Articles | Volume 16, issue 14
https://doi.org/10.5194/amt-16-3531-2023
© Author(s) 2023. 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-16-3531-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jul 2023
Research article |
| 25 Jul 2023
| 25 Jul 2023
What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations
Richard M. Schulte
CORRESPONDING AUTHOR
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO, USA
Matthew D. Lebsock
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
John M. Haynes
Cooperative Institute for Research in the Atmosphere, Fort Collins,
CO, USA
Related authors
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Luis F. Millán, Matthew D. Lebsock, and Marcin J. Kurowski
Atmos. Meas. Tech., 18, 4483–4495, https://doi.org/10.5194/amt-18-4483-2025, https://doi.org/10.5194/amt-18-4483-2025, 2025
Short summary
Short summary
This study explores the potential of a hypothetical spaceborne radar to observe water vapor within clouds.
Juan Socuellamos, Matthew Lebsock, Raquel Rodriguez Monje, Marcin Kurowski, Derek Posselt, and Robert Beauchamp
EGUsphere, https://doi.org/10.5194/egusphere-2025-4248, https://doi.org/10.5194/egusphere-2025-4248, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Using simulated airborne and spaceborne W-band (94 GHz) and G-band (239 GHz) radar observations, this article presents an optimal estimation framework that includes dual-frequency reflectivity and differential absorption measurements to obtain more accurate drizzle retrievals. We demonstrate that an improvement of more than one order of magnitude in the retrieval of the drizzle mixing ratio, droplet concentration, and mass-weighted diameter can be achieved compared to W-band only results.
Nitika Yadlapalli Yurk, Matthew Lebsock, Juan Socuellamos, Raquel Rodriguez Monje, Ken Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-618, https://doi.org/10.5194/egusphere-2025-618, 2025
Short summary
Short summary
Current knowledge of the link between clouds and climate is limited by lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time and elevation.
Marcin J. Kurowski, Matthew D. Lebsock, and Kevin M. Smalley
EGUsphere, https://doi.org/10.5194/egusphere-2025-714, https://doi.org/10.5194/egusphere-2025-714, 2025
Short summary
Short summary
This study explores how clouds respond to pollution throughout the day using high-resolution simulations. Polluted clouds show stronger daily changes: thicker clouds at night and in the morning but faster thinning in the afternoon. Pollution reduces rainfall but enhances drying, deepening the cloud layer. While the pollution initially brightens clouds, the daily cycle of cloudiness slightly reduces this brightening effect.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, Robert M. Beauchamp, and Arturo Umeyama
Earth Syst. Sci. Data, 16, 2701–2715, https://doi.org/10.5194/essd-16-2701-2024, https://doi.org/10.5194/essd-16-2701-2024, 2024
Short summary
Short summary
This paper describes multifrequency radar observations of clouds and precipitation during the EPCAPE campaign. The data sets were obtained from CloudCube, a Ka-, W-, and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals. This data collection provides a unique opportunity to study hydrometeors with diameters in the millimeter and submillimeter size range that can be used to better understand the drop size distribution within clouds and precipitation.
Leah Bertrand, Jennifer E. Kay, John Haynes, and Gijs de Boer
Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, https://doi.org/10.5194/essd-16-1301-2024, 2024
Short summary
Short summary
The vertical structure of clouds has a major impact on global energy flows, air circulation, and the hydrologic cycle. Two satellite instruments, CloudSat radar and CALIPSO lidar, have taken complementary measurements of cloud vertical structure for over a decade. Here, we present the 3S-GEOPROF-COMB product, a globally gridded satellite data product combining CloudSat and CALIPSO observations of cloud vertical structure.
Luis F. Millán, Matthew D. Lebsock, Ken B. Cooper, Jose V. Siles, Robert Dengler, Raquel Rodriguez Monje, Amin Nehrir, Rory A. Barton-Grimley, James E. Collins, Claire E. Robinson, Kenneth L. Thornhill, and Holger Vömel
Atmos. Meas. Tech., 17, 539–559, https://doi.org/10.5194/amt-17-539-2024, https://doi.org/10.5194/amt-17-539-2024, 2024
Short summary
Short summary
In this study, we describe and validate a new technique in which three radar tones are used to estimate the water vapor inside clouds and precipitation. This instrument flew on board NASA's P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign and the Synergies Of Active optical and Active microwave Remote Sensing Experiment (SOA2RSE) campaign.
