Articles | Volume 21, issue 4
https://doi.org/10.5194/acp-21-2765-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-2765-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
24 Feb 2021
Research article | Highlight paper |
| 24 Feb 2021
| 24 Feb 2021
A-Train estimates of the sensitivity of the cloud-to-rainwater ratio to cloud size, relative humidity, and aerosols
Department of Atmospheric Sciences, Texas A&M University, College Station, Texas, USA
Anita D. Rapp
Department of Atmospheric Sciences, Texas A&M University, College Station, Texas, USA
Related authors
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.
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.
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.
Bo Chen, Seth A. Thompson, Brianna H. Matthews, Milind Sharma, Ron Li, Christopher J. Nowotarski, Anita D. Rapp, and Sarah D. Brooks
EGUsphere, https://doi.org/10.5194/egusphere-2024-3363, https://doi.org/10.5194/egusphere-2024-3363, 2024
Short summary
Short summary
This study presents a new method combining ground-based measurements and lidar to track how aerosols are distributed at different heights in the atmosphere. By correcting for humidity, which causes aerosols to grow and intensify the lidar signal, the method provides more accurate aerosol vertical profiles. Our results show that aerosol profiles can vary significantly over short distances. This technique can help improve understanding of aerosol-cloud interactions.
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.
Cited articles
Abel, S. J. and Shipway, B. J.: A comparison of cloud-resolving model
simulations of trade wind cumulus with aircraft observations taken during
RICO, Q. J. Roy. Meteor. Soc., 133, 781–794, https://doi.org/10.1002/qj.55, 2007. a
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness,
Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a, b, c
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud
microphysical parameters using the CloudSat millimeter-wave radar and
temperature, J. Geophys. Res.-Atmos., 114, D00A23, https://doi.org/10.1029/2008JD010049, 2009. a
Bailey, A., Nusbaumer, J., and Noone, D.: Precipitation efficiency derived from isotope ratios in water vapor distinguishes dynamical and microphysical
influences on subtropical atmospheric constituents, J. Geophys. Res.-Atmos., 120, 9119–9137, https://doi.org/10.1002/2015JD023403, 2015. a, b
Battaglia, A., Kollias, P., Dhillon, R., Lamer, K., Khairoutdinov, M., and Watters, D.: Mind the gap – Part 2: Improving quantitative estimates of cloud and rain water path in oceanic warm rain using spaceborne radars, Atmos. Meas. Tech., 13, 4865–4883, https://doi.org/10.5194/amt-13-4865-2020, 2020. a
Benner, T. C. and Curry, J. A.: Characteristics of small tropical cumulus
clouds and their impact on the environment, J. Geophys. Res.-Atmos., 103, 28753–28767, https://doi.org/10.1029/98JD02579, 1998. a, b
Blyth, A. M., Lowenstein, J. H., Huang, Y., Cui, Z., Davies, S., and Carslaw,
K. S.: The production of warm rain in shallow maritime cumulus clouds,
Q. J. Roy. Meteor. Soc., 139, 20–31, https://doi.org/10.1002/qj.1972, 2013. a
Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the heart of
tropical cloud feedback uncertainties in climate models,
