Implications of Hot-Dry-Windy Spatiotemporal Sensitivity

Due to increasingly frequent extreme fire weather conditions—for example, high winds, low relative humidity, and an abundance of dry, overgrown vegetation—forecasters rely on fire weather metrics such as the Hot Dry Windy Index (HDWI) for wildfire response and decision-making. These metrics incorporate data with distinct timescales to predict where and when conditions have the potential to exacerbate wildfire-management efforts. The HDWI is a once-daily maximum value currently produced operationally by the U.S. Forest Service out to 14 days on a 0.5° spatial grid. It is the product of vapor pressure deficit and maximum wind speed data and was designed by Srock et al. (2018) to use predictable, synoptic-scale components of weather forecasts that are indicative of where atmospheric conditions are likely to promote extreme fire behavior. The development of higher-resolution products has created new opportunities for HDWI reanalysis and forecast generation. However, there may be logical inconsistencies in the HDWI at higher temporal and spatial resolutions that have not been previously tested; questions also persist regarding the intricacies of its calculation. Here, we use ERA5, CONUS404, and NCEP reanalysis data to derive the HDWI in four variants to evaluate sensitivity to temporal and vertical resolution. We also investigate open questions and assumptions used to generate the operational product. The four variants allow us to directly compare the operational method with minor modifications to ease processing needs, improve forecast clarity, and mitigate potential overnight impacts that may be missed in the time subset. In addition, the use of reanalysis products allows us to investigate trends and implications of the HDWI over the contiguous United States, Alaska, and Hawaii. We present the findings of our HDWI sensitivity assessment as well as an outlook for future tools and analyses.