Articles | Volume 11, issue 4
https://doi.org/10.5194/essd-11-1745-2019
© 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/essd-11-1745-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
25 Nov 2019
Data description article |
| 25 Nov 2019
| 25 Nov 2019
The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)
Columbia University, Center for Climate Systems Research, Earth
Institute, New York, NY, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Anthony D. Del Genio
NASA Goddard Institute for Space Studies, New York, NY, USA
Hélène Chepfer
LMD/IPSL, Sorbonne Université, Paris, France
LMD/IPSL, CNRS, École Polytechnique, Palaiseau, France
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- Remote sensing of planetary boundary layer from ground and space: Structure, thermodynamics, and boundary layer clouds T. Su et al. https://doi.org/10.1016/j.rse.2026.115544
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- Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 2. Focus on Marine Low Level Clouds E. Höjgård‐Olsen et al. https://doi.org/10.1029/2021JD035402
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- Observational constraint on cloud feedbacks suggests moderate climate sensitivity G. Cesana & A. Del Genio https://doi.org/10.1038/s41558-020-00970-y
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- Patterns and Mechanisms of Northeast Pacific Temperature Response to Pliocene Boundary Conditions P. Brennan et al. https://doi.org/10.1029/2021PA004370
25 citations as recorded by crossref.
- Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product A. Feofilov et al. https://doi.org/10.5194/amt-16-3363-2023
- Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity T. Myers et al. https://doi.org/10.1038/s41558-021-01039-0
- Assessing effective radiative forcing from aerosol–cloud interactions over the global ocean C. Wall et al. https://doi.org/10.1073/pnas.2210481119
- Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site J. Tian et al. https://doi.org/10.3390/rs13122309
- Top-of-Atmosphere Albedo Bias from Neglecting Three-Dimensional Cloud Radiative Effects C. Singer et al. https://doi.org/10.1175/JAS-D-21-0032.1
- Global Low Clouds Evolution and Their Meteorological Drivers Across Multiple Timescales Y. Li et al. https://doi.org/10.3390/rs17244045
- Demonstrating Aeolus capability to observe wind-cloud interactions Z. Titus et al. https://doi.org/10.5194/acp-26-443-2026
- Evaluating the Representation of Tropical Stratocumulus and Shallow Cumulus Clouds As Well As Their Radiative Effects in CMIP6 Models Using Satellite Observations N. Črnivec et al. https://doi.org/10.1029/2022JD038437
- CloudViT: exploring cloud type classification with vision transformers in global satellite data J. Lenhardt et al. https://doi.org/10.5194/acp-26-5447-2026
- Deep Convective Activity in Tropical Cyclones and its Impact on Cirrus Clouds: Using Lightning as a Proxy J. Saha et al. https://doi.org/10.1007/s00024-025-03908-7
- Tracking Shallow Convective Mixing and Its Influence on Low‐Level Clouds With Stable Water Isotopes in Vapor J. Hu et al. https://doi.org/10.1029/2021JD035355
- Cloud phase characteristics over Southeast Asia from A-Train satellite observations Y. Hong & L. Di Girolamo https://doi.org/10.5194/acp-20-8267-2020
- Stormscapes T. Hädrich et al. https://doi.org/10.1145/3414685.3417801
- Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau W. Xu & D. Lyu https://doi.org/10.3390/rs13081418
- Earth-system-model evaluation of cloud and precipitation occurrence for supercooled and warm clouds over the Southern Ocean's Macquarie Island M. Stanford et al. https://doi.org/10.5194/acp-23-9037-2023
- Remote sensing of planetary boundary layer from ground and space: Structure, thermodynamics, and boundary layer clouds T. Su et al. https://doi.org/10.1016/j.rse.2026.115544
- Machine learning of cloud types in satellite observations and climate models P. Kuma et al. https://doi.org/10.5194/acp-23-523-2023
- Thunderscapes: Simulating the Dynamics of Mesoscale Convective System T. Hao et al. https://doi.org/10.1145/3728312
- Comparative assessment of a near‐global view of individual cloud types from space‐borne active and passive sensors and ground‐based observations T. Sarkar et al. https://doi.org/10.1002/joc.7693
- Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 2. Focus on Marine Low Level Clouds E. Höjgård‐Olsen et al. https://doi.org/10.1029/2021JD035402
- Multi-objective observational constraint of tropical Atlantic and Pacific low-cloud variability narrows uncertainty in cloud feedback M. Wu et al. https://doi.org/10.1038/s41467-024-53985-w
- Observational constraint on cloud feedbacks suggests moderate climate sensitivity G. Cesana & A. Del Genio https://doi.org/10.1038/s41558-020-00970-y
- Bridging the cloud gap: AHI/ATMS synergy through CNN feature fusion for all-weather SST retrieval D. Fan et al. https://doi.org/10.1016/j.jag.2025.104887
- Comparison of Macro- and Microphysical Properties in Precipitating and Non-Precipitating Clouds over Central-Eastern China during Warm Season X. Zheng et al. https://doi.org/10.3390/rs14010152
- Patterns and Mechanisms of Northeast Pacific Temperature Response to Pliocene Boundary Conditions P. Brennan et al. https://doi.org/10.1029/2021PA004370
Saved (final revised paper)
Latest update: 01 Nov 2026
Short summary
Low clouds (cloud top below 3 km) drive most of the uncertainty in future climate projections. Here we create a new dataset, the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies the different types of low clouds – stratocumulus and cumulus – based on their morphology. CASCCAD provides a basis to evaluate climate models and potentially improve our understanding of the cloud response to climate warming, as well as reduce the uncertainty in future climate projection.
Low clouds (cloud top below 3 km) drive most of the uncertainty in future climate projections....
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