Synoptic Weather Regime Classifications for the whole year, from 2014 to 2015

The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).

近年来，天气型分类已成为ARM站点的一类高需求产品。该类天气型分类已在多项研究与主题中得到应用，涵盖气溶胶-云相互作用、陆面-大气相互作用以及云辐射效应等方向。本VAP采用无监督机器学习方法——自组织映射（Self-organizing map, SOM），依托ERA5再分析数据，对AMF外场试验与固定站点的每日天气型开展分类。该方法的核心思路主要基于Wang等人2022年发表于JGR-A的TRACER相关研究。