Geophys. Res. Lett., 32, L20806, https://doi.org/10.1029/2005GL023851, 2005. a
Boutle, I. A., Abel, S. J., Hill, P. G., and Morcrette, C. J.: Spatial
variability of liquid cloud and rain: observations and microphysical effects,
Q. J. Roy. Meteor. Soc., 140, 583–594,
https://doi.org/10.1002/qj.2140, 2014. a
Brenguier, J.-L. and Chaumat, L.: Droplet Spectra Broadening in Cumulus
Clouds, Part I: Broadening in Adiabatic Cores, J. Atmos. Sci., 58, 628–641,
https://doi.org/10.1175/1520-0469(2001)058<0628:DSBICC>2.0.CO;2, 2001. a
Bretherton, C. S., McCaa, J. R., and Grenier, H.: A New Parameterization for
Shallow Cumulus Convection and Its Application to Marine Subtropical
Cloud-Topped Boundary Layers, Part I: Description and 1D Results,
Mon. Weather Rev., 132, 864–882,
https://doi.org/10.1175/1520-0493(2004)132<0864:ANPFSC>2.0.CO;2, 2004. a
Burnet, F. and Brenguier, J.-L.: The onset of precipitation in warm cumulus
clouds: An observational case-study, Q. J. Roy. Meteor. Soc., 136, 374–381, https://doi.org/10.1002/qj.552, 2010. a, b, c, d
Chen, B. and Liu, C.: Warm Organized Rain Systems over the Tropical Eastern
Pacific, J. Climate, 29, 3403–3422, https://doi.org/10.1175/jcli-d-15-0177.1, 2016. a
Chen, S., Yau, M. K., and Bartello, P.: Turbulence Effects of Collision
Efficiency and Broadening of Droplet Size Distribution in Cumulus Clouds, J. Atmos. Sci., 75, 203–217, https://doi.org/10.1175/JAS-D-17-0123.1, 2018. a
Cho, H.-M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S.,
Di Girolamo, L., C.-Labonnote, L., Cornet, C., Riedi, J., and Holz, R. E.:
Frequency and causes of failed MODIS cloud property retrievals for liquid
phase clouds over global oceans, J. Geophys. Res.-Atmos., 120, 4132–4154, https://doi.org/10.1002/2015JD023161, 2015. a
Chong, M. and Hauser, D.: A Tropical Squall Line Observed during the COPT 81
Experiment in West Africa, Part II: Water Budget, Mon. Weather Rev.,
117, 728–744, https://doi.org/10.1175/1520-0493(1989)117<0728:ATSLOD>2.0.CO;2, 1989. a
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. a
Dagan, G., Koren, I., Altaratz, O., and Heiblum, R. H.: Aerosol effect on the
evolution of the thermodynamic properties of warm convective cloud fields,
Sci. Rep.-UK, 6, 38769, https://doi.org/10.1038/srep38769, 2016. a
Del Genio, A. D., Kovari, W., Yao, M.-S., and Jonas, J.: Cumulus Microphysics
and Climate Sensitivity, J. Climate, 18, 2376–2387,
https://doi.org/10.1175/JCLI3413.1, 2005. a
de Rooy, W. C., Bechtold, P., Fröhlich, K., Hohenegger, C., Jonker, H.,
Mironov, D., Pier Siebesma, A., Teixeira, J., and Yano, J.-I.: Entrainment
and detrainment in cumulus convection: an overview, Q. J. Roy. Meteor. Soc., 139, 1–19, https://doi.org/10.1002/qj.1959, 2013. a, b
Dufresne, J.-L. and Bony, S.: An Assessment of the Primary Sources of Spread of Global Warming Estimates from Coupled Atmosphere–Ocean Models, J. Climate, 21, 5135–5144, https://doi.org/10.1175/2008JCLI2239.1, 2008. a
Franklin, C. N.: The effects of turbulent collision–coalescence on precipitation formation and precipitation-dynamical feedbacks in simulations of stratocumulus and shallow cumulus convection, Atmos. Chem. Phys., 14, 6557–6570, https://doi.org/10.5194/acp-14-6557-2014, 2014. a, b
