Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-2831-2024
© Author(s) 2024. 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-16-2831-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
19 Jun 2024
Data description paper |
| 19 Jun 2024
| 19 Jun 2024
A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements
NASA Langley Research Center, Hampton, Virginia 23681, USA
Xia Cai
NASA Langley Research Center, Hampton, Virginia 23681, USA
Mark Vaughan
NASA Langley Research Center, Hampton, Virginia 23681, USA
Anne Garnier
Analytical Mechanics Associates, Hampton, Virginia 23666, USA
NASA Langley Research Center, Hampton, Virginia 23681, USA
Brian Magill
Analytical Mechanics Associates, Hampton, Virginia 23666, USA
NASA Langley Research Center, Hampton, Virginia 23681, USA
Melody Avery
NASA Langley Research Center, Hampton, Virginia 23681, USA
Brian Getzewich
NASA Langley Research Center, Hampton, Virginia 23681, USA
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2832, https://doi.org/10.5194/egusphere-2025-2832, 2025
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NASA’s CALIPSO satellite mission observed aerosols (airborne particles) globally from 2006 to 2023. Its final data products update improves its aerosol optical parameters over oceans by adjusting for regional and seasonal differences in a new measurement-model synergistic approach. This results in a more realistic aerosol characterization, specifically near coastlines (where sea salt mixes with pollution), with potential impacts to future studies of science applications (e.g., climate effects).
Jason L. Tackett, Robert A. Ryan, Anne E. Garnier, Jayanta Kar, Brian J. Getzewich, Xia Cai, Mark A. Vaughan, Charles R. Trepte, Ron C. Verhappen, David M. Winker, and Kam-Pui A. Lee
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The spaceborne atmospheric lidar CALIOP experienced an increasing number of intermittent low energy laser pulses in the final seven years of the 17-year long CALIPSO mission. Low energy pulses degraded the quality of retrievals in affected profiles. This paper describes low energy mitigation (LEM) algorithms that remove affected data and minimize data loss. LEM is demonstrated to correct calibration biases, reduce false feature detections, and restore the integrity of the CALIOP data record.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Jason L. Tackett, Jayanta Kar, Mark A. Vaughan, Brian J. Getzewich, Man-Hae Kim, Jean-Paul Vernier, Ali H. Omar, Brian E. Magill, Michael C. Pitts, and David M. Winker
Atmos. Meas. Tech., 16, 745–768, https://doi.org/10.5194/amt-16-745-2023, https://doi.org/10.5194/amt-16-745-2023, 2023
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The accurate identification of aerosol types in the stratosphere is important to characterize their impacts on the Earth climate system. The space-borne lidar on board CALIPSO is well-posed to identify aerosols in the stratosphere from volcanic eruptions and major wildfire events. This paper describes improvements implemented in the version 4.5 CALIPSO data release to more accurately discriminate between volcanic ash, sulfate, and smoke within the stratosphere.
Thibault Vaillant de Guélis, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker
Atmos. Meas. Tech., 15, 1931–1956, https://doi.org/10.5194/amt-15-1931-2022, https://doi.org/10.5194/amt-15-1931-2022, 2022
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A new IIR-based cloud and aerosol discrimination (CAD) algorithm is developed using the IIR brightness temperature differences for cloud and aerosol features confidently identified by the CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduce the ambiguity in a notable fraction of
not confidentV4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.
Thibault Vaillant de Guélis, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu
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We introduce a new lidar feature detection algorithm that dramatically improves the fine details of layers identified in the CALIOP data. By applying our two-dimensional scanning technique to the measurements in all three channels, we minimize false positives while accurately identifying previously undetected features such as subvisible cirrus and the full vertical extent of dense smoke plumes. Multiple comparisons to version 4.2 CALIOP retrievals illustrate the scope of the improvements made.
Melody A. Avery, Robert A. Ryan, Brian J. Getzewich, Mark A. Vaughan, David M. Winker, Yongxiang Hu, Anne Garnier, Jacques Pelon, and Carolus A. Verhappen
Atmos. Meas. Tech., 13, 4539–4563, https://doi.org/10.5194/amt-13-4539-2020, https://doi.org/10.5194/amt-13-4539-2020, 2020
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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.
