Probabilistic Wildfire Risk Burn Probability (Image Service)

National data on burn probability (BP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = &lt; 2 feet (ft); FLP2 = 2 &lt; 4 ft.; FLP3 = 4 &lt; 6 ft.; FLP4 = 6 &lt; 8 ft.; FLP5 = 8 &lt; 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.本数据集由美国农业部林务局米苏拉火灾科学实验室开发的地理空间火灾模拟（Fire Simulation, FSim）系统生成，涵盖美国本土（CONUS）、阿拉斯加州与夏威夷州的全国燃烧概率（Burn Probability, BP）数据。FSim系统集成了气象生成、野火发生、火灾蔓延及火灾抑制四大模块，旨在模拟数万次假设的当代火灾季场景下的野火发生与蔓延过程，从而估算在2020年末的当前景观条件与火灾管理模式下，任意给定区域（即像元）发生燃烧的概率。 本次发布的美国全境数据，以270米网格的空间分辨率提供了建模得到的燃烧概率（BP）与火焰长度概率（Flame-Length Probability, FLP）。火焰长度概率基于六个标准火灾强度等级（Fire Intensity Levels, FILs）进行估算，各等级对应的火焰长度类别如下：FLP1 = <2英尺（ft）；FLP2 = 2~4英尺；FLP3 = 4~6英尺；FLP4 = 6~8英尺；FLP5 = 8~12英尺；FLP6 = ≥12英尺。由于该类数据属于条件概率（即表征在火灾发生的前提下，达到特定强度等级的燃烧可能性），因此在开展火灾风险评估时，FLP数据需与BP数据结合使用。 本数据集是Short等人（2016、2020）发表的数据集的更新版本。本次第三版基于2020年前后的景观数据，即数据制作时可获取的最新LANDFIRE产品。本次数据生成流程大体沿用前版框架，但在特定环节进行了优化升级。 本元数据文档"数据质量与溯源"章节中列出的流程步骤相较前版有所扩充，以更全面地阐释各操作环节，并补充了本版数据的方法细节。除了更新自LANDFIRE的输入景观数据外，研究团队还对其他输入数据集进行了升级，包括火灾发生观测数据、网格化逐日气象数据，以及源自气象站的风速数据。 为更好地贴合近期气候状况，本次模拟所用的历史气象记录时段从第二版的1972-2012年全记录，缩短为最近15年（2006-2020年），用于生成模拟气象序列。更多细节可参见元数据文档"数据质量与溯源"章节中描述的流程步骤。