Gao, W., Liu, L., Li, J., and Lu, C.: The Microphysical Properties of
Convective Precipitation Over the Tibetan Plateau by a Subkilometer
Resolution Cloud-Resolving Simulation, J. Geophys. Res.-Atmos., 123, 3212–3227, https://doi.org/10.1002/2017JD027812,
2018. a
Gerber, H. E., Frick, G. M., Jensen, J. B., and Hudson, J. G.: Entrainment,
Mixing, and Microphysics in Trade-Wind Cumulus,
J. Meteorol. Soc. Jpn., 86A, 87–106, https://doi.org/10.2151/jmsj.86A.87, 2008. a, b
Haynes, J. M., Lecuyer, 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., 114, D00A22,
https://doi.org/10.1029/2008jd009973, 2009. a, b, c
Heus, T. and Jonker, H. J. J.: Subsiding Shells around Shallow Cumulus Clouds, J. Atmos. Sci., 65, 1003–1018,
https://doi.org/10.1175/2007jas2322.1, 2008. a, b, c
Hoffmann, F., Noh, Y., and Raasch, S.: The Route to Raindrop Formation in a
Shallow Cumulus Cloud Simulated by a Lagrangian Cloud Model, J. Atmos. Sci., 74, 2125–2142, https://doi.org/10.1175/JAS-D-16-0220.1, 2017. a
Jiang, H. and Feingold, G.: Effect of aerosol on warm convective clouds:
Aerosol-cloud-surface flux feedbacks in a new coupled large eddy model,
J. Geophys. Res.-Atmos., 111, D01202, https://doi.org/10.1029/2005JD006138, 2006. a
Jolivet, D. and Feijt, A. J.: Quantification of the accuracy of liquid water
path fields derived from NOAA 16 advanced very high resolution radiometer
over three ground stations using microwave radiometers, J. Geophys. Res.-Atmos., 110, D11204, https://doi.org/10.1029/2004JD005205, 2005. a
Jung, E., Albrecht, B. A., Feingold, G., Jonsson, H. H., Chuang, P., and Donaher, S. L.: Aerosols, clouds, and precipitation in the North Atlantic trades observed during the Barbados aerosol cloud experiment – Part 1: Distributions and variability, Atmos. Chem. Phys., 16, 8643–8666, https://doi.org/10.5194/acp-16-8643-2016, 2016a. a
Jung, E., Albrecht, B. A., Sorooshian, A., Zuidema, P., and Jonsson, H. H.: Precipitation susceptibility in marine stratocumulus and shallow cumulus from airborne measurements, Atmos. Chem. Phys., 16, 11395–11413, https://doi.org/10.5194/acp-16-11395-2016, 2016b. a, b
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. a
Kollias, P., Albrecht, B. A., and Marks Jr., F. D.: Cloud radar observations of vertical drafts and microphysics in convective rain, J. Geophys. Res.-Atmos., 108, 4053, https://doi.org/10.1029/2001JD002033, 2003. a
Koren, I., Dagan, G., and Altaratz, O.: From aerosol-limited to invigoration of warm convective clouds, Science, 344, 1143–1146,
https://doi.org/10.1126/science.1252595, 2014. a, b
Korolev, A., Khain, A., Pinsky, M., and French, J.: Theoretical study of mixing in liquid clouds – Part 1: Classical concepts, Atmos. Chem. Phys., 16, 9235–9254, https://doi.org/10.5194/acp-16-9235-2016, 2016. a
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. a
Lebsock, M. D.: Level 2C RAIN-PROFILE Product Process Description and Interface
Control Document, Tech. Rep. D-20308, Jet Propulsion Laboratory, California
Institute of Technology, Pasadena, California, USA, 2018. a
Lebsock, M. D. and L'Ecuyer, T. S.: The retrieval of warm rain from CloudSat,
J. Geophys. Res.-Atmos., 116, D20209, https://doi.org/10.1029/2011JD016076, 2011. a, b, c
Lebsock, M. D. 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. a
