Articles | Volume 17, issue 12
https://doi.org/10.5194/amt-17-3679-2024
© 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/amt-17-3679-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah, USA
Related authors
Gerald G. Mace, Sally Benson, Peter Gombert, and Tiffany Smallwood
EGUsphere, https://doi.org/10.5194/egusphere-2025-2075, https://doi.org/10.5194/egusphere-2025-2075, 2025
Short summary
Short summary
The amount of sunlight reflected by marine boundary layer clouds in the Eastern North Atlantic do not change due to a decrease in aerosol caused by reduced sulphur in shipping fuel because adjustments to liquid water path offset the decease in cloud droplet number concentration.
Ruhi S. Humphries, Melita D. Keywood, Jason P. Ward, James Harnwell, Simon P. Alexander, Andrew R. Klekociuk, Keiichiro Hara, Ian M. McRobert, Alain Protat, Joel Alroe, Luke T. Cravigan, Branka Miljevic, Zoran D. Ristovski, Robyn Schofield, Stephen R. Wilson, Connor J. Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Greg M. McFarquhar, Scott D. Chambers, Alastair G. Williams, and Alan D. Griffiths
Atmos. Chem. Phys., 23, 3749–3777, https://doi.org/10.5194/acp-23-3749-2023, https://doi.org/10.5194/acp-23-3749-2023, 2023
Short summary
Short summary
Observations of aerosols in pristine regions are rare but are vital to constraining the natural baseline from which climate simulations are calculated. Here we present recent seasonal observations of aerosols from the Southern Ocean and contrast them with measurements from Antarctica, Australia and regionally relevant voyages. Strong seasonal cycles persist, but striking differences occur at different latitudes. This study highlights the need for more long-term observations in remote regions.
Gerald G. Mace, Sally Benson, Ruhi Humphries, Peter M. Gombert, and Elizabeth Sterner
Atmos. Chem. Phys., 23, 1677–1685, https://doi.org/10.5194/acp-23-1677-2023, https://doi.org/10.5194/acp-23-1677-2023, 2023
Short summary
Short summary
The number of cloud droplets per unit volume is a significantly important property of clouds that controls their reflective properties. Computer models of the Earth's atmosphere and climate have low skill at predicting the reflective properties of Southern Ocean clouds. Here we investigate the properties of those clouds using satellite data and find that the cloud droplet number and cloud albedo in the Southern Ocean are related to the oceanic phytoplankton abundance near Antarctica.
Yuli Liu and Gerald G. Mace
Atmos. Meas. Tech., 15, 927–944, https://doi.org/10.5194/amt-15-927-2022, https://doi.org/10.5194/amt-15-927-2022, 2022
Short summary
Short summary
We propose a suite of Bayesian algorithms for synergistic radar and radiometer retrievals to evaluate the next-generation NASA Cloud, Convection and Precipitation (CCP) observing system. The algorithms address pixel-level retrievals using active-only, passive-only, and synergistic active–passive observations. Novel techniques in developing synergistic algorithms are presented. Quantitative assessments of the CCP observing system's capability in retrieving ice cloud microphysics are provided.
Ruhi S. Humphries, Melita D. Keywood, Sean Gribben, Ian M. McRobert, Jason P. Ward, Paul Selleck, Sally Taylor, James Harnwell, Connor Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Alain Protat, Simon P. Alexander, and Greg McFarquhar
Atmos. Chem. Phys., 21, 12757–12782, https://doi.org/10.5194/acp-21-12757-2021, https://doi.org/10.5194/acp-21-12757-2021, 2021
Short summary
Short summary
The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. In this paper we present a statistical summary of the latitudinal gradient of aerosol and cloud condensation nuclei concentrations obtained from five voyages spanning the Southern Ocean.
Gerald G. Mace, Sally Benson, Peter Gombert, and Tiffany Smallwood
EGUsphere, https://doi.org/10.5194/egusphere-2025-2075, https://doi.org/10.5194/egusphere-2025-2075, 2025
Short summary
Short summary
The amount of sunlight reflected by marine boundary layer clouds in the Eastern North Atlantic do not change due to a decrease in aerosol caused by reduced sulphur in shipping fuel because adjustments to liquid water path offset the decease in cloud droplet number concentration.