Matthew D. Lebsock and Mikael Witte
Atmos. Chem. Phys., 23, 14293–14305, https://doi.org/10.5194/acp-23-14293-2023, https://doi.org/10.5194/acp-23-14293-2023, 2023
Short summary
Short summary
This paper evaluates measurements of cloud drop size distributions made from airplanes. We find that as the number of cloud drops increases the distribution of the cloud drop sizes narrows. The data are used to develop a simple equation that relates the drop number to the width of the drop sizes. We then use this equation to demonstrate that existing approaches to observe the drop number from satellites contain errors that can be corrected by including the new relationship.
Kevin M. Smalley, Matthew D. Lebsock, Ryan Eastman, Mark Smalley, and Mikael K. Witte
Atmos. Chem. Phys., 22, 8197–8219, https://doi.org/10.5194/acp-22-8197-2022, https://doi.org/10.5194/acp-22-8197-2022, 2022
Short summary
Short summary
We use geostationary satellite observations to track pockets of open-cell (POC) stratocumulus and analyze how precipitation, cloud microphysics, and the environment change. Precipitation becomes more intense, corresponding to increasing effective radius and decreasing number concentrations, while the environment remains relatively unchanged. This implies that changes in cloud microphysics are more important than the environment to POC development.
Mark T. Richardson, David R. Thompson, Marcin J. Kurowski, and Matthew D. Lebsock
Atmos. Meas. Tech., 15, 117–129, https://doi.org/10.5194/amt-15-117-2022, https://doi.org/10.5194/amt-15-117-2022, 2022
Short summary
Short summary
Sunlight can pass diagonally through the atmosphere, cutting through the 3-D water vapour field in a way that
smears2-D maps of imaging spectroscopy vapour retrievals. In simulations we show how this smearing is
towardsor
away fromthe Sun, so calculating
across the solar direction allows sub-kilometre information about water vapour's spatial scaling to be calculated. This could be tested by airborne campaigns and used to obtain new information from upcoming spaceborne data products.
Richard J. Roy, Matthew Lebsock, and Marcin J. Kurowski
Atmos. Meas. Tech., 14, 6443–6468, https://doi.org/10.5194/amt-14-6443-2021, https://doi.org/10.5194/amt-14-6443-2021, 2021
Short summary
Short summary
This study describes the potential capabilities of a hypothetical spaceborne radar to observe water vapor within clouds.
Mark T. Richardson, David R. Thompson, Marcin J. Kurowski, and Matthew D. Lebsock
Atmos. Meas. Tech., 14, 5555–5576, https://doi.org/10.5194/amt-14-5555-2021, https://doi.org/10.5194/amt-14-5555-2021, 2021
Short summary
Short summary
Modern and upcoming hyperspectral imagers will take images with spatial resolutions as fine as 20 m. They can retrieve column water vapour, and we show evidence that from these column measurements you can get statistics of planetary boundary layer (PBL) water vapour. This is important information for climate models that need to account for sub-grid mixing of water vapour near the surface in their PBL schemes.
David R. Thompson, Brian H. Kahn, Philip G. Brodrick, Matthew D. Lebsock, Mark Richardson, and Robert O. Green
Atmos. Meas. Tech., 14, 2827–2840, https://doi.org/10.5194/amt-14-2827-2021, https://doi.org/10.5194/amt-14-2827-2021, 2021
Short summary
Short summary
Concentrations of water vapor in the atmosphere vary dramatically over space and time. Mapping this variability can provide insights into atmospheric processes that help us understand atmospheric processes in the Earth system. Here we use a new measurement strategy based on imaging spectroscopy to map atmospheric water vapor concentrations at very small spatial scales. Experiments demonstrate the accuracy of this technique and some initial results from an airborne remote sensing experiment.