Jayanta Kar, Mark A. Vaughan, Robert P. Damadeo, Mahesh Kovilakam, Jason L. Tackett, and Charles R. Trepte
EGUsphere, https://doi.org/10.5194/egusphere-2025-3141, https://doi.org/10.5194/egusphere-2025-3141, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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This paper assesses a possible bias in calibration of the spaceborne CALIOP lidar signals at 1064 nm resulting from relative attenuation of the signals at 1064 nm and 532 nm due to stratospheric aerosols. Multi-channel aerosol measurements from SAGE III instrument on ISS indicate that the bias is less than 1–2 % for background conditions and up to 5 % for strong stratospheric loading. Implications for extinction retrievals at 1064 nm and cascading errors for multi-layer scenes are discussed.
Travis Toth, Gregory Schuster, Marian Clayton, Zhujun Li, David Painemal, Sharon Rodier, Jayanta Kar, Tyler Thorsen, Richard Ferrare, Mark Vaughan, Jason Tackett, Huisheng Bian, Mian Chin, Anne Garnier, Ellsworth Welton, Robert Ryan, Charles Trepte, and David Winker
EGUsphere, https://doi.org/10.5194/egusphere-2025-2832, https://doi.org/10.5194/egusphere-2025-2832, 2025
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NASA’s CALIPSO satellite mission observed aerosols (airborne particles) globally from 2006 to 2023. Its final data products update improves its aerosol optical parameters over oceans by adjusting for regional and seasonal differences in a new measurement-model synergistic approach. This results in a more realistic aerosol characterization, specifically near coastlines (where sea salt mixes with pollution), with potential impacts to future studies of science applications (e.g., climate effects).
Jason L. Tackett, Robert A. Ryan, Anne E. Garnier, Jayanta Kar, Brian J. Getzewich, Xia Cai, Mark A. Vaughan, Charles R. Trepte, Ron C. Verhappen, David M. Winker, and Kam-Pui A. Lee
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
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The spaceborne atmospheric lidar CALIOP experienced an increasing number of intermittent low energy laser pulses in the final seven years of the 17-year long CALIPSO mission. Low energy pulses degraded the quality of retrievals in affected profiles. This paper describes low energy mitigation (LEM) algorithms that remove affected data and minimize data loss. LEM is demonstrated to correct calibration biases, reduce false feature detections, and restore the integrity of the CALIOP data record.
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This study investigates the formation mechanism of a tropopause cirrus cloud layer observed at extremely cold temperatures over Hyderabad in India during the 2017 Asian summer monsoon using balloon-borne sensors. Ice crystals smaller than 50 µm were found in this optically thin cirrus cloud layer. Combined analysis of back trajectories, satellite, and model data revealed that the formation of this layer was influenced by waves and stratospheric hydration induced by typhoon Hato.
David L. Mitchell, Anne Emilie Garnier, and Sarah Woods
EGUsphere, https://doi.org/10.5194/egusphere-2024-3790, https://doi.org/10.5194/egusphere-2024-3790, 2024
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Motivated by the need to better understand the physics of cirrus clouds, a satellite retrieval for cirrus cloud ice water content, ice particle number concentration and effective size was developed by exploiting relationships between cirrus cloud measurements made during field campaigns and cloud radiative properties measured by satellite. These retrievals tested favorably when compared against corresponding aircraft measurements and were found to depend on the visual opacity of the cloud.
David L. Mitchell and Anne Garnier
EGUsphere, https://doi.org/10.5194/egusphere-2024-3814, https://doi.org/10.5194/egusphere-2024-3814, 2024
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Arguably the greatest knowledge gap in cirrus cloud research is the relative roles of homogeneous and heterogeneous ice nucleation in cirrus cloud formation. Since this depends on temperature, latitude, season, and topography, a satellite remote sensing method was developed to measure cirrus cloud properties. It was found that cirrus clouds strongly affected by homogeneous ice nucleation may account for over half of the overall cirrus cloud radiative effect during winter outside the tropics.
Robert A. Ryan, Mark A. Vaughan, Sharon D. Rodier, Jason L. Tackett, John A. Reagan, Richard A. Ferrare, Johnathan W. Hair, John A. Smith, and Brian J. Getzewich
Atmos. Meas. Tech., 17, 6517–6545, https://doi.org/10.5194/amt-17-6517-2024, https://doi.org/10.5194/amt-17-6517-2024, 2024
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We introduce Ocean Derived Column Optical Depth (ODCOD), a new way to estimate column optical depths using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements from the ocean surface. ODCOD estimates include contributions from particulates in the full column, which CALIOP estimates do not, making it a complement measurement to CALIOP’s standard estimates. We find that ODCOD compares well with other established data sets in the daytime but tends to estimate higher at night.
