标题： LASSE：非稳定气候状态下风暴潮极端事件的学习主动采样方法 显示英文标题

标题： LASSE: Learning Active Sampling for Storm Tide Extremes in Non-Stationary Climate Regimes

摘要： 为了进行风险评估（例如从用于气候研究的大尺度降尺度的风暴目录中识别产生破坏性风暴潮的热带气旋）往往难以实现，因为它需要大量的昂贵蒙特卡罗水动力学模拟。 在这里，我们展示了替代模型在准确性、召回率和精确度方面具有前景，并且它们能够“推广”到新的气候情景。 然后，我们提出了一种信息量丰富的在线学习方法，仅使用少量水动力学模拟即可快速搜索极端风暴潮产生的热带气旋。 从一组具有详细风暴潮水动力学模拟的最小热带气旋子集开始，替代模型选择信息量丰富的数据进行在线重新训练，并迭代地改进其对破坏性热带气旋的预测。 在广泛的降尺度热带气旋目录上的结果显示，使用不到 20% 的模拟作为训练，即可以 100% 的精确度检索出罕见的破坏性风暴。 信息量丰富的采样方法高效，可扩展到大型风暴目录，并能推广到气候情景中。 显示英文摘要

摘要： Identifying tropical cyclones that generate destructive storm tides for risk assessment, such as from large downscaled storm catalogs for climate studies, is often intractable because it entails many expensive Monte Carlo hydrodynamic simulations. Here, we show that surrogate models are promising from accuracy, recall, and precision perspectives, and they "generalize" to novel climate scenarios. We then present an informative online learning approach to rapidly search for extreme storm tide-producing cyclones using only a few hydrodynamic simulations. Starting from a minimal subset of TCs with detailed storm tide hydrodynamic simulations, a surrogate model selects informative data to retrain online and iteratively improves its predictions of damaging TCs. Results on an extensive catalog of downscaled TCs indicate 100% precision in retrieving rare destructive storms using less than 20% of the simulations as training. The informative sampling approach is efficient, scalable to large storm catalogs, and generalizable to climate scenarios.