Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter Profiles
The planetary boundary layer height (PBLH) is the atmospheric region closest to the earth’s surface and has important implications on weather forecasting, air quality, and climate research. However, lidar-based methods traditionally used to determine PBLH—such as the ideal profile fitting method (IP...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/23/4006 |
| _version_ | 1827700606222991360 |
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| author | Lin Du Ya’ni Pan Wei Wang |
| author_facet | Lin Du Ya’ni Pan Wei Wang |
| author_sort | Lin Du |
| collection | DOAJ |
| description | The planetary boundary layer height (PBLH) is the atmospheric region closest to the earth’s surface and has important implications on weather forecasting, air quality, and climate research. However, lidar-based methods traditionally used to determine PBLH—such as the ideal profile fitting method (IPF), maximum gradient method, and wavelet covariance transform—are not only heavily influenced by cloud layers, but also rely heavily on a low signal-to-noise ratio (SNR). Therefore, a random sample fitting (RANSAF) method was proposed for PBLH detection based on combining the random sampling consensus and IPF methods. According to radiosonde measurements, the testing of simulated and satellite-based signals shows that the proposed RANSAF method can reduce the effects of the cloud layer and significantly fluctuating noise on lidar-based PBLH detection better than traditional algorithms. The low PBLH bias derived by the RANSAF method indicates that the improved algorithm has a superior performance in measuring PBLH under a low SNR or when a cloud layer exists where the traditional methods are mostly ineffective. The RANSAF method has the potential to determine regional PBLH on the basis of satellite-based lidar backscatter profiles. |
| first_indexed | 2024-03-10T14:15:24Z |
| format | Article |
| id | doaj.art-3eed5db97c844a1793722a809a735c56 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:15:24Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3eed5db97c844a1793722a809a735c562023-11-20T23:47:30ZengMDPI AGRemote Sensing2072-42922020-12-011223400610.3390/rs12234006Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter ProfilesLin Du0Ya’ni Pan1Wei Wang2Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Geoscience and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geoscience and Info-Physics, Central South University, Changsha 410083, ChinaThe planetary boundary layer height (PBLH) is the atmospheric region closest to the earth’s surface and has important implications on weather forecasting, air quality, and climate research. However, lidar-based methods traditionally used to determine PBLH—such as the ideal profile fitting method (IPF), maximum gradient method, and wavelet covariance transform—are not only heavily influenced by cloud layers, but also rely heavily on a low signal-to-noise ratio (SNR). Therefore, a random sample fitting (RANSAF) method was proposed for PBLH detection based on combining the random sampling consensus and IPF methods. According to radiosonde measurements, the testing of simulated and satellite-based signals shows that the proposed RANSAF method can reduce the effects of the cloud layer and significantly fluctuating noise on lidar-based PBLH detection better than traditional algorithms. The low PBLH bias derived by the RANSAF method indicates that the improved algorithm has a superior performance in measuring PBLH under a low SNR or when a cloud layer exists where the traditional methods are mostly ineffective. The RANSAF method has the potential to determine regional PBLH on the basis of satellite-based lidar backscatter profiles.https://www.mdpi.com/2072-4292/12/23/4006boundary layer heightatmospherelidarrandom sample fittingaerosol |
| spellingShingle | Lin Du Ya’ni Pan Wei Wang Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter Profiles Remote Sensing boundary layer height atmosphere lidar random sample fitting aerosol |
| title | Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter Profiles |
| title_full | Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter Profiles |
| title_fullStr | Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter Profiles |
| title_full_unstemmed | Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter Profiles |
| title_short | Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter Profiles |
| title_sort | random sample fitting method to determine the planetary boundary layer height using satellite based lidar backscatter profiles |
| topic | boundary layer height atmosphere lidar random sample fitting aerosol |
| url | https://www.mdpi.com/2072-4292/12/23/4006 |
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