Articles | Volume 24, issue 11
https://doi.org/10.5194/acp-24-6477-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-6477-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jun 2024
Research article |
| 04 Jun 2024
| 04 Jun 2024
Deep-learning-derived planetary boundary layer height from conventional meteorological measurements
Lawrence Livermore National Laboratory, Livermore, CA, USA
Yunyan Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
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Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
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Short summary
The planetary boundary layer is critical to our climate system. This study uses a deep learning approach to estimate the planetary boundary layer height (PBLH) from conventional meteorological measurements. By training data from comprehensive field observations, our model examines the influence of various meteorological factors on PBLH and demonstrates effectiveness across different scenarios, offering a reliable tool for understanding boundary layer dynamics.
The planetary boundary layer is critical to our climate system. This study uses a deep learning...
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