回溯极端事件：在最终状态条件下初始化生成1000成员集合
Extremes on Rewind: Generating 1,000-Member Ensembles Initialized at a Final Condition
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中文总结 AI 辅助
本研究使用非自回归基础模型cBottle-video生成1000成员集合，针对三个极端事件验证了终点约束集合的多样性与有效性，为罕见高影响事件的情景规划提供了高效方案。
中文摘要 AI 辅助
针对罕见高影响事件的情景规划常需大规模集合来随机采样相关轨迹。尽管自回归天气模拟器可高效生成此类集合，但分离目标轨迹需筛选PB级数据，该挑战随预报时效和事件罕见度呈指数增长。相比之下，像Climate in a Bottle视频（cBottle-video）这类非自回归基础模型可直接采样终止于极端事件的轨迹，避免大规模集合搜索。我们使用cBottle-video生成1000成员集合，对2021年太平洋西北部（PNW）热浪、桑迪飓风、伊恩飓风这三个极端事件进行起始和/或终点条件约束。终点约束集合自由端的前期500 hPa位势高度（$z_{500}$）离散度达到起始约束集合最终状态离散度的84%至89%，显示出与各极端事件一致的显著多样性。对于2021年PNW热浪，终点约束集合成员初始温度均高于再分析数据且持续偏暖，以持续前期热取代观测到的快速增强。对于桑迪飓风，终点约束集合前期端$z_{500}$的主导模态解释了路径纬度方差的44%，约10%的集合成员初始为比桑迪更强的飓风。对于伊恩飓风，终点约束轨迹中首次登陆位置的变化凸显了风险规划中考虑中间灾害暴露的重要性。
英文摘要
Scenario planning for rare, high-impact events often requires massive ensembles to stochastically sample relevant trajectories. Although autoregressive weather emulators can efficiently generate such ensembles, isolating trajectories of interest requires sifting through petabytes of data, a challenge that grows exponentially with lead time and rarity. In contrast, a non-autoregressive foundation model like Climate in a Bottle video (cBottle-video) can directly sample trajectories terminating in extremes, avoiding large-ensemble search. We use cBottle-video to generate 1000-member ensembles with start- and/or end-conditioning across three extreme events---the 2021 Pacific Northwest (PNW) heatwave, Superstorm Sandy, and Hurricane Ian. Antecedent 500 hPa geopotential height ($z_{500}$) spread at the free end of end-conditioned ensembles reaches 84--89\% of the final-state spread of start-conditioned ensembles, revealing substantial diversity consistent with each extreme event. For the 2021 PNW heatwave, end-conditioned ensemble members begin uniformly warmer than reanalysis and stay warm, replacing the observed rapid intensification with persistent antecedent heat. For Superstorm Sandy, the leading modes of $z_{500}$ at the antecedent end of the end-conditioned ensemble explain 44\% of the variance in track latitude, and roughly 10\% of ensemble members begin as stronger hurricanes than Sandy. For Hurricane Ian, variation in the first landfall location among end-conditioned trajectories underscores the importance of accounting for intermediate hazard exposure in risk planning.
