Characterizing Seasonal Variation of the Atmospheric Mixing Layer Height Using Machine Learning Approaches

Details

• Journal Title:
  Remote Sensing
• Personal Author:
  Chu, Yufei ; Lin, Guo ; Deng, Min ; Guo, Hanqing ; Zhang, Jun A.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies)
• Description:
  As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. To examine how these factors vary across seasons, a seasonal analysis is necessary. However, dividing data by season reduces the sample size, which can affect result reliability and complicate factor comparisons. To address these challenges, this study replaces default parameters with grid search optimization and incorporates cross-validation to mitigate dataset limitations. Using XGBoost with four years of data from the atmospheric radiation measurement (ARM) (Southern Great Plains (SGP) C1 site, cross-validation stabilizes correlation coefficient fluctuations from 0.3 to within 0.1. With optimized parameters, the R value can reach 0.81. Analysis of the C1 site reveals that the relative importance of different factors changes across seasons. Lower tropospheric stability (LTS, ~0.53) is the dominant factor at C1 throughout the year. However, during DJF, latent heat flux (LHF, 0.44) surpasses LTS (0.22). In SON, LTS (0.58) becomes more influential than LHF (0.18). Further comparisons among the four long-term SGP sites (C1, E32, E37, and E39) show seasonal variations in relative importance. Notably, during JJA, the differences in the relative importance of the three factors across all sites are lower than in other seasons. This suggests that boundary layer development in the summer is not dominated by a single factor, reflecting a more intricate process likely influenced by seasonal conditions such as enhanced convective activity, higher temperatures, and humidity, which collectively contribute to a balanced distribution of parameter impacts. Furthermore, the relative importance of LTS gradually increases from morning to noon, indicating that LTS becomes more significant as the boundary layer approaches its maximum height. Consequently, the LTS in the early morning in autumn exhibits greater relative importance compared to other seasons. This reflects a faster development of the mixing layer height (MLH) in autumn, suggesting that it is easier to retrieve the MLH from the previous day during this period. The findings enhance understanding of boundary layer evolution and contribute to improved boundary layer parameterization.
• Keywords:
  Boundary Layer (Meteorology) Heat Flux Machine Learning Boundary Layer (Meteorology) Heat Flux Machine Learning Cross-validation Latent Heat Flux Lidar Lower Tropospheric Stability Mixing Layer Height Sensible Heat Flux SHAP XGBoost

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• Source:
  Remote Sens. 2025, 17(8), 1399
• DOI:
  https://doi.org/10.3390/rs17081399
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant no. NA22OAR4050669D ; Grant no. NA22OAR4590174 ; Grant no. NA22OAR4590178
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:917267414f68f3394976ed158d428549fc3dea6fc1ee3011c7f89add1a730193100b5a92437c88d3aa3c986d7a62fe859318f238a7882badfc87645540811824
• Download URL:
  https://repository.library.noaa.gov/view/noaa/70618/noaa_70618_DS1.pdf
• File Type:
  [PDF - 2.79 MB ]