Claire L. Ryder, Clément Bézier, Helen F. Dacre, Rory Clarkson, Vassilis Amiridis, Eleni Marinou, Emmanouil Proestakis, Zak Kipling, Angela Benedetti, Mark Parrington, Samuel Rémy, and Mark Vaughan
Nat. Hazards Earth Syst. Sci., 24, 2263–2284, https://doi.org/10.5194/nhess-24-2263-2024, https://doi.org/10.5194/nhess-24-2263-2024, 2024
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Desert dust poses a hazard to aircraft via degradation of engine components. This has financial implications for the aviation industry and results in increased fuel burn with climate impacts. Here we quantify dust ingestion by aircraft engines at airports worldwide. We find Dubai and Delhi in summer are among the dustiest airports, where substantial engine degradation would occur after 1000 flights. Dust ingestion can be reduced by changing take-off times and the altitude of holding patterns.
Jianyu Zheng, Zhibo Zhang, Hongbin Yu, Anne Garnier, Qianqian Song, Chenxi Wang, Claudia Di Biagio, Jasper F. Kok, Yevgeny Derimian, and Claire Ryder
Atmos. Chem. Phys., 23, 8271–8304, https://doi.org/10.5194/acp-23-8271-2023, https://doi.org/10.5194/acp-23-8271-2023, 2023
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We developed a multi-year satellite-based retrieval of dust optical depth at 10 µm and the coarse-mode dust effective diameter over global oceans. It reveals climatological coarse-mode dust transport patterns and regional differences over the North Atlantic, the Indian Ocean and the North Pacific.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
Short summary
Short summary
Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Jason L. Tackett, Jayanta Kar, Mark A. Vaughan, Brian J. Getzewich, Man-Hae Kim, Jean-Paul Vernier, Ali H. Omar, Brian E. Magill, Michael C. Pitts, and David M. Winker
Atmos. Meas. Tech., 16, 745–768, https://doi.org/10.5194/amt-16-745-2023, https://doi.org/10.5194/amt-16-745-2023, 2023
Short summary
Short summary
The accurate identification of aerosol types in the stratosphere is important to characterize their impacts on the Earth climate system. The space-borne lidar on board CALIPSO is well-posed to identify aerosols in the stratosphere from volcanic eruptions and major wildfire events. This paper describes improvements implemented in the version 4.5 CALIPSO data release to more accurately discriminate between volcanic ash, sulfate, and smoke within the stratosphere.
Zhujun Li, David Painemal, Gregory Schuster, Marian Clayton, Richard Ferrare, Mark Vaughan, Damien Josset, Jayanta Kar, and Charles Trepte
Atmos. Meas. Tech., 15, 2745–2766, https://doi.org/10.5194/amt-15-2745-2022, https://doi.org/10.5194/amt-15-2745-2022, 2022
Short summary
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For more than 15 years, CALIPSO has revolutionized our understanding of the role of aerosols in climate. Here we evaluate CALIPSO aerosol typing over the ocean using an independent CALIPSO–CloudSat product. The analysis suggests that CALIPSO correctly categorizes clean marine aerosol over the open ocean, elevated smoke over the SE Atlantic, and dust over the tropical Atlantic. Similarities between clean and dusty marine over the open ocean implies that algorithm modifications are warranted.
Thibault Vaillant de Guélis, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker
Atmos. Meas. Tech., 15, 1931–1956, https://doi.org/10.5194/amt-15-1931-2022, https://doi.org/10.5194/amt-15-1931-2022, 2022
Short summary
Short summary
A new IIR-based cloud and aerosol discrimination (CAD) algorithm is developed using the IIR brightness temperature differences for cloud and aerosol features confidently identified by the CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduce the ambiguity in a notable fraction of
not confidentV4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3253–3276, https://doi.org/10.5194/amt-14-3253-2021, https://doi.org/10.5194/amt-14-3253-2021, 2021
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The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results are presented in a companion paper (Part II).
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3277–3299, https://doi.org/10.5194/amt-14-3277-2021, https://doi.org/10.5194/amt-14-3277-2021, 2021
Short summary
Short summary
The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version 3 as a result of the significant changes implemented in the version 4 algorithms, which are presented in a companion paper (Part I).