Luis Millán, Richard Roy, and Matthew Lebsock
Atmos. Meas. Tech., 13, 5193–5205, https://doi.org/10.5194/amt-13-5193-2020, https://doi.org/10.5194/amt-13-5193-2020, 2020
Short summary
Short summary
This paper describes the feasibility of using a differential absorption radar technique for the remote sensing of total column water vapor from a spaceborne platform.
Mark Richardson, Matthew D. Lebsock, James McDuffie, and Graeme L. Stephens
Atmos. Meas. Tech., 13, 4947–4961, https://doi.org/10.5194/amt-13-4947-2020, https://doi.org/10.5194/amt-13-4947-2020, 2020
Short summary
Short summary
We previously combined CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data and reflected-sunlight measurements from OCO-2 (Orbiting Carbon Observatory 2) for information about low clouds over oceans. The satellites are no longer formation-flying, so this work is a step towards getting new information about these clouds using only OCO-2. We can rapidly and accurately identify liquid oceanic clouds and obtain their height better than a widely used passive sensor.
Cited articles
Boers, R., Acarreta, J. R., and Gras, J. L.: Satellite monitoring of the first indirect aerosol effect: Retrieval of the droplet concentration of water clouds, J. Geophys. Res.-Atmos., 111, D22208, https://doi.org/10.1029/2005JD006838, 2006.
Brenguier, J.-L., Pawlowska, H., Schüller, L., Preusker, R., Fischer,
J., and Fouquart, Y.: Radiative Properties of Boundary Layer Clouds: Droplet
Effective Radius versus Number Concentration, J. Atmos.
Sci., 57, 803–821, https://doi.org/10.1175/1520-0469(2000)057<0803:RPOBLC>2.0.CO;2, 2000.
Brenguier, J.-L., Pawlowska, H., and Schüller, L.: Cloud microphysical
and radiative properties for parameterization and satellite monitoring of
the indirect effect of aerosol on climate, J. Geophys. Res.-Atmos., 108, D158632, https://doi.org/10.1029/2002JD002682, 2003.
Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Ghan, S. J.,
Kiehl, J. T., Le Treut, H., Li, Z.-X., Liang, X.-Z., Mitchell, J. F. B.,
Morcrette, J.-J., Randall, D. A., Riches, M. R., Roeckner, E., Schlese, U.,
Slingo, A., Taylor, K. E., Washington, W. M., Wetherald, R. T., and Yagai,
I.: Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric
General Circulation Models, Science, 245, 513–516,
https://doi.org/10.1126/science.245.4917.513, 1989.
Christensen, M. W., Stephens, G. L., and Lebsock, M. D.: Exposing biases in
retrieved low cloud properties from CloudSat: A guide for evaluating
observations and climate data, J. Geophys. Res.-Atmos.,
118, 12120–12131, https://doi.org/10.1002/2013JD020224, 2013.
CloudSat Data Processing Center: Data Products, CloudSat DPC [data set], https://www.cloudsat.cira.colostate.edu/data-products, last access: 7 March 2023.
Colbo, K. and Weller, R.: The variability and heat budget of the upper ocean
under the Chile-Peru stratus, J. Mar. Res., 65, 607–637,
https://doi.org/10.1357/002224007783649510, 2007.
Colbo, K. and Weller, R. A.: Accuracy of the IMET Sensor Package in the
Subtropics, J. Atmos. Ocean. Tech., 26, 1867–1890,
https://doi.org/10.1175/2009JTECHO667.1, 2009.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M.
D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M.,
Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A.,
Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D.,
Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K.,
Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang,
Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm
Clouds: A Review of the Current State of Knowledge and Perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018.
Hartmann, D. L., Ockert-Bell, M. E., and Michelsen, M. L.: The Effect of
Cloud Type on Earth's Energy Balance: Global Analysis, J. Climate,
5, 1281–1304, https://doi.org/10.1175/1520-0442(1992)005<1281:TEOCTO>2.0.CO;2, 1992.
Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu,
C., Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with
spaceborne W-band radar, J. Geophys. Res.-Atmos., 114, D00A22,
https://doi.org/10.1029/2008JD009973, 2009.