Lebsock, M. D., L'Ecuyer, T. S., and Stephens, G. L.: Detecting the Ratio of
Rain and Cloud Water in Low-Latitude Shallow Marine Clouds,
J. Appl. Meteorol. Clim., 50, 419–432, https://doi.org/10.1175/2010JAMC2494.1, 2011. a, b, c
Lebsock, M. D., Morrison, H., and Gettelman, A.: Microphysical implications of
cloud-precipitation covariance derived from satellite remote sensing, J. Geophys. Res.-Atmos., 118, 6521–6533, https://doi.org/10.1002/jgrd.50347, 2013. a
L'Ecuyer, T. S. and Stephens, G. L.: An Estimation-Based Precipitation
Retrieval Algorithm for Attenuating Radars, J. Appl. Meteorol.,
41, 272–285, https://doi.org/10.1175/1520-0450(2002)041<0272:AEBPRA>2.0.CO;2, 2002. a
Lee, H. and Baik, J.-J.: A Physically Based Autoconversion Parameterization, J. Atmos. Sci., 74, 1599–1616, https://doi.org/10.1175/JAS-D-16-0207.1, 2017. a
Li, X., Sui, C.-H., and Lau, K.-M.: Precipitation Efficiency in the Tropical
Deep Convective Regime: A 2D Cloud Resolving Modeling Study, J. Meteorol. Soc. Jpn., 80, 205–212, https://doi.org/10.2151/jmsj.80.205, 2002. a
Lim, K.-S. S. and Hong, S.-Y.: Development of an Effective Double-Moment Cloud Microphysics Scheme with Prognostic Cloud Condensation Nuclei (CCN) for
Weather and Climate Models, Mon. Weather Rev., 138, 1587–1612,
https://doi.org/10.1175/2009MWR2968.1, 2010. a
Liu, J. and Li, Z.: Significant Underestimation in the Optically Based
Estimation of the Aerosol First Indirect Effect Induced by the Aerosol
Swelling Effect, Geophys. Res. Lett., 45, 5690–5699,
https://doi.org/10.1029/2018GL077679, 2018. a, b
Liu, Y. and Daum, P. H.: Parameterization of the Autoconversion Process, Part I: Analytical Formulation of the Kessler-Type Parameterizations, J. Atmos. Sci., 61, 1539–1548, https://doi.org/10.1175/1520-0469(2004)061<1539:POTAPI>2.0.CO;2, 2004. a
Lohmann, U. and Roeckner, E.: Design and performance of a new cloud
microphysics scheme developed for the ECHAM general circulation
model, Clim. Dynam., 12, 557–572, https://doi.org/10.1007/BF00207939, 1996. a
Lu, C., Liu, Y., Niu, S., and Vogelmann, A. M.: Lateral entrainment rate in
shallow cumuli: Dependence on dry air sources and probability density
functions, Geophys. Res. Lett., 39, L20812, https://doi.org/10.1029/2012GL053646,
2012. a
Lutsko, N. J. and Cronin, T. W.: Increase in Precipitation Efficiency With
Surface Warming in Radiative-Convective Equilibrium,
J. Adv. Model. Earth Sy., 10, 2992–3010, https://doi.org/10.1029/2018MS001482, 2018. a, b, c
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. a, b, c
Medeiros, B. and Stevens, B.: Revealing differences in GCM representations of
low clouds, Clim. Dynam., 36, 385–399, https://doi.org/10.1007/s00382-009-0694-5,
2011. a
Michel Flores, J., Bar-Or, R. Z., Bluvshtein, N., Abo-Riziq, A., Kostinski, A., Borrmann, S., Koren, I., Koren, I., and Rudich, Y.: Absorbing aerosols at high relative humidity: linking hygroscopic growth to optical properties, Atmos. Chem. Phys., 12, 5511–5521, https://doi.org/10.5194/acp-12-5511-2012, 2012. a
Mieslinger, T., Horvath, A., Buehler, S. A., and Sakradzija, M.: The Dependence of Shallow Cumulus Macrophysical Properties on Large-Scale Meteorology as Observed in ASTER Imagery, J. Geophys. Res.-Atmos., 124,
