Investigating a New Fire Weather Index Using Reanalysis Data

Conventional wisdom and experience have shown that wildland fires are more likely to exhibit large growth or erratic behavior when conditions become especially hot, dry, and/or windy. However, while the fire-weather community has numerous weather-related indices that attempt to inform the operational community of potentially problematic fire weather conditions, no weather-based fire index is widely available for operational use to assess the potential for variations in temperature, humidity, and winds both at and above the surface to affect a fire. This presentation details the definition and evaluation of one such index, which we call the Hot-Dry-Windy Index (HDW).  HDW is intended to highlight days on which hot, dry, and windy conditions maximize such that anomalous fire behavior can occur, but remain relatively low before and after the anomalous day(s).

While indices intended for operational application to fire situations should always be tested using the best data available in real time, a necessary first step is to look at prior events to determine if the index exhibits predictive power for recent significant fires. The ongoing development of long-term reanalysis products provides the fire-weather community with a consistent dataset that can be readily used to test the validity of fire-weather indices over multiple events across a wide area. We will examine whether HDW has predictive power for determining days where weather could significantly enhance the potential for hazardous fire behavior. We will compare the performance of HDW against existing fire-weather indices to determine whether it demonstrates better predictive power for a selection of recent significant fires than the indices that are currently available to the fire community. We will use meteorological data from the NCEP Climate Forecast System Reanalysis (CFSR) to examine the effectiveness of the index. While the CFSR's resolution is insufficient to assess the impact of local terrain effects or meso-/microscale weather phenomena, the data can be used to find if there are larger-scale meteorological influences that are more likely to affect a given location. In this presentation, we will show that this simple index can provide useful information to the fire community.