Ruhi S. Humphries, Melita D. Keywood, Jason P. Ward, James Harnwell, Simon P. Alexander, Andrew R. Klekociuk, Keiichiro Hara, Ian M. McRobert, Alain Protat, Joel Alroe, Luke T. Cravigan, Branka Miljevic, Zoran D. Ristovski, Robyn Schofield, Stephen R. Wilson, Connor J. Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Greg M. McFarquhar, Scott D. Chambers, Alastair G. Williams, and Alan D. Griffiths
Atmos. Chem. Phys., 23, 3749–3777, https://doi.org/10.5194/acp-23-3749-2023, https://doi.org/10.5194/acp-23-3749-2023, 2023
Short summary
Short summary
Observations of aerosols in pristine regions are rare but are vital to constraining the natural baseline from which climate simulations are calculated. Here we present recent seasonal observations of aerosols from the Southern Ocean and contrast them with measurements from Antarctica, Australia and regionally relevant voyages. Strong seasonal cycles persist, but striking differences occur at different latitudes. This study highlights the need for more long-term observations in remote regions.
Gerald G. Mace, Sally Benson, Ruhi Humphries, Peter M. Gombert, and Elizabeth Sterner
Atmos. Chem. Phys., 23, 1677–1685, https://doi.org/10.5194/acp-23-1677-2023, https://doi.org/10.5194/acp-23-1677-2023, 2023
Short summary
Short summary
The number of cloud droplets per unit volume is a significantly important property of clouds that controls their reflective properties. Computer models of the Earth's atmosphere and climate have low skill at predicting the reflective properties of Southern Ocean clouds. Here we investigate the properties of those clouds using satellite data and find that the cloud droplet number and cloud albedo in the Southern Ocean are related to the oceanic phytoplankton abundance near Antarctica.
Yuli Liu and Gerald G. Mace
Atmos. Meas. Tech., 15, 927–944, https://doi.org/10.5194/amt-15-927-2022, https://doi.org/10.5194/amt-15-927-2022, 2022
Short summary
Short summary
We propose a suite of Bayesian algorithms for synergistic radar and radiometer retrievals to evaluate the next-generation NASA Cloud, Convection and Precipitation (CCP) observing system. The algorithms address pixel-level retrievals using active-only, passive-only, and synergistic active–passive observations. Novel techniques in developing synergistic algorithms are presented. Quantitative assessments of the CCP observing system's capability in retrieving ice cloud microphysics are provided.
Ruhi S. Humphries, Melita D. Keywood, Sean Gribben, Ian M. McRobert, Jason P. Ward, Paul Selleck, Sally Taylor, James Harnwell, Connor Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Alain Protat, Simon P. Alexander, and Greg McFarquhar
Atmos. Chem. Phys., 21, 12757–12782, https://doi.org/10.5194/acp-21-12757-2021, https://doi.org/10.5194/acp-21-12757-2021, 2021
Short summary
Short summary
The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. In this paper we present a statistical summary of the latitudinal gradient of aerosol and cloud condensation nuclei concentrations obtained from five voyages spanning the Southern Ocean.
Cited articles
Albrecht, B. A., Fairall, C. W., Thomson, D. W., and White, A. B.: Surface-based remote sensing of the observed and the adiabatic liquid water content of stratocumuls clouds, Geophys. Res. Lett., 17, 89–92, 1990.
Austin, R. T. and Stephens, G. L.: Retrieval of stratus cloud microphysical parameters using millimeter-wave radar and visible optical depth in preparation for CloudSat: 1. algorithm formulation. J. Geophys. Res.-Atmos., 106, 28233–28242, https://doi.org/10.1029/2000jd000293, 2001.
Baumgardner, D., Abel, S. J., Axisa, D., Cotton, R., Crosier, J., Field, P., Gurganus, C., Heymsfield, A., Korolev, A., Kramer, M., Lawson, P., McFarquhar, G., Ulanowski, Z., and Um, J.: Cloud ice properties: In-situ measurements and challenges. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Mon., 58, 9.1–9.23, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0011.1, 2017.
Cadeddu, M. and Tuftedal, M.: Microwave Radiometer (MWRLOS), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1999490, 2024.
Campbell, J. R., Hlavka, D. L., Welton, E. J., Flynn, C. J., Turner, D. D., Spinhirne, J. D., Scott, V. S., and Hwang, I. H.: Full-Time, Eye-Safe Cloud and Aerosol Lidar Observation at Atmospheric Radiation Measurement Program Sites: Instruments and Data Processing, J. Atmos. Ocean. Tech., 19, 431–442, https://doi.org/10.1175/1520-0426(2002)019<0431:FTESCA>2.0.CO;2, 2002.
Cooper, S. J., L'Ecuyer, T. S., Gabriel, P., Baran, A. J., and Stephens, G. L.: Objective assessment of the information content of visible and infrared radiance measurements for cloud microphysical property retrievals over the global oceans. part II: Ice clouds, J. Appl. Meteorol., 45, 42–62, https://doi.org/10.1175/jam2327.1, 2006.