Henderson, D. S., L'Ecuyer, T., Stephens, G., Partain, P., and Sekiguchi,
M.: A Multisensor Perspective on the Radiative Impacts of Clouds and
Aerosols, J. Appl. Meteorol. Clim., 52, 853–871,
https://doi.org/10.1175/JAMC-D-12-025.1, 2013.
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.
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H.,
Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P.,
Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota,
T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H.,
Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez,
A., Wandinger, U., Wehr, T., and van Zadelhoff, G.-J.: The EarthCARE
Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols,
Precipitation, and Radiation, B. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015.
Justice, C. O., Vermote, E., Townshend, J. R. G., Defries, R., Roy, D. P.,
Hall, D. K., Salomonson, V. V., Privette, J. L., Riggs, G., Strahler, A.,
Lucht, W., Myneni, R. B., Knyazikhin, Y., Running, S. W., Nemani, R. R.,
Wan, Z., Huete, A. R., van Leeuwen, W., Wolfe, R. E., Giglio, L., Muller,
J., Lewis, P., and Barnsley, M. J.: The Moderate Resolution Imaging
Spectroradiometer (MODIS): land remote sensing for global change research,
IEEE T. Geosci. Remote, 36, 1228–1249, https://doi.org/10.1109/36.701075, 1998.
King, M. D., Kaufman, Y. J., Menzel, W. P., and Tanre, D.: Remote sensing of
cloud, aerosol, and water vapor properties from the moderate resolution
imaging spectrometer (MODIS), IEEE T. Geosci. Remote, 30, 2–27, https://doi.org/10.1109/36.124212, 1992.
Klein, S. A. and Hartmann, D. L.: The Seasonal Cycle of Low Stratiform
Clouds, J. Climate, 6, 1587–1606,
https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2, 1993.
Krueger, S. K., McLean, G. T., and Fu, Q.: Numerical Simulation of the
Stratus-to-Cumulus Transition in the Subtropical Marine Boundary Layer. Part II: Boundary-Layer Circulation, J. Atmos. Sci., 52,
2851–2868, https://doi.org/10.1175/1520-0469(1995)052<2851:NSOTST>2.0.CO;2, 1995.
Lamer, K., Kollias, P., Battaglia, A., and Preval, S.: Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars, Atmos. Meas. Tech., 13, 2363–2379, https://doi.org/10.5194/amt-13-2363-2020, 2020.
Lebsock, M. D. and L'Ecuyer, T. S.: The retrieval of warm rain from CloudSat, J. Geophys. Res., 116, D20209, https://doi.org/10.1029/2011JD016076, 2011.
Lebsock, M. and Su, H.: Application of active spaceborne remote sensing for
understanding biases between passive cloud water path retrievals, J. Geophys. Res.-Atmos., 119, 8962–8979, https://doi.org/10.1002/2014JD021568, 2014.
Leinonen, J., Lebsock, M. D., Stephens, G. L., and Suzuki, K.: Improved
Retrieval of Cloud Liquid Water from CloudSat and MODIS, J. Appl.
Meteorol. Clim., 55, 1831–1844, https://doi.org/10.1175/JAMC-D-16-0077.1, 2016.
Li, J.-L., Lee, S., Ma, H.-Y., Stephens, G., and Guan, B.: Assessment of the
cloud liquid water from climate models and reanalysis using satellite
observations, Terrestrial, Atmos. Ocean. Sci., 29, 653–678,
https://doi.org/10.3319/TAO.2018.07.04.01, 2018.
Ma, C.-C., Mechoso, C. R., Robertson, A. W., and Arakawa, A.: Peruvian
Stratus Clouds and the Tropical Pacific Circulation: A Coupled
Ocean-Atmosphere GCM Study, J. Climate, 9, 1635–1645,
https://doi.org/10.1175/1520-0442(1996)009<1635:PSCATT>2.0.CO;2, 1996.
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor
Detection Using Cloudsat – An Earth-Orbiting 94 GHz Cloud Radar, J. Atmos. Ocean. Tech., 25, 519–533, https://doi.org/10.1175/2007JTECHA1006.1, 2008.
