Toward instrument combination for boundary layer classification

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 during wintertime in the Arve River valley near Chamonix–Mont-Blanc during the Passy-2015 field experiment. The ABL classification obtained from microwave radiometer and ceilometer observations (ground-based remote sensors [GBReS]) combination is compared with high-frequency radiosoundings (RS) data and the French convective scale AROME model outputs. Classifications from RS and GBReS broadly agree, demonstrating the good behavior of the method, AROME leading to different results at night. The difference of AROME is likely due to the different nature of the data (model fields are smoother and include forecasting errors). The results show the ability of unsupervised classification to segment relevant objects in the boundary layer and the benefit to use a combination of GBReS.