Thibault Vaillant de Guélis, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu
Atmos. Meas. Tech., 14, 1593–1613, https://doi.org/10.5194/amt-14-1593-2021, https://doi.org/10.5194/amt-14-1593-2021, 2021
Short summary
Short summary
We introduce a new lidar feature detection algorithm that dramatically improves the fine details of layers identified in the CALIOP data. By applying our two-dimensional scanning technique to the measurements in all three channels, we minimize false positives while accurately identifying previously undetected features such as subvisible cirrus and the full vertical extent of dense smoke plumes. Multiple comparisons to version 4.2 CALIOP retrievals illustrate the scope of the improvements made.
David L. Mitchell, John Mejia, Anne Garnier, Yuta Tomii, Martina Krämer, and Farnaz Hosseinpour
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-846, https://doi.org/10.5194/acp-2020-846, 2020
Publication in ACP not foreseen
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This may be the first estimate of the radiative contribution of homogeneous ice nucleation in cirrus clouds on a global, regional and seasonal scale. This is achieved by constraining an atmospheric global climate model with measured cirrus cloud properties via satellite remote sensing. The results show that the overall radiative warming contributed by homogeneous ice nucleation at the top of the atmosphere is 2.4 W m-2 outside the ± 30° latitude zone during non-summer months (JJA).
Melody A. Avery, Robert A. Ryan, Brian J. Getzewich, Mark A. Vaughan, David M. Winker, Yongxiang Hu, Anne Garnier, Jacques Pelon, and Carolus A. Verhappen
Atmos. Meas. Tech., 13, 4539–4563, https://doi.org/10.5194/amt-13-4539-2020, https://doi.org/10.5194/amt-13-4539-2020, 2020
Short summary
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.
Cited articles
Avery, M. A., Ryan, R. A., Getzewich, B. J., Vaughan, M. A., Winker, D. M., Hu, Y., Garnier, A., Pelon, J., and Verhappen, C. A.: CALIOP V4 cloud thermodynamic phase assignment and the impact of near-nadir viewing angles, Atmos. Meas. Tech., 13, 4539–4563, https://doi.org/10.5194/amt-13-4539-2020, 2020. a, b
Berry, E. and Mace, G. G.: Cloud properties and radiative effects of the Asian summer monsoon derived from A-Train data, J. Geophys. Res.-Atmos., 119, 9492–9508, https://doi.org/10.1002/2014JD021458, 2014. a
Bühl, J., Alexander, S., Crewell, S., Heymsfield, A., Kalesse, H., Khain, A., Maahn, M., Van-Tricht, K., and Wendisch, M.: Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges – Chapter 10: Remote Sensing, Meteor. Mon., 58, 10.1–10.21, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0015.1, 2017. a
Cazenave, Q., Ceccaldi, M., Delanoë, J., Pelon, J., Groß, S., and Heymsfield, A.: Evolution of DARDAR-CLOUD ice cloud retrievals: new parameters and impacts on the retrieved microphysical properties, Atmos. Meas. Tech., 12, 2819–2835, https://doi.org/10.5194/amt-12-2819-2019, 2019. a, b
Ceccaldi, M., Delanoë, J., Hogan, R. J., Pounder, N. L., Protat, A., and Pelon, J.: From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations, J. Geophys. Res., 118, 7962–7981, https://doi.org/10.1002/jgrd.50579, 2013. a
Delanoë, J.: DARDAR CLOUD – Heymfield composite mass-size relationship, Aeris [data set], https://doi.org/10.25326/449, 2023. a
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res., 115, D00H29, https://doi.org/10.1029/2009JD012346, 2010. a, b
Deng, M., Mace, G. G., Wang, Z., and Okamoto, H.: Tropical Composition, Cloud and Climate Coupling Experiment Validation for Cirrus Cloud Profiling Retrieval Using CloudSat Radar and CALIPSO Lidar, J. Geophys. Res., 115, D00J15, https://doi.org/10.1029/2009JD013104, 2010. a, b
