Articles | Volume 21, issue 15
https://doi.org/10.5194/acp-21-11979-2021
© Author(s) 2021. 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-21-11979-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Aug 2021
Research article |
| 10 Aug 2021
| 10 Aug 2021
Satellite retrieval of cloud base height and geometric thickness of low-level cloud based on CALIPSO
Xin Lu
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China
Feiyue Mao
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan, 430079, China
Collaborative Innovation Center for Geospatial Technology, Wuhan,
430079, China
Daniel Rosenfeld
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China
Institute of Earth Sciences, The Hebrew University of Jerusalem,
Jerusalem, 91904, Israel
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences and Institute for Climate and Global Change Research,
Nanjing University, Nanjing, 210023, China
Zengxin Pan
Institute of Earth Sciences, The Hebrew University of Jerusalem,
Jerusalem, 91904, Israel
Wei Gong
Electronic Information School, Wuhan University, Wuhan, 430072, China
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Cited
13 citations as recorded by crossref.
- Marine cloud base height retrieval from MODIS cloud properties using machine learning J. Lenhardt et al. 10.5194/amt-17-5655-2024
- Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height M. Wang et al. 10.5194/acp-24-14239-2024
- Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing D. Rosenfeld et al. 10.1029/2022RG000799
- Realistic representation of mixed-phase clouds increases projected climate warming S. Hofer et al. 10.1038/s43247-024-01524-2
- Low‐Level Marine Tropical Clouds in Six CMIP6 Models Are Too Few, Too Bright but Also Too Compact and Too Homogeneous D. Konsta et al. 10.1029/2021GL097593
- The Temperature Control of Cloud Adiabatic Fraction and Coverage X. Lu et al. 10.1029/2023GL105831
- Improving the Treatment of Subgrid Cloud Variability in Warm Rain Simulation in CESM2 H. Wang et al. 10.1029/2022MS003103
- ARMTRAJ: a set of multipurpose trajectory datasets augmenting the Atmospheric Radiation Measurement (ARM) user facility measurements I. Silber et al. 10.5194/essd-17-29-2025
- Global characteristics of cloud macro-physical properties from active satellite remote sensing Y. Chi et al. 10.1016/j.atmosres.2024.107316
- Effects of smoke on marine low clouds and radiation during 2020 western United States wildfires L. Dong et al. 10.1016/j.atmosres.2024.107295
- Obtaining Cloud Base Height and Phase From Thermal Infrared Radiometry Using a Deep Learning Algorithm Q. Wang et al. 10.1109/TGRS.2023.3317532
- Cloud-Base Height Retrieval from MODIS Satellite Data Based on Self-Organizing Neural Networks A. Skorokhodov et al. 10.1134/S1024856023060209
- Evaluating satellite-based precipitation products for spatiotemporal drought analysis H. Khan et al. 10.1016/j.jaridenv.2024.105225
13 citations as recorded by crossref.
- Marine cloud base height retrieval from MODIS cloud properties using machine learning J. Lenhardt et al. 10.5194/amt-17-5655-2024
- Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height M. Wang et al. 10.5194/acp-24-14239-2024
- Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing D. Rosenfeld et al. 10.1029/2022RG000799
- Realistic representation of mixed-phase clouds increases projected climate warming S. Hofer et al. 10.1038/s43247-024-01524-2
- Low‐Level Marine Tropical Clouds in Six CMIP6 Models Are Too Few, Too Bright but Also Too Compact and Too Homogeneous D. Konsta et al. 10.1029/2021GL097593
- The Temperature Control of Cloud Adiabatic Fraction and Coverage X. Lu et al. 10.1029/2023GL105831
- Improving the Treatment of Subgrid Cloud Variability in Warm Rain Simulation in CESM2 H. Wang et al. 10.1029/2022MS003103
- ARMTRAJ: a set of multipurpose trajectory datasets augmenting the Atmospheric Radiation Measurement (ARM) user facility measurements I. Silber et al. 10.5194/essd-17-29-2025
- Global characteristics of cloud macro-physical properties from active satellite remote sensing Y. Chi et al. 10.1016/j.atmosres.2024.107316
- Effects of smoke on marine low clouds and radiation during 2020 western United States wildfires L. Dong et al. 10.1016/j.atmosres.2024.107295
- Obtaining Cloud Base Height and Phase From Thermal Infrared Radiometry Using a Deep Learning Algorithm Q. Wang et al. 10.1109/TGRS.2023.3317532
- Cloud-Base Height Retrieval from MODIS Satellite Data Based on Self-Organizing Neural Networks A. Skorokhodov et al. 10.1134/S1024856023060209
- Evaluating satellite-based precipitation products for spatiotemporal drought analysis H. Khan et al. 10.1016/j.jaridenv.2024.105225
Latest update: 18 Jan 2025
Short summary
In this paper, a novel method for retrieving cloud base height and geometric thickness is developed and applied to produce a global climatology of boundary layer clouds with a high accuracy. The retrieval is based on the 333 m resolution low-level cloud distribution as obtained from the CALIPSO lidar data. The main part of the study describes the variability of cloud vertical geometrical properties in space, season, and time of the day. Resultant new insights are presented.
In this paper, a novel method for retrieving cloud base height and geometric thickness is...
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