11477–11505, https://doi.org/10.1029/2019JD030768, 2019. a
Minor, H. A., Rauber, R. M., Göke, S., and Di Girolamo, L.: Trade Wind Cloud
Evolution Observed by Polarization Radar: Relationship to Giant Condensation
Nuclei Concentrations and Cloud Organization, J. Atmos. Sci., 68, 1075–1096, https://doi.org/10.1175/2010JAS3675.1, 2011. a
Mitrescu, C., L'Ecuyer, T., Haynes, J., Miller, S., and Turk, J.: CloudSat
Precipitation Profiling Algorithm–Model Description, J. Appl. Meteorol. Clim., 49, 991–1003, https://doi.org/10.1175/2009JAMC2181.1, 2010. a
Morrison, H., Curry, J. A., and Khvorostyanov, V. I.: A New Double-Moment
Microphysics Parameterization for Application in Cloud and Climate Models,
Part I: Description, J. Atmos. Sci., 62, 1665–1677, https://doi.org/10.1175/JAS3446.1, 2005. a
Moser, D. H. and Lasher-Trapp, S.: The Influence of Successive Thermals on
Entrainment and Dilution in a Simulated Cumulus Congestus, J. Atmos. Sci., 74, 375–392, https://doi.org/10.1175/JAS-D-16-0144.1, 2017. a, b, c, d
Nam, C., Bony, S., Dufresne, J.-L., and Chepfer, H.: The “too few, too
bright” tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421, 2012. a, b
Neubauer, D., Christensen, M. W., Poulsen, C. A., and Lohmann, U.: Unveiling aerosol–cloud interactions – Part 2: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data, Atmos. Chem. Phys., 17, 13165–13185, https://doi.org/10.5194/acp-17-13165-2017, 2017. a
Pinsky, M., Khain, A., and Korolev, A.: Theoretical analysis of mixing in liquid clouds – Part 3: Inhomogeneous mixing, Atmos. Chem. Phys., 16, 9273–9297, https://doi.org/10.5194/acp-16-9273-2016, 2016a. a
Pinsky, M., Khain, A., Korolev, A., and Magaritz-Ronen, L.: Theoretical investigation of mixing in warm clouds – Part 2: Homogeneous mixing, Atmos. Chem. Phys., 16, 9255–9272, https://doi.org/10.5194/acp-16-9255-2016, 2016b. a
Platnick, S. and Valero, F. P. J.: A Validation of a Satellite Cloud Retrieval during ASTEX, J. Atmos. Sci., 52, 2985–3001,
https://doi.org/10.1175/1520-0469(1995)052<2985:AVOASC>2.0.CO;2, 1995. a
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, 2003. a
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. a, b
Rennó, N. O., Emanuel, K. A., and Stone, P. H.: Radiative-convective model
with an explicit hydrologic cycle: 1. Formulation and sensitivity to model
parameters, J. Geophys. Res.-Atmos., 99,
14429–14441, https://doi.org/10.1029/94JD00020, 1994. a
Romps, D. M.: An Analytical Model for Tropical Relative Humidity, J. Climate, 27, 7432–7449, https://doi.org/10.1175/JCLI-D-14-00255.1, 2014. a
Ruiz-Arias, J. A., Dudhia, J., Gueymard, C. A., and Pozo-Vázquez, D.: Assessment of the Level-3 MODIS daily aerosol optical depth in the context of surface solar radiation and numerical weather modeling, Atmos. Chem. Phys., 13, 675–692, https://doi.org/10.5194/acp-13-675-2013, 2013. a
Saleeby, S. M., Herbener, S. R., van den Heever, S. C., and L'Ecuyer, T.:
Impacts of Cloud Droplet–Nucleating Aerosols on Shallow Tropical
Convection, J. Atmos. Sci., 72, 1369–1385,
https://doi.org/10.1175/JAS-D-14-0153.1, 2015. a, b
Schmeissner, T., Shaw, R. A., Ditas, J., Stratmann, F., Wendisch, M., and
Siebert, H.: Turbulent Mixing in Shallow Trade Wind Cumuli: Dependence on