Cromwell, E. and Reynolds, M.: Marine Surface Meteorological Instrumentation (AADMET), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1593144, 2024.
Frisch, A. S., Feingold, G., Fairall, C. W., Uttal, T., and Snider, J. B.: On cloud radar and microwave radiometer measurements of stratus cloud liquid water profiles, J. Geophys. Res., 103, 23195–23197, 1998.
Gettelman, A. and Morrison, H.: Advanced Two-moment bulk microphysics for global models. part I: Off-line tests and comparison with other schemes, J. Climate, 28, 1268–1287, https://doi.org/10.1175/jcli-d-14-00102.1, 2015.
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., 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.
Hu, Y., Vaughan, M., McClain, C., Behrenfeld, M., Maring, H., Anderson, D., Sun-Mack, S., Flittner, D., Huang, J., Wielicki, B., Minnis, P., Weimer, C., Trepte, C., and Kuehn, R.: Global statistics of liquid water content and effective number concentration of water clouds over ocean derived from combined CALIPSO and MODIS measurements, Atmos. Chem. Phys., 7, 3353–3359, https://doi.org/10.5194/acp-7-3353-2007, 2007.
Hu, Y., Winker, D., Vaughan, M., Lin, B., Omar, A., Trepte, C., Flittner, D., Yang, P., Nasiri, S. L., Baum, B., Holz, R., Sun, W., Liu, Z., Wang, Z., Young, S., Stamnes, K., Huang, J., and Kuehn, R.: Calipso/Caliop Cloud Phase Discrimination algorithm, J. Atmos. Ocean. Tech., 26, 2293–2309, https://doi.org/10.1175/2009jtecha1280.1, 2009.
Humphries, R. S., Keywood, M. D., Gribben, S., McRobert, I. M., Ward, J. P., Selleck, P., Taylor, S., Harnwell, J., Flynn, C., Kulkarni, G. R., Mace, G. G., Protat, A., Alexander, S. P., and McFarquhar, G.: Southern Ocean latitudinal gradients of cloud condensation nuclei, Atmos. Chem. Phys., 21, 12757–12782, https://doi.org/10.5194/acp-21-12757-2021, 2021.
Humphries, R. S., Keywood, M. D., Ward, J. P., Harnwell, J., Alexander, S. P., Klekociuk, A. R., Hara, K., McRobert, I. M., Protat, A., Alroe, J., Cravigan, L. T., Miljevic, B., Ristovski, Z. D., Schofield, R., Wilson, S. R., Flynn, C. J., Kulkarni, G. R., Mace, G. G., McFarquhar, G. M., Chambers, S. D., Williams, A. G., and Griffiths, A. D.: Measurement report: Understanding the seasonal cycle of Southern Ocean aerosols, Atmos. Chem. Phys., 23, 3749–3777, https://doi.org/10.5194/acp-23-3749-2023, 2023.
Keeler, E., Burk, K., and Kyrouac, J.: Balloon-Borne Sounding System (SONDEWNPN), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1595321, 2024.
Kollias, P., Puigdomènech Treserras, B., and Protat, A.: Calibration of the 2007–2017 record of Atmospheric Radiation Measurements cloud radar observations using CloudSat, Atmos. Meas. Tech., 12, 4949–4964, https://doi.org/10.5194/amt-12-4949-2019, 2019.
Koontz, A., C. Flynn, E. Andrews, J. Uin, O. Enekwizu, C. Hayes, and C. Salwen: Cloud Condensation Nuclei Particle Counter (AOSCCN1COLAVG), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1255094, 2024.
Lawson, P., Gurganus, C., Woods, S., and Bruintjes, R.: Aircraft observations of Cumulus microphysics ranging from the tropics to midlatitudes: Implications for a “new” Secondary ice process, J. Atmos. Sci., 74, 2899–2920, https://doi.org/10.1175/jas-d-17-0033.1, 2017.
Lawson, R. P., O'Connor, D., Zmarzly, P., Weaver, K., Baker, B., Mo, Q., and Jonsson, H.: The 2D-S (stereo) probe: Design and preliminary tests of a new airborne high-speed, high-resolution particle imaging probe, J. Atmos. Ocean., Tech., 23, 1462–1477, https://doi.org/10.1175/JTECH1927.1, 2006.