Deng, M., Mace, G. G., Wang, Z., and Berry, E.: CloudSat 2C-ICE Product Update with a New Ze Parameterization in Lidar-only Region, J. Geophys. Res.-Atmos., 120, 12198–12208, https://doi.org/10.1002/2015JD023600, 2015. a
Deng, M., Mace, G. G., and Wang, Z.: 2C-ICE PI_R05, CIRA [data set], https://www.cloudsat.cira.colostate.edu/data-products/2c-ice (last access: 21 May 2024), 2019. a
Dolinar, E. K., Campbell, J. R., Marquis, J. W., Garnier, A. E., and Karpowicz, B. M.: Novel parameterization of Ice Cloud Effective Diameter from Collocated CALIOP-IIR and CloudSat Retrievals, J. Appl. Meteor. Clim. 61, 891–907, https://doi.org/10.1175/JAMC-D-21-0163.1, 2022. a
Duncan, D. I. and Eriksson, P.: An update on global atmospheric ice estimates from satellite observations and reanalyses, Atmos. Chem. Phys., 18, 11205–11219, https://doi.org/10.5194/acp-18-11205-2018, 2018. a, b, c, d
Eliasson, S., Buehler, S. A., Milz, M., Eriksson, P., and John, V. O.: Assessing observed and modelled spatial distributions of ice water path using satellite data, Atmos. Chem. Phys., 11, 375–391, https://doi.org/10.5194/acp-11-375-2011, 2011. a, b
Garnier, A., Pelon, J., Pascal, N., Vaughan, M. A., Dubuisson, P., Yang, P., and Mitchell, D. L.: Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans, Atmos. Meas. Tech., 14, 3277–3299, https://doi.org/10.5194/amt-14-3277-2021, 2021. a
Gelaro, R, McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Getzewich, B. J., Vaughan, M. A., Hunt, W. H., Avery, M. A., Powell, K. A., Tackett, J. L., Winker, D. M., Kar, J., Lee, K.-P., and Toth, T. D.: CALIPSO lidar calibration at 532 nm: version 4 daytime algorithm, Atmos. Meas. Tech., 11, 6309–6326, https://doi.org/10.5194/amt-11-6309-2018, 2018. a
Haladay, T. and Stephens, G.: Characteristics of Tropical Thin Cirrus Clouds Deduced from Joint CloudSat and CALIPSO Observations, J. Geophys. Res., 114, D00A25, https://doi.org/10.1029/2008JD010675, 2009. a
Heymsfield, A., Winker, D., Avery, M., Vaughan, M., Diskin, G., Deng, M., Mitev, V., and Matthey, R.: Relationships between Ice Water Content and Volume Extinction Coefficient from In Situ Observations for Temperatures from 0° to −86 °C: Implications for Spaceborne Lidar Retrievals, J. Appl. Meteor. Climatol., 53, 479–505, https://doi.org/10.1175/JAMC-D-13-087.1, 2014. a, b
Hong, Y., Liu, G., and Li, J.-L. F.: Assessing the Radiative Effects of Global Ice Clouds Based on CloudSat and CALIPSO Measurements, J. Climate, 29, 7651–7674, https://doi.org/10.1175/JCLI-D-15-0799.1, 2016. a
Hu, Y.: Depolarization ratio–effective lidar ratio relation: Theoretical basis for space lidar cloud phase discrimination, Geophys. Res. Lett., 34, L11812, https://doi.org/10.1029/2007GL029584, 2007. a
Hunt, W. H., Winker, D. M., Vaughan, M. A., Powell, K. A., Lucker, P. L., and Weimer, C.: CALIPSO Lidar Description and Performance Assessment, J. Atmos. Ocean. Tech., 26, 1214–1228, https://doi.org/10.1175/2009JTECHA1223.1, 2009. a, b
Kar, J., Vaughan, M. A., Lee, K.-P., Tackett, J. L., Avery, M. A., Garnier, A., Getzewich, B. J., Hunt, W. H., Josset, D., Liu, Z., Lucker, P. L., Magill, B., Omar, A. H., Pelon, J., Rogers, R. R., Toth, T. D., Trepte, C. R., Vernier, J.-P., Winker, D. M., and Young, S. A.: CALIPSO lidar calibration at 532 nm: version 4 nighttime algorithm, Atmos. Meas. Tech., 11, 1459–1479, https://doi.org/10.5194/amt-11-1459-2018, 2018. a
Key, J. R.: Estimating the Area Fraction of Geophysical Fields from Measurements Along a Transect, IEEE Trans. Geosci. Remote, 31, 1099–1102, https://doi.org/10.1109/36.263782, 1993. a
