用贝叶斯方法分析风速数据，自动识别地中海气旋
A Statistical Learning Approach to Mediterranean Cyclones
- 用潜在狄利克雷分配算法处理风速数据
- 实现维度大幅降低，提升检测效率
- 适合气候研究与气象预警系统使用
地中海气旋是极端气象事件，相比热带海洋气旋了解较少。随着气候变化加剧，该地区受其影响日益严重，但精确刻画仍具挑战。本文展示如何利用贝叶斯算法（潜在狄利克雷分配）基于风速数据对地中海气旋进行分类，实现显著的维度压缩，从而可应用监督统计学习技术实现新气旋的检测与追踪。
原文摘要 · Abstract (English)
Mediterranean cyclones are extreme meteorological events of which much less is known compared to their tropical, oceanic counterparts. The raising interest in such phenomena is due to their impact on a region increasingly more affected by climate change, but a precise characterization remains a non trivial task. In this work we showcase how a Bayesian algorithm (Latent Dirichlet Allocation) can classify Mediterranean cyclones relying on wind velocity data, leading to a drastic dimensional reduction that allows the use of supervised statistical learning techniques for detecting and tracking new cyclones.
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