L'Ecuyer, T. S., Gabriel, P., Leesman, K., Cooper, S. J., and Stephens, G. L.: Objective assessment of the information content of visible and infrared radiance measurements for cloud microphysical property retrievals over the global oceans. part I: Liquid clouds, J. Appl. Meteorol. Clim., 45, 20–41, https://doi.org/10.1175/jam2326.1, 2006.
Lewis, J. R., Campbell, J. R., Stewart, S. A., Tan, I., Welton, E. J., and Lolli, S.: Determining cloud thermodynamic phase from the polarized Micro Pulse Lidar, Atmos. Meas. Tech., 13, 6901–6913, https://doi.org/10.5194/amt-13-6901-2020, 2020.
Li, J., Hu, Y., Huang, J., Stamnes, K., Yi, Y., and Stamnes, S.: A new method for retrieval of the extinction coefficient of water clouds by using the tail of the CALIOP signal, Atmos. Chem. Phys., 11, 2903–2916, https://doi.org/10.5194/acp-11-2903-2011, 2011.
Lindenmaier, I., Feng, Y.-C., Bharadwaj, N., Johnson, K., Isom, B., Hardin, J., Matthews, A., Wendler, T., Melo de Castro, V., and Rocque, M.: Marine W-Band (95 GHz) ARM Cloud Radar (MWACR), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1973911, 2024.
Maahn, M., Turner, D. D., Löhnert, U., Posselt, D. J., Ebell, K., Mace, G. G., and Comstock, J. M.: Optimal estimation retrievals and their uncertainties: What every atmospheric scientist should know, B. Am. Meteorol. Soc., 101, E1512–E1523, https://doi.org/10.1175/bams-d-19-0027.1, 2020.
Mace, G. G., Protat, A., Humphries, R. S., Alexander, S. P., McRobert, I. M., Ward, J., Selleck, P., Keywood, M., and McFarquhar, G. M.: Southern Ocean cloud properties derived from Capricorn and Marcus Data, J. Geophys. Res., 126, e2020JD033368, https://doi.org/10.1029/2020jd033368, 2021.
Mather J.: ARM User Facility 2020 Decadal Vision, edited by: Jundt, R., Stafford, R., and Larsen, S., U.S. Department of Energy, DOE/SC-ARM-20-014, https://doi.org/10.2172/1782812, 2021.
McFarquhar, G. M., Bretherton, C. S., Marchand, R., Protat, A., DeMott, P. J., Alexander, S. P., Roberts, G. C., Twohy, C. H., Toohey, D., Siems, S., Huang, Y., Wood, R., Rauber, R. M., Lasher-Trapp, S., Jensen, J., Stith, J. L., Mace, J., Um, J., Järvinen, E., Schnaiter, M., Gettelman, A., Sanchez, K. J., McCluskey, C. S., Russell, L. M., McCoy, I. L., Atlas, R. L., Bardeen,C. G., Moore, K. A., Hill, T. C. J., Humphries, R. S., Keywood, M. D., Ristovski, Z., Cravigan, L., Schofield, R., Fairall, C., Mallet, M. D., Kreidenweis, S. M., Rainwater, B., D'Alessandro, J., Wang, Y., Wu, W., Saliba, G., Levin, E. J. T., Ding, S., Lang, F., Truong, S. C. H., Wolff, C., Haggerty, J., Harvey, M. J., Klekociuk, A. R., and McDonald, A.: Observations of clouds, aerosols, precipitation, and surface radiation over the Southern Ocean: An overview of Capricorn, Marcus, MICRE, and Socrates, B. Am. Meteorol. Soc., 102, E894–E928, https://doi.org/10.1175/bams-d-20-0132.1, 2021.
McCoy, I. L., Bretherton, C. S., Wood, R., Twohy, C. H., Gettelman, A., Bardeen, C. G., and Toohey, D. W: Influences of recent particle formation on Southern Ocean aerosol variability and low cloud properties, J. Geophys. Res., 126, e2020JD033529, https://doi.org/10.1029/2020jd033529, 2021.
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.
Miller, M. A., Jensen, M. P., and Clothiaux, E. E.: Diurnal cloud and thermodynamic variations in the stratocumulus transition regime: A case study using in situ and remote sensors, J. Atmos. Sci., 55, 2294–2310, https://doi.org/10.1175/1520-0469(1998)055<2294:dcatvi>2.0.co;2, 1998.
Muradyan, P., Cromwell, E., Koontz, A., Coulter, R., Flynn, C., Ermold, B., and O'Brien, J.: Micropulse Lidar (MPLPOLFS), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1320657, 2024.
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.
O'Connor, E. J., Illingworth, A. J., and Hogan, R. J.: A technique for autocalibration of cloud lidar, J. Atmos. Ocean. Tech., 21, 777–786, 2004.