Kotarba, A. Z.: Errors in global cloud climatology due to transect sampling with the CALIPSO satellite lidar mission, Atmos. Res., 279, 106379, https://doi.org/10.1016/j.atmosres.2022.106379, 2022. a
Kotarba, A. Z. and Solecki, M.: Uncertainty Assessment of the Vertically-Resolved Cloud Amount for Joint CloudSat–CALIPSO Radar–Lidar Observations, Remote Sens., 13, 807, https://doi.org/10.3390/rs13040807, 2021. a
Li, J.-L. F., Waliser, D. E., Stephens, G., and Lee, S.: Characterizing and Understanding Cloud Ice and Radiation Budget Biases in Global Climate Models and Reanalysis, Meteor. Mon., 56, 13.01–13.20, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0007.1, 2016. a
Liu, Z., Kar, J., Zeng, S., Tackett, J., Vaughan, M., Avery, M., Pelon, J., Getzewich, B., Lee, K.-P., Magill, B., Omar, A., Lucker, P., Trepte, C., and Winker, D.: Discriminating between clouds and aerosols in the CALIOP version 4.1 data products, Atmos. Meas. Tech., 12, 703–734, https://doi.org/10.5194/amt-12-703-2019, 2019. a, b, c
Mace, G., Zhang, Q., Vaughan, M., Marchand, R., Stephens, G., Trepte, C., and Winker, D.: A Description of Hydrometeor Layer Occurrence Statistics Derived from the First Year of Merged Cloudsat and CALIPSO Data, J. Geophys. Res., 114, D00A26, https://doi.org/10.1029/2007JD009755, 2009. a
Mioche, G., Josset, D., Gayet, J.-F., Pelon, J., Garnier, A., Minikin, A., and Schwarzenboeck, A.: Validation of the CALIPSO/CALIOP extinction coefficients from in situ observations in mid-latitude cirrus clouds during CIRCLE-2 experiment, J. Geophys. Res., 115, D00H25, https://doi.org/10.1029/2009JD012376, 2010. a
Murtagh, D. Frisk, U., Merino, F., Ridal, M., Jonsson, A., Stegman, J., Witt, G., Eriksson, P., Jiménez, C., Megie, G., de la Noë, J., Ricaud, P., Baron, P., Pardo, J. R., Hauchcorne, A., Llewellyn, E. J., Degenstein, D. A., Gattinger, R. L., Lloyd, N. D., Evans, W. F. J., McDade, I. C., Haley, C. S., Sioris, C., von Savigny, C., Solheim, B. H., McConnell, J. C., Strong, K., Richardson, E. H., Leppelmeier, G. W., Kyrölä, E., Auvinen, H., and Oikarinen, L.: An overview of the Odin atmospheric mission, Can. J. Phys., 80, 309–318, https://doi.org/10.1139/p01-157, 2002. a
NASA JPL: NASA Shuttle Radar Topography Mission Global 1 arc second, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003 (last access: 23 February 2023), 2013. a
Noël, V. and Sassen, K.: Study of planar ice crystal orientations in ice clouds from scanning polarization lidar observations, J. Appl. Meteor, 44, 643–664, https://doi.org/10.1175/JAM2223.1, 2005. a
Noel, V., Chepfer, H., Chiriaco, M., and Yorks, J.: The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station, Atmos. Chem. Phys., 18, 9457–9473, https://doi.org/10.5194/acp-18-9457-2018, 2018. a
Powell, K. A., Hostetler, C. A., Liu, Z., Vaughan, M. A., Kuehn, R. E., Hunt, W. H., Lee, K., Trepte, C. R., Rogers, R. R., Young, S. A., and Winker, D. M.: CALIPSO Lidar Calibration Algorithms: Part I – Nighttime 532 nm Parallel Channel and 532 nm Perpendicular Channel, J. Atmos. Ocean. Tech., 26, 2015–2033, https://doi.org/10.1175/2009JTECHA1242.1, 2009. a
Pitts, M. C., Poole, L. R., and Gonzalez, R.: Polar stratospheric cloud climatology based on CALIPSO spaceborne lidar measurements from 2006 to 2017, Atmos. Chem. Phys., 18, 10881–10913, https://doi.org/10.5194/acp-18-10881-2018, 2018. a
Sassen, K.: Ice crystal habit discrimination with the optical backscatter depolarization technique, J. Appl. Meteor., 16, 425–431, 1977. a