Martin, G. M., Johnson, D. W., and Spice, A.: The Measurement and
Parameterization of Effective Radius of Droplets in Warm Stratocumulus
Clouds, J. Atmos. Sci., 51, 1823–1842,
https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2, 1994.
Mechoso, C. R., Wood, R., Weller, R., Bretherton, C. S., Clarke, A. D., Coe,
H., Fairall, C., Farrar, J. T., Feingold, G., Garreaud, R., Grados, C.,
McWilliams, J., Szoeke, S. P. de, Yuter, S. E., and Zuidema, P.:
Ocean–Cloud–Atmosphere–Land Interactions in the Southeastern Pacific: The
VOCALS Program, B. Am. Meteorol. Soc., 95, 357–375, https://doi.org/10.1175/BAMS-D-11-00246.1, 2014.
Miles, N. L., Verlinde, J., and Clothiaux, E. E.: Cloud Droplet Size Distributions in Low-Level Stratiform Clouds, J. Atmos. Sci., 57, 295–311, https://doi.org/10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2, 2000.
Min, Q., Joseph, E., Lin, Y., Min, L., Yin, B., Daum, P. H., Kleinman, L. I., Wang, J., and Lee, Y.-N.: Comparison of MODIS cloud microphysical properties with in-situ measurements over the Southeast Pacific, Atmos. Chem. Phys., 12, 11261–11273, https://doi.org/10.5194/acp-12-11261-2012, 2012.
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and
Effective Particle Radius of Clouds from Reflected Solar Radiation
Measurements. Part I: Theory, J. Atmos. Sci., 47,
1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
Painemal, D. and Zuidema, P.: Assessment of MODIS cloud effective radius and
optical thickness retrievals over the Southeast Pacific with VOCALS-REx in
situ measurements, J. Geophys. Res.-Atmos., 116, D24206,
https://doi.org/10.1029/2011JD016155, 2011.
Platnick, S.: Vertical photon transport in cloud remote sensing problems,
J. Geophys. Res.-Atmos., 105, 22919–22935, https://doi.org/10.1029/2000JD900333, 2000.
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A.,
Riedi, J. C., and Frey, R. A.: The MODIS cloud products: algorithms and
examples from Terra, IEEE T. Geosci. Remote, 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003.
Rangno, A. L. and Hobbs, P. V.: Microstructures and precipitation
development in cumulus and small cumulonimbus clouds over the warm pool of
the tropical Pacific Ocean, Q. J. Roy. Meteor. Soc., 131, 639–673, https://doi.org/10.1256/qj.04.13, 2005.
Rauber, R. M., Stevens, B., Ochs, H. T., Knight, C., Albrecht, B. A., Blyth,
A. M., Fairall, C. W., Jensen, J. B., Lasher-Trapp, S. G., Mayol-Bracero, O.
L., Vali, G., Anderson, J. R., Baker, B. A., Bandy, A. R., Burnet, E.,
Brenguier, J.-L., Brewer, W. A., Brown, P. R. A., Chuang, R., Cotton, W. R.,
Girolamo, L. D., Geerts, B., Gerber, H., Göke, S., Gomes, L., Heikes, B.
G., Hudson, J. G., Kollias, P., Lawson, R. R., Krueger, S. K., Lenschow, D.
H., Nuijens, L., O'Sullivan, D. W., Rilling, R. A., Rogers, D. C., Siebesma,
A. P., Snodgrass, E., Stith, J. L., Thornton, D. C., Tucker, S., Twohy, C.
H., and Zuidema, P.: Rain in Shallow Cumulus Over the Ocean: The RICO
Campaign, B. Am. Meteorol. Soc., 88, 1912–1928,
https://doi.org/10.1175/BAMS-88-12-1912, 2007.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and
Practice, World Scientific, https://doi.org/10.1142/3171, 2000.
Saito, M., Yang, P., Hu, Y., Liu, X., Loeb, N., Smith Jr., W. L., and Minnis,
P.: An Efficient Method for Microphysical Property Retrievals in Vertically
Inhomogeneous Marine Water Clouds Using MODIS-CloudSat Measurements, J. Geophys. Res.-Atmos., 124, 2174–2193, https://doi.org/10.1029/2018JD029659, 2019.