Cloud Life Cycle, J. Atmos. Sci., 72, 1447–1465,
https://doi.org/10.1175/jas-d-14-0230.1, 2015. a, b
Seethala, C. and Horvath, A.: Global assessment of AMSR-E and MODIS cloud
liquid water path retrievals in warm oceanic clouds, J. Geophys. Res.-Atmos., 115, D13202, https://doi.org/10.1029/2009JD012662, 2010. a, b
Seifert, A. and Onishi, R.: Turbulence effects on warm-rain formation in precipitating shallow convection revisited, Atmos. Chem. Phys., 16, 12127–12141, https://doi.org/10.5194/acp-16-12127-2016, 2016. a
Seifert, A., Nuijens, L., and Stevens, B.: Turbulence effects on warm-rain
autoconversion in precipitating shallow convection, Q. J. Roy. Meteor. Soc., 136, 1753–1762, https://doi.org/10.1002/qj.684, 2010. a, b
Short, D. A. and Nakamura, K.: TRMM Radar Observations of Shallow Precipitation
over the Tropical Oceans, J. Climate, 13, 4107–4124,
https://doi.org/10.1175/1520-0442(2000)013<4107:TROOSP>2.0.CO;2, 2000. a
Squires, P.: The Microstructure and Colloidal Stability of Warm Clouds, Tellus, 10, 256–261, https://doi.org/10.1111/j.2153-3490.1958.tb02011.x, 1958. a
Stevens, D. E., Ackerman, A. S., and Bretherton, C. S.: Effects of domain size and numerical resolution on the simulation of shallow cumulus convection, J. Atmos. Sci., 59, 3285–3301,
https://doi.org/10.1175/1520-0469(2002)059<3285:EODSAN>2.0.CO;2, 2002. a
Su, W., Schuster, G. L., Loeb, N. G., Rogers, R. R., Ferrare, R. A., Hostetler, C. A., Hair, J. W., and Obland, M. D.: Aerosol and cloud interaction observed from high spectral resolution lidar data, J. Geophys. Res.-Atmos., 113, D24202, https://doi.org/10.1029/2008JD010588, 2008. a
Sui, C.-H., Li, X., Yang, M.-J., and Huang, H.-L.: Estimation of
Oceanic Precipitation Efficiency in Cloud
Models, J. Atmos. Sci., 62, 4358–4370, https://doi.org/10.1175/JAS3587.1, 2005. a, b
Sui, C.-H., Li, X., and Yang, M.-J.: On the Definition of Precipitation
Efficiency, J. Atmos. Sci., 64, 4506–4513,
https://doi.org/10.1175/2007JAS2332.1, 2007. a, b
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. a
Tao, W.-K., Johnson, D., Shie, C.-L., and Simpson, J.: The Atmospheric Energy Budget and Large-Scale Precipitation Efficiency of Convective Systems during TOGA COARE, GATE, SCSMEX, and ARM: Cloud-Resolving Model Simulations, J. Atmos. Sci., 61, 2405–2423,
https://doi.org/10.1175/1520-0469(2004)061<2405:TAEBAL>2.0.CO;2, 2004. a
Tian, Y. and Kuang, Z.: Dependence of entrainment in shallow cumulus convection on vertical velocity and distance to cloud edge, Geophys. Res. Lett., 43, 4056–4065, https://doi.org/10.1002/2016gl069005, 2016. a, b, c
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in
Large-Scale Models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a
Trivej, P. and Stevens, B.: The Echo Size Distribution of Precipitating Shallow Cumuli, J. Atmos. Sci., 67, 788–804,
https://doi.org/10.1175/2009JAS3178.1, 2010. a, b, c
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8,
1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974. a, b
van Zanten, M. C., Stevens, B., Nuijens, L., Siebesma, A. P., Ackerman, A. S., Burnet, F., Cheng, A., Couvreux, F., Jiang, H., Khairoutdinov, M., Kogan, Y., Lewellen, D. C., Mechem, D., Nakamura, K., Noda, A., Shipway, B. J., Slawinska, J., Wang, S., and Wyszogrodzki, A.: Controls on precipitation and cloudiness in simulations of trade-wind cumulus as observed during RICO, J. Adv. Model. Earth Sy., 3, M06001, https://doi.org/10.1029/2011MS000056, 2011. a
