Articles | Volume 12, issue 3
https://doi.org/10.5194/amt-12-1841-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-1841-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Mar 2019
Research article |
| 20 Mar 2019
| 20 Mar 2019
Cloud base height retrieval from multi-angle satellite data
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Odran Sourdeval
Institute of Meteorology, University of Leipzig, Leipzig, Germany
Laboratoire d'Optique Atmosphérique, Université de Lille, Villeneuve-d'Ascq, France
Johannes Mülmenstädt
Institute of Meteorology, University of Leipzig, Leipzig, Germany
Johannes Quaas
Institute of Meteorology, University of Leipzig, Leipzig, Germany
Susanne Crewell
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
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16 citations as recorded by crossref.
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- Constraining the Twomey effect from satellite observations: issues and perspectives J. Quaas et al. 10.5194/acp-20-15079-2020
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- Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing D. Rosenfeld et al. 10.1029/2022RG000799
- Global characteristics of cloud macro-physical properties from active satellite remote sensing Y. Chi et al. 10.1016/j.atmosres.2024.107316
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- Variation in δ15N of fog-dependent Tillandsia ecosystems reflect water availability across climate gradients in the hyperarid Atacama Desert A. Jaeschke et al. 10.1016/j.gloplacha.2019.103029
- Estimate of daytime single-layer cloud base height from advanced baseline imager measurements H. Lin et al. 10.1016/j.rse.2022.112970
- Marine cloud base height retrieval from MODIS cloud properties using machine learning J. Lenhardt et al. 10.5194/amt-17-5655-2024
- Obtaining Cloud Base Height and Phase From Thermal Infrared Radiometry Using a Deep Learning Algorithm Q. Wang et al. 10.1109/TGRS.2023.3317532
- Addressing the difficulties in quantifying droplet number response to aerosol from satellite observations H. Jia et al. 10.5194/acp-22-7353-2022
- Satellite retrieval of cloud base height and geometric thickness of low-level cloud based on CALIPSO X. Lu et al. 10.5194/acp-21-11979-2021
- Cloud base height retrieval from multi-angle satellite data C. Böhm et al. 10.5194/amt-12-1841-2019
- Using CALIOP to estimate cloud-field base height and its uncertainty: the Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset J. Mülmenstädt et al. 10.5194/essd-10-2279-2018
14 citations as recorded by crossref.
- Long‐term variability of the low‐level cloud base height in Poland D. Matuszko et al. 10.1002/joc.8041
- Constraining the Twomey effect from satellite observations: issues and perspectives J. Quaas et al. 10.5194/acp-20-15079-2020
- Water vapor variability in the Atacama Desert during the 20th century C. Böhm et al. 10.1016/j.gloplacha.2020.103192
- Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing D. Rosenfeld et al. 10.1029/2022RG000799
- Global characteristics of cloud macro-physical properties from active satellite remote sensing Y. Chi et al. 10.1016/j.atmosres.2024.107316
- Accounting for 3D radiative effects in MODIS aerosol retrievals near clouds using CALIPSO observations G. Wen et al. 10.3389/frsen.2023.1333814
- A new classification of satellite-derived liquid water cloud regimes at cloud scale C. Unglaub et al. 10.5194/acp-20-2407-2020
- CLANN: Cloud amount neural network for estimating 3D cloud from geostationary satellite imager H. Lin et al. 10.1016/j.rse.2025.114600
- Variation in δ15N of fog-dependent Tillandsia ecosystems reflect water availability across climate gradients in the hyperarid Atacama Desert A. Jaeschke et al. 10.1016/j.gloplacha.2019.103029
- Estimate of daytime single-layer cloud base height from advanced baseline imager measurements H. Lin et al. 10.1016/j.rse.2022.112970
- Marine cloud base height retrieval from MODIS cloud properties using machine learning J. Lenhardt et al. 10.5194/amt-17-5655-2024
- Obtaining Cloud Base Height and Phase From Thermal Infrared Radiometry Using a Deep Learning Algorithm Q. Wang et al. 10.1109/TGRS.2023.3317532
- Addressing the difficulties in quantifying droplet number response to aerosol from satellite observations H. Jia et al. 10.5194/acp-22-7353-2022
- Satellite retrieval of cloud base height and geometric thickness of low-level cloud based on CALIPSO X. Lu et al. 10.5194/acp-21-11979-2021
2 citations as recorded by crossref.
Latest update: 18 Apr 2025
Short summary
The cloud base height (CBH) is important for air traffic, for describing the energy budget of the Earth and for other applications. Ground-based CBH measurements are only available for individual sites and mostly limited to land. Satellites are a powerful tool for global coverage. While the cloud top height is derived operationally, the derivation of CBH from space is more difficult as the clouds hide their base. Here, we present a method to retrieve the CBH from multi-angle satellite data.
The cloud base height (CBH) is important for air traffic, for describing the energy budget of...
