Look for complete geospatial metadata in this layer's associated xml document available from the download link * Metric Name: Probability of High Fire Severity * Tier: 1 * Data Vintage: 08/2023. Includes disturbances through the end of 2022 * Unit Of Measure: Probability, 0 to 1 * Metric Definition and Relevance: These metrics depicts the probability of high severity fire as constructed by Pyrologix LLC. This operational-control probability raster indicates the probability that the headfire flame length in each pixel will exceed 8 foot flame lengths, the threshold that defines fires that would exceed manual control. * Creation Method: Probability of High Fire Severity (defined as >8 ft) was produced by Pyrologix LLC, a wildfire threat assessment research firm, as part of a spatial wildfire hazard assessment across all land ownerships for the state of California. The ongoing work generally follows the framework outlined in Scott and Thompson (2013), with custom methods and significant improvements developed by Pyrologix. The project generally consists of three components: fuelscape calibration and updates, wildfire hazard assessment, and risk assessment. To date, this work has resulted in a wide variety of spatial data layers related to wildfire hazard and risk, including operational control probabilities based on conditions prior to the 2023 fire season. Work to date has been funded by the USDA Forest Service Region 5, the California Energy Commission, and the USDI Bureau of Land Management with data contributions from CAL FIRE. Please reference the Pyrologix 2021 project report (Volger et al., 2021) for more information. Pyrologix uses the Wildfire Exposure Simulation Tool (WildEST) to develop this data layer, a deterministic wildfire modeling tool that integrates variable weather input variables and weights them based on how they will likely be realized on the landscape. WildEST is more robust than the stochastic intensity values developed with FSim. This is especially true in low wildfire occurrence areas where predicted intensity values from FSim are reliant on a very small sample size of potential weather variables. * Credits: Pyrologix, LLC \--James Newman (California State BLM Office) jnewman@blm.gov