Application of similarity theory associate with eddy covariance technique for atmospheric stability classification in Thailand
Keywords:
Atmospheric Stability Classification, Monin Obukhov Similarity Theory (MOST), Eddy Covariance TechniqueAbstract
Atmospheric stability classification is essential for determining pollution accumulation tendencies in geographical areas. While the KU Tower monitoring station at Kasetsart University, Bangkok, can classify atmospheric stability via multiple methods, these classifications are impractical for general meteorological stations due to specialized equipment requirements. This study proposes before use of Monin-Obukhov Similarity Theory (MOST) as a solution, which requires only basic meteorological parameters. To validate this approach, we first compared three conventional methods at KU Tower from 2016-2023 (44,814 hourly measurements): The temperature Gradient (Delta-T), Richardson Number (Ri), and Monin-Obukhov (MO) using IRGASON equipment, against Solar Radiation Delta-T (SRDT) as reference. The MO Method showed the best agreement with SRDT (NMSE = 0.301). Building on this finding, we implemented MOST using basic meteorological data (wind speed, temperature and cloud cover) and geographical surface parameters (roughness length, albedo and Bowen ratio) from Google Earth Engine. A comparison between MOST and the validated MO Method reveals moderately similar (NMSE = 0.238), confirming the effectiveness of MOST for classifying atmospheric stability via widely available meteorological data. This method can support air quality management in Thailand through applications in land use planning, industrial zone designation, and area-specific emission regulations.
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