Abstract:To handle the complexity of the atmospheric boundary layer (ABL) and make accurate feature detection (top height, low-level jets, inversions, etc.), a prior necessary step is to identify the type of boundary layer. This study proposes a new method to identify the boundary layer type through unsupervised classification and the synergistic use of ground-based remote sensing. Unsupervised classification is used to lighten the human supervision. The new classification was applied to a 1-day case study collected du… Show more
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