Articles | Volume 13, issue 8
https://doi.org/10.5194/amt-13-4539-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-13-4539-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2020
Research article |
| 25 Aug 2020
| 25 Aug 2020
CALIOP V4 cloud thermodynamic phase assignment and the impact of near-nadir viewing angles
NASA Langley Research Center, Atmospheric Composition Branch, Hampton,
VA 23681, USA
Robert A. Ryan
Science Systems Applications Inc., 1 Enterprise Pkwy, Hampton, VA 23666, USA
Brian J. Getzewich
Science Systems Applications Inc., 1 Enterprise Pkwy, Hampton, VA 23666, USA
Mark A. Vaughan
NASA Langley Research Center, Atmospheric Composition Branch, Hampton,
VA 23681, USA
David M. Winker
NASA Langley Research Center, Atmospheric Composition Branch, Hampton,
VA 23681, USA
Yongxiang Hu
NASA Langley Research Center, Atmospheric Composition Branch, Hampton,
VA 23681, USA
Anne Garnier
Science Systems Applications Inc., 1 Enterprise Pkwy, Hampton, VA 23666, USA
Jacques Pelon
Laboratoire Atmosphères, Milieux, Observations Spatiales,
UPMC-UVSQ-CNRS, Paris, France
Carolus A. Verhappen
Science Systems Applications Inc., 1 Enterprise Pkwy, Hampton, VA 23666, USA
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Cited
26 citations as recorded by crossref.
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- Determining AHI Cloud-Top Phase and Intercomparisons With MODIS Products Over North Pacific X. Zhuge et al. 10.1109/TGRS.2020.2990955
- Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans A. Garnier et al. 10.5194/amt-14-3277-2021
- Hemispheric and Seasonal Contrast in Cloud Thermodynamic Phase From A‐Train Spaceborne Instruments D. Villanueva et al. 10.1029/2020JD034322
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- Horizontally oriented ice crystals observed by the synergy of zenith- and slant-pointed polarization lidar over Wuhan (30.5°N, 114.4°E), China Y. He et al. 10.1016/j.jqsrt.2021.107626
- Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning S. Zeng et al. 10.3390/atmos12010010
- The Spatial Heterogeneity of Cloud Phase Observed by Satellite A. Sokol & T. Storelvmo 10.1029/2023JD039751
- Lessons Learned from the Updated GEWEX Cloud Assessment Database C. Stubenrauch et al. 10.1007/s10712-024-09824-0
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- Cloud–Aerosol Classification Based on the U-Net Model and Automatic Denoising CALIOP Data X. Zhou et al. 10.3390/rs16050904
- A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements D. Winker et al. 10.5194/essd-16-2831-2024
- Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithms A. Garnier et al. 10.5194/amt-14-3253-2021
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- Improvement in cloud retrievals from VIIRS through the use of infrared absorption channels constructed from VIIRS+CrIS data fusion Y. Li et al. 10.5194/amt-13-4035-2020
25 citations as recorded by crossref.
- Gaussian Mixture Model-Based Cloud- Phase Estimation From GEO- KOMPSAT-2A Observations D. Kim & D. Shin 10.1109/TGRS.2024.3383888
- Determining AHI Cloud-Top Phase and Intercomparisons With MODIS Products Over North Pacific X. Zhuge et al. 10.1109/TGRS.2020.2990955
- Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans A. Garnier et al. 10.5194/amt-14-3277-2021
- Hemispheric and Seasonal Contrast in Cloud Thermodynamic Phase From A‐Train Spaceborne Instruments D. Villanueva et al. 10.1029/2020JD034322
- Fengyun-3D/MERSI-II Cloud Thermodynamic Phase Determination Using a Machine-Learning Approach D. Zhao et al. 10.3390/rs13122251
- Climatology and trends of cirrus geometrical and optical properties in the Amazon region from 7-yr of CALIPSO observations B. Portella & H. Barbosa 10.1016/j.atmosres.2023.107167
- A novel method of identifying and analysing oil smoke plumes based on MODIS and CALIPSO satellite data A. Mereuţă et al. 10.5194/acp-22-5071-2022
- Assessing the benefits of Imaging Infrared Radiometer observations for the CALIOP version 4 cloud and aerosol discrimination algorithm T. Vaillant de Guélis et al. 10.5194/amt-15-1931-2022
- Detecting the Phase of Marine Boundary Layer Clouds: Some Implications for Cloud Albedo N. Rampal & R. Davies 10.1029/2022JD037496
- Horizontally oriented ice crystals observed by the synergy of zenith- and slant-pointed polarization lidar over Wuhan (30.5°N, 114.4°E), China Y. He et al. 10.1016/j.jqsrt.2021.107626
- Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning S. Zeng et al. 10.3390/atmos12010010
- The Spatial Heterogeneity of Cloud Phase Observed by Satellite A. Sokol & T. Storelvmo 10.1029/2023JD039751
- Lessons Learned from the Updated GEWEX Cloud Assessment Database C. Stubenrauch et al. 10.1007/s10712-024-09824-0
- Global aerosol vertical structure analysis by clustering gridded CALIOP aerosol profiles with fuzzy k-means L. Wang et al. 10.1016/j.scitotenv.2020.144076
- Wildfire smoke, Arctic haze, and aerosol effects on mixed-phase and cirrus clouds over the North Pole region during MOSAiC: an introduction R. Engelmann et al. 10.5194/acp-21-13397-2021
- Joint multiscale cloud detection algorithm for ground-based lidar W. Xu et al. 10.1364/OE.473727
- Ice-nucleating particles in northern Greenland: annual cycles, biological contribution and parameterizations K. Sze et al. 10.5194/acp-23-4741-2023
- Sensitivity of Mixed-Phase Cloud Optical Properties to Cloud Particle Model and Microphysical Factors at Wavelengths from 0.2 to 100 µm Q. Luo et al. 10.3390/rs13122330
- Statistical Analysis for Parameters of Specularly Reflective Layers in High-Level Clouds over Western Siberia Based on MODIS Data A. Skorokhodov & A. Konoshonkin 10.1134/S1024856023010153
- An extended lidar-based cirrus cloud retrieval scheme: first application over an Arctic site K. Nakoudi et al. 10.1364/OE.414770
- Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product G. van Zadelhoff et al. 10.5194/amt-16-3631-2023
- Cloud–Aerosol Classification Based on the U-Net Model and Automatic Denoising CALIOP Data X. Zhou et al. 10.3390/rs16050904
- A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements D. Winker et al. 10.5194/essd-16-2831-2024
- Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithms A. Garnier et al. 10.5194/amt-14-3253-2021
- Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research X. Zhuge et al. 10.5194/essd-16-1747-2024
Latest update: 03 Nov 2024
Short summary
CALIOP data users will find more cloud layers detected in V4, with edges that extend further than in V3, for an increase in total atmospheric cloud volume of 6 %–9 % for high-confidence cloud phases and 1 %–2 % for all cloudy bins, including cloud fringes and unknown cloud phases. In V4 there are many fewer cloud layers identified as horizontally oriented ice, particularly in the 3° off-nadir view. Depolarization at 532 nm is the predominant parameter determining cloud thermodynamic phase.
CALIOP data users will find more cloud layers detected in V4, with edges that extend further...
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