Larry Bradshaw

Evaluation of fire danger rating indexes using logistic regression and percentile analysis

Methods are presented for analysing the relationship between fire danger rating indexes and fire activity as a means of evaluating the performance of fire danger rating systems. Percentile analysis is used to examine the data itself; logistic regression provides a means for statistical analysis. Ranking of selected items indicates indexes that deserve further assessment using subjective considerations. Methods rely on generally available data: the fire danger index for every day in the fire season, fire discovery date, and final fire size. For logistic regression analysis, the independent variable is the index, and dependent variables are fire-day, large-fire-day, and multiple-fire-day. Data analysis methods have been incorporated into the FireFamily Plus computer program for easy application. Potential uses of the analysis include choosing the most appropriate fire danger index and fuel model for an area, evaluating proposed changes to a fire danger rating system, and assessing the performance of a system in a location other than that for which it was designed. As a demonstration, this technique was applied to evaluation of several indexes and fuel models of the U.S. National Fire Danger Rating System on the Tonto National Forest in Arizona, USA, using fire and weather data for 1974–2001.

FIRES: Fire Information Retrieval and Evaluation System - A program for fire danger rating analysis

A computer program, FIRES: Fire Information Retrieval and Evaluation System, provides methods for evaluating the performance of fire danger rating indexes. The relationship between fire danger indexes and historical fire occurrence and size is examined through logistic regression and percentiles. Historical seasonal trends of fire danger and fire occurrence can be plotted and compared. Methods for defining critical levels of fire danger are provided. The paper includes a review of NFDRS philosophy and application, a description of input and output, and a summary of fire danger rating programs and data bases and their relationship to FIRES.

A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part II. An exploratory study of the effect of simulated winds on fire growth simulations

The effect of fine-resolution wind simulations on fire growth simulations is explored. The wind models are (1) a wind field consisting of constant speed and direction applied everywhere over the area of interest; (2) a tool based on the solution of the conservation of mass only (termed mass-conserving model) and (3) a tool based on a solution of conservation of mass and momentum (termed momentum-conserving model). Fire simulations use the FARSITE fire simulation system to simulate fire growth for one hypothetical fire and two actual wildfires. The momentum-conserving model produced fire perimeters that most closely matched the observed fire spread, followed by the mass-conserving model and then the uniform winds. The results suggest that momentum-conserving and mass-conserving models can reduce the sensitivity of fire growth simulations to input wind direction, which is advantageous to fire growth modellers. The mass-conserving and momentum-conserving wind models may be useful for operational use as decision support tools in wildland fire management, prescribed fire planning, smoke dispersion modelling, and firefighter and public safety.

The Effect of Model Resolution in Predicting Meteorological Parameters Used in Fire Danger Rating

Previous studies of model performance at varying resolutions have focused on winter storms or isolated convective events. Little attention has been given to the static high pressure situations that may lead to severe wildfire outbreaks. This study focuses on such an event so as to evaluate the value of increased model resolution for prediction of fire danger. The results are intended to lay the groundwork for using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) as input to the National Fire Danger Rating System to provide gridded predictions of fire danger indices. Predicted weather parameters were derived from MM5 and evaluated at three different resolutions (36, 12, and 4 km). Model output was compared with observations during the 2000 fire season in western Montana and northern Idaho to help to determine the models skill in predicting fire danger. For application in fire danger rating, little significant improvement was found in skill with increased model resolution using standard forecast verification techniques. Diurnal bias of modeled temperature and relative humidity resulted in errors larger than the differences between resolutions. Significant timing and magnitude errors at all resolutions could jeopardize accurate prediction of fire danger.

Evaluation of MM5 model resolution when applied to prediction of National Fire Danger Rating indexes

Weather predictions from the MM5 mesoscale model were used to compute gridded predictions of National Fire Danger Rating System (NFDRS) indexes. The model output was applied to a case study of the 2000 fire season in Northern Idaho and Western Montana to simulate an extreme event. To determine the preferred resolution for automating NFDRS predictions, model performance was evaluated at 36, 12, and 4 km. For those indexes evaluated, the best results were consistently obtained for the 4-km domain, whereas the 36-km domain had the largest mean absolute errors. Although model predictions of fire danger indexes are consistently lower than observed, analysis of time series results indicates that the model does well in capturing trends and extreme changes in NFDRS indexes.