Preprints
https://doi.org/10.5194/egusphere-2025-630
https://doi.org/10.5194/egusphere-2025-630
27 Feb 2025
 | 27 Feb 2025
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Decoupling the PBL Height, the Mixing Layer Height, and the Aerosol Layer Top in LiDAR Measurements over Chiang Mai, Northern Thailand

Ronald Macatangay, Thiranan Sonkaew, Sherin Hassan Bran, Worapop Thongsame, Titaporn Supasri, Mana Panya, Jeerasak Longmali, Raman Solanki, Ben Svasti Thomson, and Achim Haug

Abstract. Accurate determination of the planetary boundary layer (PBL) height, mixing layer height (MLH), and aerosol layer top (ALT) is critical for air quality and climate studies, especially in regions with complex aerosol dynamics like Chiang Mai, northern Thailand. This study develops a novel LiDAR-based methodology that incorporates a temperature-based dynamic maximum analysis altitude (MAA) to decouple these layers, addressing the limitations of conventional methods such as the Haar Wavelet Covariance Transform (WCT). Traditional fixed-altitude approaches often misclassify the ALT as the PBL height, particularly during nighttime or transition periods, leading to significant overestimations. By dynamically adjusting the MAA based on surface temperature variations, the proposed approach effectively distinguishes the PBL from residual aerosol layers and cloud interference. Comparison against radiosonde data and WRF-Chem simulations demonstrates strong agreement, with LiDAR-derived PBL heights showing improved diurnal resolution and accuracy. However, model simulations tend to overestimate the PBL height during high aerosol events, highlighting the need for refined aerosol-radiation interaction parameterizations. This study underscores the importance of integrating thermodynamic and aerosol data for accurate boundary layer characterization and provides a robust framework for improving air quality and climate models in regions with high aerosol loading and complex topography. These findings have implications for enhancing pollutant transport analysis and advancing LiDAR-based remote sensing techniques in Southeast Asia.

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Ronald Macatangay, Thiranan Sonkaew, Sherin Hassan Bran, Worapop Thongsame, Titaporn Supasri, Mana Panya, Jeerasak Longmali, Raman Solanki, Ben Svasti Thomson, and Achim Haug

Status: open (until 17 Apr 2025)

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Ronald Macatangay, Thiranan Sonkaew, Sherin Hassan Bran, Worapop Thongsame, Titaporn Supasri, Mana Panya, Jeerasak Longmali, Raman Solanki, Ben Svasti Thomson, and Achim Haug
Ronald Macatangay, Thiranan Sonkaew, Sherin Hassan Bran, Worapop Thongsame, Titaporn Supasri, Mana Panya, Jeerasak Longmali, Raman Solanki, Ben Svasti Thomson, and Achim Haug

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Short summary
Air pollution affects climate and health, but accurately measuring how pollutants mix in the atmosphere remains challenging. In Chiang Mai, Thailand, we developed a new LiDAR-based method to better distinguish key atmospheric layers by incorporating temperature-based adjustments. This improves accuracy, especially at night, compared to traditional techniques. Our findings help refine air quality models and provide better data for tackling pollution in Southeast Asia.
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