标题： LASSE：非平稳气候制度中风暴潮极端事件的主动采样学习 显示英文标题

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

摘要： 识别生成破坏性风暴潮的热带气旋以进行风险评估，例如从大规模降尺度风暴目录中进行气候研究，通常难以处理，因为它需要许多昂贵的蒙特卡洛水动力模拟。 在这里，我们表明从准确性、召回率和精确度的角度来看，代理模型是很有前景的，并且它们“泛化”到新的气候情景。 然后，我们提出了一种信息丰富的在线学习方法，仅使用少量水动力模拟快速搜索产生极端风暴潮的气旋。 从一组具有详细风暴潮水动力模拟的最小热带气旋开始，代理模型选择信息丰富的数据进行在线重新训练，并迭代地提高其对破坏性热带气旋的预测能力。 在广泛的降尺度热带气旋目录上的结果表明，使用少于20%的模拟作为训练，可以100%的精确度检索出稀有的破坏性风暴。 信息采样方法高效，可扩展到大型风暴目录，并可泛化到不同的气候情景。 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 a 100% precision retrieving the rare destructive storms training 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.