Modification of the Hot-Dry-Windy Index Using High Resolution Rapid Refresh Model Data

Abstract

Fire weather is defined as the meteorological conditions conducive to the rapid spread and intensification of a wildfire. It is generally agreed that sudden and rapid wildfire intensification is one of the greatest hazards facing wildfire managers today. The Hot-Dry-Windy (HDW) Index created by Srock et al. (2018) provides a means for predicting rapid wildfire intensification. A limitation of the HDW index is the temporal output of once every six hours when utilizing the National Centers for Environmental Prediction (NCEP) Coupled Forecast System Model Version 2 (CFsV2) data. The use of National Oceanic and Atmosphere Administration (NOAA) High Resolution Rapid Refresh (HRRR) model data has the benefits of being able to capture mesoscale processes along with a six-fold increase in the temporal outputs of the HDW index. This assists in the capture of a high fire spread rate due to a rapid change in meteorological conditions that would have been otherwise missed by the coarser resolution provided by the original model. This analysis was made with the meteorological model data in the window of a historic wildfire case featuring Santa Ana winds (SAWs). The HRRR data was found to be much more capable of modeling the terrain of the coastal mountain ranges in the southern California area. The mesoscale modeling capability also aided in the HDW Index not suffering as severely from artificially lowered values near the coast when compared with the CFsV2 output.