Sassen, K., Wang, Z., and Liu, D.: Cirrus clouds and deep convection in the tropics: Insights from CALIPSO and CloudSat, J. Geophys. Res., 114, D00H06, https://doi.org/10.1029/2009JD011916, 2009. a
Silber, I., Fridlind, A. M., Verlinde, J., Ackerman, A. S., Cesana, G. V., and Knopf, D. A.: The prevalence of precipitation from polar supercooled clouds, Atmos. Chem. Phys., 21, 3949–3971, https://doi.org/10.5194/acp-21-3949-2021, 2021. a
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O’Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Team: The CloudSat Mission and the A-Train, B. Am. Meteorol. Soc., 1771-1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002. a
Tackett, J. L., Winker, D. M., Getzewich, B. J., Vaughan, M. A., Young, S. A., and Kar, J.: CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design, Atmos. Meas. Tech., 11, 4129–4152, https://doi.org/10.5194/amt-11-4129-2018, 2018. a
Tackett, J., Ryan, R., Vaughan, M, Garnier, A., Getzewich, B., Winker, D., and Trepte, C.: Mitigation Strategy for the Impact of Low Energy Laser Pulses in CALIOP Calibration and Level 2 Retrievals, Proceedings of the 30th International Laser Radar Conference, 617–623, https://doi.org/10.1007/978-3-031-37818-8_79, 2023. a
Tanelli, S., Dobrowalski, G., Lebsock, M., Durden, S. L., Partain, P., Reinke, D., and Reinke, D: CloudSat’s Cloud Profiling Radar: Status, Performance, and latest data product changes, CALIPSO-CloudSat Science Team Meeting, 3–5 November 2014, Alexandria VA USA, 2014. a
Vaughan, M., Powell, K., Kuehn, R., Young, S., Winker, D., Hostetler, C., Hunt, W., Liu, Z., McGill, M., and Getzewich, B.: Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements, J. Atmos. Ocean. Tech., 26, 2034–2050, https://doi.org/10.1175/2009JTECHA1228.1, 2009. a, b, c
Vaughan, M., Garnier, A., Josset, D., Avery, M., Lee, K.-P., Liu, Z., Hunt, W., Pelon, J., Hu, Y., Burton, S., Hair, J., Tackett, J. L., Getzewich, B., Kar, J., and Rodier, S.: CALIPSO lidar calibration at 1064 nm: version 4 algorithm, Atmos. Meas. Tech., 12, 51–82, https://doi.org/10.5194/amt-12-51-2019, 2019. a
Vaughan, M., Pitts, M., Trepte, C., Winker, D., Detweiler, P., Garnier, A., Getzewich, B., Hunt, W., Lambeth, J., Lee, K.-P., Lucker, P., Murray, T., Rodier, S., Tremas, T., Bazureau, A., and Pelon, J.: Cloud-Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO) data management system data products catalog, Release 4.95, NASA Langley Research Center Document PC-SCI-503, 277 pp., https://www-calipso.larc.nasa.gov/products/CALIPSO_DPC_Rev4x95.pdf (last access: 10 July 2023), 2023. a, b, c
Waliser, D. E., Li, J.-L. F., Woods, C. P., Austin, R. T., Bacmeister, J., Chern, J., Del Genio, A., Jiang, J. H., Kuang, Z., Meng, H., Minnis, P., Platnick, S., Rossow, W. B., Stephens, G. L., Sun-Mack, S., Tao, W.-K., Tompkins, A. M., Vane, D. G., Walker, C., and Wu, D.: Cloud ice: A climate model challenge with signs and expectations of progress, J. Geophys. Res., 114, D00A21, https://doi.org/10.1029/2008JD010015, 2009. a, b, c, d, e, f
Waters, J. W., Froidevaux, L., Harwood, R. S., Jarnot, R. F., Pickett, H. M., Read, W. G., Siegel, P. H., Cofield, R. E., Filipiak, M. J., Flower, D. A., Holden, J. R., Lau, G. K., Livesey, N. J., Manney, G. L., Pumphrey, H. C., Santee, M. L., Wu, D. L., Cuddy, D. T., Lay, R. R., Loo, M. S., Perun, V. S., Schwartz, M. J., Stek, P. C., Thurstans, R. P., Boyles, M. A., Chandra, K. M., Chavez, M. C., Chen, G.-S., Chudasama, B. V., Dodge, R., Fuller, R. A., Girard, M. A., Jiang, J. H., Jiang, Y., Knosp, B. W., LaBelle, R. C., Lam, J. C., Lee, K. A., Miller, D., Oswald, J. E., Patel, N. C., Pukala, D. M., Quintero, O., Scaff, D. M., van Snyder, W., Tope, M. C., Wagner, P. A., and Walch, M. J.: The Earth Observing System Microwave Limb Sounder (EOS MLS) on the Aura satellite, IEEE Trans. Geosci. Remote Sens., 44, 1075–1092, https://doi.org/10.1109/TGRS.2006.873771, 2006. a