Vial, J., Dufresne, J.-L., and Bony, S.: On the interpretation of inter-model
spread in CMIP5 climate sensitivity estimates, Clim. Dynam., 41,
3339–3362, https://doi.org/10.1007/s00382-013-1725-9, 2013. a
Waliser, D. E. and Gautier, C.: A Satellite-derived Climatology of the ITCZ, J. Climate, 6, 2162–2174,
https://doi.org/10.1175/1520-0442(1993)006<2162:ASDCOT>2.0.CO;2, 1993. a
Wang, Y., Chen, Y., Fu, Y., and Liu, G.: Identification of precipitation onset based on Cloudsat observations,
J. Quant. Spectrosc. Ra., 188, 142–147, https://doi.org/10.1016/j.jqsrt.2016.06.028, 2017. a, b
Watson, C. D., Smith, R. B., and Nugent, A. D.: Processes Controlling
Precipitation in Shallow, Orographic, Trade Wind Convection, J. Atmos. Sci., 72, 3051–3072, https://doi.org/10.1175/JAS-D-14-0333.1, 2015. a, b
Witkowski, M., Vane, D., Livermore, T., Rokey, M., Barthuli, M., Gravseth,
I. J., Pieper, B., Rodzinak, A., Silva, S., and Woznick, P.: CloudSat anomaly
recovery and operational lessons learned, Tech. Rep., Jet Propulsion
Laboratory, National Aeronautics and Space Administration, Pasadena, California, USA, 2012. a
Witte, M. K., Morrison, H., Jensen, J. B., Bansemer, A., and Gettelman, A.: On the Covariability of Cloud and Rain Water as a Function of Length Scale, J. Atmos. Sci., 76, 2295–2308, https://doi.org/10.1175/JAS-D-19-0048.1, 2019. a
Wood, R. and Bretherton, C. S.: Boundary Layer Depth, Entrainment, and
Decoupling in the Cloud-Capped Subtropical and Tropical Marine Boundary
Layer, J. Climate, 17, 3576–3588,
https://doi.org/10.1175/1520-0442(2004)017<3576:BLDEAD>2.0.CO;2, 2004. a, b
Wyant, M. C., Khairoutdinov, M., and Bretherton, C. S.: Climate sensitivity and cloud response of a GCM with a superparameterization, Geophys. Res. Lett., 33, L06714, https://doi.org/10.1029/2005GL025464, 2006. a
Wyszogrodzki, A. A., Grabowski, W. W., Wang, L.-P., and Ayala, O.: Turbulent collision-coalescence in maritime shallow convection, Atmos. Chem. Phys., 13, 8471–8487, https://doi.org/10.5194/acp-13-8471-2013, 2013.
a
Zhang, G. and McFarlane, N. A.: Sensitivity of climate simulations to the
parameterization of cumulus convection in the Canadian climate centre general
circulation model, Atmos. Ocean, 33, 407–446,
https://doi.org/10.1080/07055900.1995.9649539, 1995. a
Zhao, M.: An Investigation of the Connections among Convection, Clouds, and
Climate Sensitivity in a Global Climate Model, J. Climate, 27,
1845–1862, https://doi.org/10.1175/JCLI-D-13-00145.1, 2014. a
Short summary
We use satellite observations of shallow cumulus clouds to investigate the influence of cloud size on the ratio of cloud water path to rainwater (WRR) in different environments. For a fixed temperature and relative humidity, WRR increases with cloud size, but it varies little with aerosols. These results imply that increasing WRR with rising temperature relates not only to deeper clouds but also to more frequent larger clouds.
We use satellite observations of shallow cumulus clouds to investigate the influence of cloud...
Altmetrics
Final-revised paper
Preprint