用轨迹感知的kNN检测罕见气象异常，高效且精确。
TRAKNN: Efficient Trajectory Aware Spatiotemporal kNN for Rare Meteorological Trajectory Detection
- 基于循环模式分离计算复杂度，实现长轨迹快速搜索
- 在75年欧洲气压数据上识别出与极端事件吻合的稀有轨迹
- 无需训练，可在普通工作站运行，适合气候研究者
极端天气事件如风灾和热浪由持续数日的大气环流模式驱动。传统研究多关注瞬时大气状态，但捕捉这些空间场的时间演变轨迹对表征罕见且影响深远的大气行为至关重要。然而，在跨几十年、大陆尺度的网格化数据上进行全量相似性搜索面临巨大计算与内存挑战。本文提出TRAKNN（TRajectory Aware KNN），一种完全无监督、数据无关的框架，用于在时空数据中以精确kNN方法检测几何稀疏的短轨迹。TRAKNN采用基于循环的算法，将计算复杂度与轨迹长度解耦，并结合高效的批处理操作，最大化计算强度。这些优化使在标准工作站（CPU或GPU）上进行全量分析成为可能。我们在75年每日欧洲海平面气压数据上评估该方法，结果表明TRAKNN识别出的稀有轨迹对应物理上连贯的大气异常，并与独立的极端事件数据库高度一致。
原文摘要 · Abstract (English)
Extreme weather events, such as windstorms and heatwaves, are driven by persistent atmospheric circulation patterns that evolve over several consecutive days. While traditional circulation-based studies often focus on instantaneous atmospheric states, capturing the temporal evolution, or trajectory, of these spatial fields is essential for characterizing rare and potentially impactful atmospheric behavior. However, performing an exhaustive similarity search on multi-decadal, continental-scale gridded datasets presents significant computational and memory challenges. In this paper, we propose TRAKNN (TRajectory Aware KNN), a fully unsupervised and data-agnostic framework for detecting geometrically rare short trajectories in spatio-temporal data with an exact kNN approach. TRAKNN leverages a recurrence-based algorithm that decouples computational complexity from trajectory length and efficient batch operations, maximizing computational intensity. These optimizations enable exhaustive analysis on standard workstations, either on CPU or on GPU. We evaluate our approach on 75 years of daily European sea-level pressure data. Our results illustrate that rare trajectories identified by TRAKNN correspond to physically coherent atmospheric anomalies and align with independent extreme-event databases.
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