A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data
Abstract
Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7.more »
- Authors:
-
[1];
- National Univ. of Defense Technology, Changsha (China)
- PLA, Sanya (China). Unit No. 91922
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1783082
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Geoscience and Remote Sensing Letters
- Additional Journal Information:
- Journal Volume: 19; Journal ID: ISSN 1545-598X
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; clouds; support vector machines; estimation; thermodynamics; atmospheric measurements; extraterrestrial measurements; training; atmospheric emitted radiance interferometer (AERI); diurnal cycles; planetary boundary layer height (PBLH); seasonal cycles; support vector machine (SVM)
Citation Formats
Ye, Jin, Liu, Lei, Wang, Qi, Hu, Shuai, and Li, Shulei. A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data. United States: N. p., 2021.
Web. doi:10.1109/lgrs.2021.3073048.
Ye, Jin, Liu, Lei, Wang, Qi, Hu, Shuai, & Li, Shulei. A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data. United States. https://doi.org/10.1109/lgrs.2021.3073048
Ye, Jin, Liu, Lei, Wang, Qi, Hu, Shuai, and Li, Shulei. Mon .
"A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data". United States. https://doi.org/10.1109/lgrs.2021.3073048. https://www.osti.gov/servlets/purl/1783082.
@article{osti_1783082,
title = {A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data},
author = {Ye, Jin and Liu, Lei and Wang, Qi and Hu, Shuai and Li, Shulei},
abstractNote = {Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.},
doi = {10.1109/lgrs.2021.3073048},
journal = {IEEE Geoscience and Remote Sensing Letters},
number = ,
volume = 19,
place = {United States},
year = {Mon Apr 26 00:00:00 EDT 2021},
month = {Mon Apr 26 00:00:00 EDT 2021}
}
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