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.
Platnick, S., Ackerman, S. A., King, M. D., Meyer, K., Menzel, W. P., Holz, R. E., Baum, B. A., and Yang, P.: MODIS atmosphere L2 cloud product (06_L2), Terra, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MOD06_L2.006, 2015a.
Platnick, S., Ackerman, S. A., King, M. D., Meyer, K., Menzel, W. P., Holz, R. E., Baum, B. A., and Yang, P.: MODIS atmosphere L2 cloud product (06_L2), Aqua, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MYD06_L2.006, 2015b.
Platt, C. M.: Lidar observation of a mixed-phase altostratus cloud, J. Appl. Meteorol., 16, 339–345, https://doi.org/10.1175/1520-0450(1977)016<0339:looamp>2.0.co;2, 1977.
Platt, C. M., Winker, D. M., Vaughan, M. A., and Miller, S. D.: Backscatter-to-extinction ratios in the top layers of tropical mesoscale convective systems and in isolated cirrus from Lite Observations, J. Appl. Meteorol., 38, 1330–1345, https://doi.org/10.1175/1520-0450(1999)038<1330:bterit>2.0.co;2, 1999.
Posselt, D. J. and Mace, G. G.: MCMC-based assessment of the error characteristics of a surface-based combined radar–Passive Microwave Cloud Property Retrieval, J. Appl. Meteorol. Clim., 53, 2034–2057, https://doi.org/10.1175/jamc-d-13-0237.1, 2014.
Protat, A., CSIRO, and Marine National Facility: RV Investigator BOM Atmospheric Data Overview (2016 onwards), v2, CSIRO, Data Collection [data set], https://doi.org/10.25919/5f688fcc97166, 2020.
Rodgers, C. D.: : Inverse Methods for Atmospheric Sounding, Theory and Practice, World Scientific Publishing Co. Ltd., Singapore, https://doi.org/10.1142/3171, 2000.
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2005.
Sivaraman, C., Flynn, D., Riihimaki, L., Comstock, J., and Zhang, D.: Cloud mask from Micropulse Lidar (30SMPLCMASK1ZWANG), Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1508389, 2020.
Stephens, G. L.: Radiation profiles in extended water clouds. I: Theory, J. Atmos. Sci., 35, 2111–2122, https://doi.org/10.1175/1520-0469(1978)035<2111:rpiewc>2.0.co;2, 1978.
Stuefer, M., Stuefer, M., and Wong, T.: Camera That Monitors a Site Area (CAMSEASTATE), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1971078, 2024.
Thompson, G. and Eidhammer, T.: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone, J. Atmos. Sci., 71, 3636–3658, https://doi.org/10.1175/jas-d-13-0305.1, 2014.
Turner, D. D., Kneifel, S., and Cadeddu, M. P.: An improved liquid water absorption model at microwave frequencies for supercooled liquid water clouds, J. Atmos. Ocean. Tech., 33, 33–44, https://doi.org/10.1175/jtech-d-15-0074.1, 2016.
Twohy, C. H., DeMott, P. J., Russell, L. M., Toohey, D. W., Rainwater, B., Geiss, R., Sanchez, K. J., Lewis, S., Roberts, G. C., Humphries, R. S., McCluskey, C. S., Moore, K. A., Selleck, P. W., Keywood, M. D., Ward, J. P., and McRobert, I. M.: Cloudnucleating particles over the Southern Ocean in a changing climate, Earths Future, 9, e2020EF001673, https://doi.org/10.1029/2020EF001673, 2021.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974.
UCAR/NCAR – Earth Observing Laboratory: NSF/NCAR GV HIAPER Raw 2D-S Imagery, Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://doi.org/10.26023/M3KV-1SS0-DF10, 2018.
UCAR/NCAR – Earth Observing Laboratory: SOCRATES: High Rate (HRT – 25 sps) Navigation, State Parameter, and Microphysics Flight-Level Data, Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://doi.org/10.26023/K5VQ-K6KY-W610, 2019.
Walton, S.: Navigational Location and Attitude (NAV), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1974348, 2024.
Zhang, D.: MWR Retrievals (MWRRET1LILJCLOU), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1027369, 2024.
Zhao, Y., Mace, G. G., and Comstock, J. M.: The occurrence of particle size distribution bimodality in Midlatitude Cirrus as inferred from ground-based remote sensing data, J. Atmos. Sci., 68, 1162–1177, https://doi.org/10.1175/2010jas3354.1, 2011.
Short summary
The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding...