Best estimate of the planetary boundary layer height from multiple remote sensing measurements
<p>Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML)...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
|
| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/18/3453/2025/amt-18-3453-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849730484713553920 |
|---|---|
| author | D. Zhang J. Comstock C. Sivaraman K. Mo R. Krishnamurthy J. Tian T. Su Z. Li N. Roldán-Henao |
| author_facet | D. Zhang J. Comstock C. Sivaraman K. Mo R. Krishnamurthy J. Tian T. Su Z. Li N. Roldán-Henao |
| author_sort | D. Zhang |
| collection | DOAJ |
| description | <p>Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.</p> |
| format | Article |
| id | doaj-art-064cd1817e1a400e88b4933da24f775d |
| institution | DOAJ |
| issn | 1867-1381 1867-8548 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj-art-064cd1817e1a400e88b4933da24f775d2025-08-20T03:08:51ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482025-07-01183453347510.5194/amt-18-3453-2025Best estimate of the planetary boundary layer height from multiple remote sensing measurementsD. Zhang0J. Comstock1C. Sivaraman2K. Mo3R. Krishnamurthy4J. Tian5T. Su6Z. Li7N. Roldán-Henao8Pacific Northwest National Laboratory, Richland, Washington, USAPacific Northwest National Laboratory, Richland, Washington, USAPacific Northwest National Laboratory, Richland, Washington, USAPacific Northwest National Laboratory, Richland, Washington, USAPacific Northwest National Laboratory, Richland, Washington, USAPacific Northwest National Laboratory, Richland, Washington, USALawrence Livermore National Laboratory, Livermore, CA, USADepartment of Atmospheric and Oceanic Sciences, University of Maryland, College Park, College Park, MD, USADepartment of Atmospheric and Oceanic Sciences, University of Maryland, College Park, College Park, MD, USA<p>Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.</p>https://amt.copernicus.org/articles/18/3453/2025/amt-18-3453-2025.pdf |
| spellingShingle | D. Zhang J. Comstock C. Sivaraman K. Mo R. Krishnamurthy J. Tian T. Su Z. Li N. Roldán-Henao Best estimate of the planetary boundary layer height from multiple remote sensing measurements Atmospheric Measurement Techniques |
| title | Best estimate of the planetary boundary layer height from multiple remote sensing measurements |
| title_full | Best estimate of the planetary boundary layer height from multiple remote sensing measurements |
| title_fullStr | Best estimate of the planetary boundary layer height from multiple remote sensing measurements |
| title_full_unstemmed | Best estimate of the planetary boundary layer height from multiple remote sensing measurements |
| title_short | Best estimate of the planetary boundary layer height from multiple remote sensing measurements |
| title_sort | best estimate of the planetary boundary layer height from multiple remote sensing measurements |
| url | https://amt.copernicus.org/articles/18/3453/2025/amt-18-3453-2025.pdf |
| work_keys_str_mv | AT dzhang bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements AT jcomstock bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements AT csivaraman bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements AT kmo bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements AT rkrishnamurthy bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements AT jtian bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements AT tsu bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements AT zli bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements AT nroldanhenao bestestimateoftheplanetaryboundarylayerheightfrommultipleremotesensingmeasurements |