculations.
5F.23
Example of a Live Fuel Index climatology calculated using FireFamily Plus v.4
VI. FORECAST INITIALIZATIONS 5F.47-49
The National Weather Service takes data from two NWS-managed mesonets
(DCNet [Washington DC National Capital Region] and RAMAN [East
Tennessee River Valley]), grid it, quality control it, project it on 5-km grid;
observations are on the same grid/resolution as NDFD (National Digital
Forecast Database).
RTMA Product Description. The National Weather Service (NWS)
weather forecast offices (WFO) produce and send digital forecasts to various
users. These forecasts of hydrometeorological variables, such as temperature
and precipitation, contribute to the generation of the NWS National Digital
Forecast Database (NDFD). The Real-Time Mesoscale Analysis (RTMA) is
a gridded analysis of the hydrometeorological variables that matches the
NDFD spatial resolution (5-km).
5F.24
RTMA product generation occurs for the CONUS region and includes the
following products: surface temperature; surface dew point; wind speed and
direction; cloud and precipitation amount products; and u and v wind
components.
RTMA Purpose. The primary purpose of the RTMA is to provide an
National Digital Forecast Database (NDFD) matching-resolution analysis to
verify NWS digital forecasts.
VII. FORECASTING SYSTEMS 5F.50-54
A. 7-Day Significant Fire Potential Product
Overview: The 7-Day Significant Fire Potential Product is the latest
decision support tool produced by Predictive Services pertaining to the
allocation of resources. This product was designed to determine when
and where regionally and nationally shared resources would be in
demand for fire suppression across the U.S. for the next 7 days. For the
purposes of this product, Significant Fire Potential has been defined
as…”The likelihood a wildland fire scenario will require mobilization
of additional resources from outside the area in which the fire scenario
originates.” For the purpose of this product we have defined a
significant fire in most areas, mainly in the west, to be a fire large
enough to require resources from outside the fire event area. Large fire
size has been individually determined for each Predictive Service Area
(PSA) across the US. A PSA is a defined as an area of similar fuel and
climatology regime. Large fire sizes vary by fuel type, climatology,
resource availability, and location (such as the wildland-urban
interface.) It is important to note that when interpreting this product it
is crucial to understand that although weather is a major contributor to
large fire potential, this product is not a weather forecast! It is a
Significant Fire Potential Forecast.
B. The Significant Fire Potential Model:
Fire managers know that assessing large fire potential is a difficult task.
There are many variables that are responsible for large fire development
such as fuel dryness, fuel type, fuel continuity, weather triggers, terrain,
resource capability, and suppression strategy. In an attempt to simplify
the model, some assumptions can be made concerning a few of the
5F.25
variables mentioned above and thus the conceptual model for significant
fire potential becomes a function of fuel conditions (dryness only),
significant weather triggers, and resource availability.
C. Fuel Dryness:
Remote Automated Weather Stations (RAWS) are used within the fire
community to access real-time weather information. Data collected from
these RAWS can also be used for weather forecasting, research purposes
and climate analysis. The National Fire Danger Rating System (NFDRS)
was developed to help predict fire behavior and activity at each RAWS.
One of the most practical ways to determine fuel dryness is to relate fire
danger indices with fire occurrence using statistical methods. A statistical
analysis was done using selected RAWS for each PSA to correlate
historical NFDRS indices with historical fire occurrence data to
determine fuel dryness thresholds for each PSA. Fuel dryness is
represented by one of three colors described below.
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Ave. %
Dryness
Large Fire Potential Description probability of a
Level
large fire event
Green Indicates a DL which historically has
resulted in a very low probability of 1-3%
(Moist)
large fires.
Indicates a transitional dryness situation
Yellow
that will not typically result in large 5-7%
(Dry) fires unless accompanied by a
Significant Weather Trigger.
Indicates a DL which results in a much
higher than normal probability of large
Brown
fires when accompanied by a 12-15%
(Very Dry) Significant Weather Trigger. A low to
moderate probability for large fire exits
in the absence of a trigger.
Indicates an especially high probability
of large fires. Occurs when the DL is
Red either brown or yellow and is
accompanied with a significant weather 20-25%
(High Risk)
trigger. DL will appear red with a
symbol designating the specific weather
trigger.
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D. Significant Weather Triggers:
A weather phenomena resulting in an environment that has a significant
impact on fire spread, intensity, or occurrence.
An expected combination of dry fuels and a lightning trigger.
This is NOT simply a lightning forecast, but a forecast of lightning
conducive to large fire activity.
Represents critically dry and windy conditions. While this
condition does not start fires, it often produces a favorable
environment for new starts or existing fires to become large.
An expected critical combination of dry fuels and an
unseasonably hot and dry air mass. While this condition does not start
fires, it often produces a favorable environment for new starts or
existing fires to become large.
Represents high recreation in combination with dry fuels.
Certain „high rec‟ days can lead to a high number of ignitions when the
fuels are dry, this combination can lead to an elevated risk of large
fires. A good example has been the 4th of July.
An expected critical combination of an unstable air mass and
dry fuels. While this situation does not start fires, it often produces a
favorable environment for new starts or existing fires to become large.
E. Resource Availability:
As competition for resources increase, the probability of large fire
activity rises, provided that new ignitions occur.
F. The Product:
The model employed in this product uses fuel dryness, weather, and
resource information to make the final assessment for where and when
large fires will occur. The variables that make up fuel dryness, whether
they be the ERC, 10 hr, 100 hr fuel moisture, etc. are projected out to 7
days using regression equations that have been developed through the
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Desert Research Institute (DRI). The equations are based on various
meteorological parameters which simulate the NFDRS indices. The
product itself consists of a chart that contains fuel dryness information
for each PSA along with any significant weather denoted by various
symbols. In addition to the chart, the product contains a narrative section
highlighting local weather and fuel conditions, as well as available
resources for initial attack activity. Additional information about the 7
Day Significant Fire Potential Product can be found on each GACC‟s
Predictive Services webpage where available.
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Review Lesson 5F Objective: 5F.55
Provide information on products that support fire program decisions.
Review Unit 5 Objectives: 5F.56
• Introduce the Fire Danger Rating Operating Plan.
• Compare and contrast climatological breakpoints and fire business
thresholds.
• Determine, distinguish and apply staffing level and adjective rating.
• Examine how to use NFDRS as a decision-making tool.
• Demonstrate the ability to create a PocketCard and discuss additional
applications of NFDRS.
• Provide an introduction and description of other products which support the
application of fire danger rating.
Review Course Objectives: 5F.57
Upon completion of this course the student will demonstrate the knowledge and
skills necessary to operate, apply and manage the National Fire Danger Rating
System.
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