TRAKNN: Efficient Trajectory Aware Spatiotemporal kNN for Rare Meteorological Trajectory Detection

2026-03-02

AI Summary

This study addresses the formidable computational and memory challenges of efficiently detecting rare atmospheric circulation trajectories in multi-decadal, continental-scale gridded meteorological data. The authors propose TRAKNN, a novel framework that introduces trajectory-aware mechanisms into k-nearest neighbor search for the first time, enabling fully unsupervised and data-agnostic identification of geometrically rare short trajectories in spatiotemporal data. By leveraging recursive algorithms and efficient batching, TRAKNN decouples computational complexity from trajectory length and is compatible with both CPU and GPU architectures, substantially improving scalability. Experiments on 75 years of European sea-level pressure data demonstrate that TRAKNN successfully identifies rare atmospheric trajectories consistent with known physical anomalies and corroborated by independent extreme event databases, all while enabling full-scale analysis on a standard workstation.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• trajectory detection
• spatiotemporal kNN
• rare meteorological events
• atmospheric circulation
• similarity search

Innovation

• trajectory-aware
• spatiotemporal kNN
• rare event detection
• recurrence-based algorithm
• computational efficiency