Sassen, K., Wang, Z., and Liu, D.: Global distribution of cirrus clouds from
CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations
(CALIPSO) measurements, J. Geophys. Res.-Atmos., 113, D00A12,
https://doi.org/10.1029/2008JD009972, 2008.
Schulte, R.: A-Train Subadiabatic Model, Zenodo [code],
https://doi.org/10.5281/zenodo.7706791, 2023.
Serpetzoglou, E., Albrecht, B. A., Kollias, P., and Fairall, C. W.: Boundary
Layer, Cloud, and Drizzle Variability in the Southeast Pacific Stratocumulus
Regime, J. Climate, 21, 6191–6214, https://doi.org/10.1175/2008JCLI2186.1, 2008.
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C.,
L'Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten
Years of Actively Observing the Earth System, B. Am. Meteorol. Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018.
Stephens, G. L.: Radiation Profiles in Extended Water Clouds. II:
Parameterization Schemes, J. Atmos. Sci., 35,
2123–2132, https://doi.org/10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2, 1978.
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M.,
Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J.,
Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang,
Z., and Marchand, R.: CloudSat mission: Performance and early science after
the first year of operation, J. Geophys. Res.-Atmos.,
113, D00A18, https://doi.org/10.1029/2008JD009982, 2008.
Stephens, G. L., L'Ecuyer, T., Forbes, R., Gettelmen, A., Golaz, J.-C.,
Bodas-Salcedo, A., Suzuki, K., Gabriel, P., and Haynes, J.: Dreary state of
precipitation in global models, J. Geophys. Res.-Atmos., 115, D24211, https://doi.org/10.1029/2010JD014532, 2010.
Tanelli, S., Durden, S. L., Im, E., Pak, K. S., Reinke, D. G., Partain, P.,
Haynes, J. M., and Marchand, R. T.: CloudSat's Cloud Profiling Radar After
Two Years in Orbit: Performance, Calibration, and Processing, IEEE
T. Geosci. Remote, 46, 3560–3573, https://doi.org/10.1109/TGRS.2008.2002030, 2008.
Winker, D. M., Vaughan, M. A., Omar, A., 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.
Wood, R. and Hartmann, D. L.: Spatial Variability of Liquid Water Path in
Marine Low Cloud: The Importance of Mesoscale Cellular Convection, J. Climate, 19, 1748–1764, https://doi.org/10.1175/JCLI3702.1, 2006.
Wood, R., Kubar, T. L., and Hartmann, D. L.: Understanding the Importance of
Microphysics and Macrophysics for Warm Rain in Marine Low Clouds. Part II:
Heuristic Models of Rain Formation, J. Atmos. Sci., 66, 2973–2990, https://doi.org/10.1175/2009JAS3072.1, 2009.
Zelinka, M. D., Zhou, C., and Klein, S. A.: Insights from a refined
decomposition of cloud feedbacks, Geophys. Res. Lett., 43,
9259–9269, https://doi.org/10.1002/2016GL069917, 2016.
Zhang, Z. and Platnick, S.: An assessment of differences between cloud
effective particle radius retrievals for marine water clouds from three
MODIS spectral bands, J. Geophys. Res.-Atmos., 116, D20215,
https://doi.org/10.1029/2011JD016216, 2011.
Zuidema, P., Painemal, D., de Szoeke, S., and Fairall, C.: Stratocumulus
Cloud-Top Height Estimates and Their Climatic Implications, J.
Climate, 22, 4652–4666, https://doi.org/10.1175/2009JCLI2708.1, 2009.
Short summary
In order to constrain climate models and better understand how clouds might change in future climates, accurate satellite estimates of cloud liquid water content are important. The satellite currently best suited to this purpose, CloudSat, is not sensitive enough to detect some non-raining low clouds. In this study we show that information from two other satellite instruments, MODIS and CALIOP, can be combined to provide cloud water estimates for many of the clouds that are missed by CloudSat.
In order to constrain climate models and better understand how clouds might change in future...
