Santa Ana winds, common to Southern California from the fall through early spring, are a type of downslope windstorm originating from a direction generally ranging from 3608/08 to 1008 and are usually accompanied by very low humidity. Since fuel conditions tend to be driest from late September through the middle of November, Santa Ana winds occurring during this time have the greatest potential to produce large, devastating fires upon ignition. Such catastrophic fires occurred in 1993, 2003, 2007, and 2008. Because of the destructive nature of such fires, there has been a growing desire to categorize Santa Ana wind events in much the same way that tropical cyclones have been categorized. The Santa Ana wildfire threat index (SAWTI) is a tool for categorizing Santa Ana wind events with respect to anticipated fire potential. The latest version of the index has been a result of a three-and-a-half-year collaboration effort between the USDA Forest Service, the San Diego Gas and Electric utility (SDG&E), and the University of California, Los Angeles (UCLA). The SAWTI uses several meteorological and fuel moisture variables at 3-km resolution as input to the Weather Research and Forecasting (WRF) Model to generate the index out to 6 days. In addition to the index, a 30-yr climatology of weather, fuels, and the SAWTI has been developed to help put current and future events into perspective. This paper outlines the methodology for developing the SAWTI, including a discussion on the various datasets employed and its operational implementation.

The criteria used to define Santa Ana winds (SAWs) are dependent upon both the impact of interest (e.g., catastrophic wildfires) and the location and/or time of day examined. We employ a comprehensive definition and methodology for constructing a climatological SAW time series from 1981 through 2016 for two Southern California regions, Los Angeles and San Diego. For both regions, we examine SAW climatology, distinguish SAW-associated synoptic-scale atmospheric patterns, and detect long-term, significant SAW trends. San Diego has 30% fewer SAW days compared to Los Angeles with 80% of SAW events starting in Los Angeles first. Further, 45% of San Diego SAW events are single-day events compared to 35% for Los Angeles. The longest duration event spanned 16 days for Los Angeles (27 November–12 December 1988) and 8 days for San Diego (9–16 January 2009). Although SAW-driven fires can be large and devastating, these types of fires occurred on only 6% and 5% of SAW days for the Los Angeles and San Diego regions, respectively. Finally, we find and investigate an extended period of elevated SAW day count occurring after 2005. This new climatology will allow us to produce month- and season-ahead forecasts of SAW days, which is useful for planning end-of-year staffing coverage by the local, state, and federal fire agencies.

Live fuel moisture content plays a significant and complex role in wildfire propagation. However, in situ historical and near real-time live fuel moisture measurements are temporally and spatially sparse within wildfire-prone regions. Routine bi-weekly sampling intervals are sometimes exceeded if the weather is unfavourable and/or field personnel are unavailable. To fill these spatial and temporal gaps, we have developed a daily gridded chamise (Adenostoma fasciculatum) live fuel moisture product that can be used, in conjunction with other predictors, to assess current and historical wildfire danger/behaviour. Chamise observations for 52 new-and 41 old-growth California sites from the National Fuel Moisture Database were statistically related to dynamically downscaled high-resolution weather predictors using a random forest machine learning model. This model captures reasonably well the temporal and spatial variability of chamise live fuel moisture content within California. Compared with observations, model-predicted live fuel moisture values have an overall R 2 , root mean squared error (RMSE) and bias of 0.79, 15.34% and 0.26%, respectively, for new growth and 0.63, 8.81% and 0.11% for old growth. Given the success of the model, we have begun to use it to produce daily forecasts of chamise live fuel moisture content for California utilities.

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