Probabilistic Wildfire Risk Flame Length Probability 6 (Image Service)

National data on burn probability (BP) and conditional flame-length probability (FLP) 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 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 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.本数据集包含美国本土（CONUS）、阿拉斯加州与夏威夷州的燃烧概率（burn probability, BP）及条件火焰长度概率（conditional flame-length probability, FLP）全国模拟数据，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系统（Fire Simulation, FSim）生成。该FSim系统集成天气生成、野火发生、火灾蔓延与火灾扑救四大模块，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，以估算当前（2020年末）景观条件与火灾管理模式下，指定网格像元发生燃烧的概率。本数据集提供美国全域270米空间分辨率网格下的模拟BP与FLP数据。火焰长度概率针对六个标准火灾强度等级（Fire Intensity Levels, FILs）开展估算，六个等级对应的火焰长度分类如下：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FLP数据为条件概率（即表示火灾发生前提下，某一强度等级出现的可能性），开展风险评估时需将其与BP数据结合使用。本数据集为Short等人2016年、2020年发布数据的最新版本。第三版数据基于2020年左右的景观数据，即生产时可获取的最新LANDFIRE产品。本次数据生成方法整体沿用此前版本的流程，并在特定环节完成优化。本元数据文档“数据质量与溯源”章节所列流程步骤相较此前版本有所扩充，以更全面地阐释各环节，并补充本版数据的方法细节。除更新了LANDFIRE提供的输入景观数据外，本次研究还对野火发生数据集、网格化逐日观测天气数据及气象站风速数据等其他输入项进行了升级。为更好贴合近期气候条件，本次模拟使用的历史天气记录时长缩短至最近15年（2006-2020年），而非第二版采用的1972-2012年完整历史记录。更多细节可参见“数据质量与溯源”章节中描述的流程步骤。