论文标题
来自小数据样本的热带气旋空间波极端的统计估计:使用长期合成气旋数据验证STM-E方法的验证
Statistical estimation of spatial wave extremes for tropical cyclones from small data samples: validation of the STM-E approach using long-term synthetic cyclone data for the Caribbean Sea
论文作者
论文摘要
热带气旋在某个位置的出现很少见,对于许多地点,只有短期的观察或后广播。因此,对于旋风诱导的小样本诱导的显着波高(SWH)的回报值的估计(对应于可用数据的周期比可获得的数据的时间大得多)是具有挑战性的。开发了STM-E(时空最大值和暴露)模型,以减少与竞争对手在这种情况下的竞争者方法相比的回报率估计值的偏差,以及对回报值不确定性的现实估计。 STM-E从空间邻域中利用数据满足某些条件而不是来自单个位置的数据,以进行回报值估计。 本文对瓜德罗普(Guadeloupe)附近的加勒比海的热带气旋的STM-E模型进行了批判性评估,该模型提供了大型合成旋风的数据库,相当于3,000年以上的观察。结果表明,STM-E产生的SWH的500年回报值及其可变性的值,估计从200年的旋风数据数据中估计,这与通过从完整的合成旋风数据库中抽样500年的数据获得的直接经验估计值一致;发现了相似的结果,以估算与大约50年数据相对应的样品的100年回报值。通常,STM-E还提供了相对于单个位置分析的返回值的偏差和更现实的不确定性估计。
Occurrences of tropical cyclones at a location are rare, and for many locations, only short periods of observations or hindcasts are available. Hence, estimation of return values (corresponding to a period considerably longer than that for which data is available) for cyclone-induced significant wave height (SWH) from small samples is challenging. The STM-E (space-time maximum and exposure) model was developed to provide reduced bias in estimates of return values compared to competitor approaches in such situations, and realistic estimates of return value uncertainty. STM-E exploits data from a spatial neighbourhood satisfying certain conditions, rather than data from a single location, for return value estimation. This article provides critical assessment of the STM-E model for tropical cyclones in the Caribbean Sea near Guadeloupe for which a large database of synthetic cyclones is available, corresponding to more than 3,000 years of observation. Results indicate that STM-E yields values for the 500-year return value of SWH and its variability, estimated from 200 years of cyclone data, consistent with direct empirical estimates obtained by sampling 500 years of data from the full synthetic cyclone database; similar results were found for estimation of the 100-year return value from samples corresponding to approximately 50 years of data. In general, STM-E also provides reduced bias and more realistic uncertainty estimates for return values relative to single location analysis.