Winker, D. M., Vaughan, M. A., Omar, A. H., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009. a, b
Winker, D. M., Pelon, J., Coakley Jr., J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L., Le Treut, H., McCormick, M. P., Megie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO mission: A Global 3D view of aerosols and clouds, B. Am. Meteorol. Soc., 91, 1211–1229, https://doi.org/10.1175/2010BAMS3009.1, 2010. a
Winker, D. M., Tackett, J. L., Getzewich, B. J., Liu, Z., Vaughan, M. A., and Rogers, R. R.: The global 3-D distribution of tropospheric aerosols as characterized by CALIOP, Atmos. Chem. Phys., 13, 3345–3361, https://doi.org/10.5194/acp-13-3345-2013, 2013. a
Winker, D., Chepfer, H., Noël, V., and Cai, X.: Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors, Surv. Geophys., 38, 1483–1508, https://doi.org/10.1007/s10712-017-9452-0, 2017. a
Winker, D. M., Cai, X., McGill, B., Getzewich, B., Vaughan, M., Garnier, A., and Avery, M.: CALIPSO Level 3 Ice Cloud Product, Atmospheric Sciences Data Center (ASDC) [data set], https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00, 2018. a, b
Wu, D. L., Jiang, J. H., Read, W. G., Austin, R. T., Davis, C. P., Lambert, A., Stephens, G. L., Vane, D. G., and Waters, J. W.: Validation of the Aura MLS cloud ice water content measurements, J. Geophys. Res., 113, D15S10, https://doi.org/10.1029/2007JD008931, 2008. a
Wu. D., Austin, R. T., Deng, M., Durden, S. L., Heymsfield, A. J., Jiang, J. H., Lambert, A., Li, J.-L., Livesey, N. J., McFarquhar, G. M., Pittman, J. V., Stephens, G. L., Tanelli, S., Vane, D. G., and Waliser, D. E.: Comparisons of global cloud ice from MLS, CloudSat, and correlative data sets, J. Geophys. Res., 114, D00A24, https://doi.org/10.1029/2008JD009946, 2009. a
Young, S. A. and Vaughan, M. A.: The retrieval of profiles of particulate extinction from Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) data: Algorithm description, J. Atmos. Ocean. Tech., 26, 1105–1119, https://doi.org/10.1175/2008JTECHA1221.1, 2009. a
Young, S. A., Vaughan, M. A., Kuehn, R. E., and Winker, D. M.: The Retrieval of Profiles of Particulate Extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Data: Uncertainty and Error Sensitivity Analyses, J. Atmos. Ocean. Tech., 30, 395–428, https://doi.org/10.1175/JTECH-D-12-00046.1, 2013. a, b, c, d
Young, S. A., Vaughan, M. A., Garnier, A., Tackett, J. L., Lambeth, J. D., and Powell, K. A.: Extinction and optical depth retrievals for CALIPSO's Version 4 data release, Atmos. Meas. Tech., 11, 5701–5727, https://doi.org/10.5194/amt-11-5701-2018, 2018. a, b, c, d
Zhang, D., Wang, Z., and Liu, D.: A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements, J. Geophys. Res., 115, D00H13, https://doi.org/10.1029/2009JD012143, 2010. a
Short summary
Clouds play important roles in both weather and climate. In this paper we describe version 1.0 of a unique global ice cloud data product derived from over 12 years of global spaceborne lidar measurements. This monthly gridded product provides a unique vertically resolved characterization of the occurrence and properties, optical and physical, of thin ice clouds and the tops of deep convective clouds. It should provide significant value for cloud research and model evaluation.
Clouds play important roles in both weather and climate. In this paper we describe version 1.0...
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