A REVIEW OF MACHINE LEARNING METHODS TO MONITOR HEIGHT OF THE ATMOSPHERIC BOUNDARY LAYER

The area in air next to the Earth's place when heat, moisture, and trace elements are reduced is known as the atmospheric boundary layer (ABL). Despite being. significant for a number of applications, quantitative information regarding the spatial and temporal shifts inside the width of the ABL with its sub-layers are quite lacking. The latest advances in algorithm development and ground- based ML technologies make it possible to constantly profile the whole ABL vertical level with excellent vertical as well as temporal resolution. This paper provides an overview of the several retrieval techniques generated through the identification of ABL sublayer peak from various atmospheric parameters, as well as an overview of the strengths and weaknesses of numerous sensor kinds used for ABL measuring. The effective combination of methods for monitoring the ABL's diurnal evolution is described, with a particular emphasis on areas where instrumental or methodological synergy is seen to be specifically promising. The review focusses the importance of standardizing data collection across organized SN and customized data processing in producing high-quality goods that are necessary for capturing the temporal as well as spatial complication of the lowest levels in the atmosphere where humans breathe.