Articles | Volume 23, issue 4
https://doi.org/10.5194/acp-23-2729-2023
© 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/acp-23-2729-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Feb 2023
Research article |
| 27 Feb 2023
| 27 Feb 2023
Establishment of an analytical model for remote sensing of typical stratocumulus cloud profiles under various precipitation and entrainment conditions
Huazhe Shang
State Key Laboratory of Remote Sensing Science, The Aerospace
Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Univ. Lille, CNRS, UMR 8518 Laboratoire d'Optique Atmosphérique (LOA), 59000 Lille, France
Souichiro Hioki
Univ. Lille, CNRS, UMR 8518 Laboratoire d'Optique Atmosphérique (LOA), 59000 Lille, France
Guillaume Penide
Univ. Lille, CNRS, UMR 8518 Laboratoire d'Optique Atmosphérique (LOA), 59000 Lille, France
Céline Cornet
Univ. Lille, CNRS, UMR 8518 Laboratoire d'Optique Atmosphérique (LOA), 59000 Lille, France
State Key Laboratory of Remote Sensing Science, The Aerospace
Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Jérôme Riedi
Univ. Lille, CNRS, UMR 8518 Laboratoire d'Optique Atmosphérique (LOA), 59000 Lille, France
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The empirical model has very good quality of MC data fitting for all considered cases.
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Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
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The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Souichiro Hioki, Jérôme Riedi, and Mohamed S. Djellali
Atmos. Meas. Tech., 14, 1801–1816, https://doi.org/10.5194/amt-14-1801-2021, https://doi.org/10.5194/amt-14-1801-2021, 2021
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This research estimates the magnitude of a motion-induced error in the measurement of polarimetric state of light by a planned instrument on a future satellite. We discovered that the motion-induced error can not be cancelled out by spatiotemporal averaging, but it can be predicted from the along-track change of the intensity of light. With the estimated statistics and the simulation model, this research paves a way to provide pixel-level quality information in the future satellite products.
Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, Valery Shcherbakov, Céline Cornet, Julien Delanoë, Yahya Gour, Olivier Jourdan, Sandra Banson, and Edouard Bray
Atmos. Meas. Tech., 14, 199–221, https://doi.org/10.5194/amt-14-199-2021, https://doi.org/10.5194/amt-14-199-2021, 2021
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Spaceborne lidar and radar are suitable tools to investigate cloud vertical properties on a global scale. This paper presents the McRALI code that provides simulations of lidar and radar signals from the EarthCARE mission. Regarding radar signals, cloud heterogeneity induces a severe bias in velocity estimates. Regarding lidar signals, multiple scattering is not negligible. Our results also give some insight into the reliability of lidar signal modeling using independent column approximation.
Cited articles
Alexandrov, M. D., Miller, D. J., Rajapakshe, C., Fridlind, A., van
Diedenhoven, B., Cairns, B., Ackerman, A. S., and Zhang, Z.: Vertical
profiles of droplet size distributions derived from cloud-side observations
by the research scanning polarimeter: Tests on simulated data, J. Atmos. Res., 239, 104924, https://doi.org/10.1016/j.atmosres.2020.104924, 2020a.
Alexandrov, M. D., Miller, D. J., Rajapakshe, C., Fridlind, A., van
Diedenhoven, B., Cairns, B., Ackerman, A. S., and Zhang, Z.: Vertical
profiles of droplet size distributions derived from cloud-side observations
by the research scanning polarimeter: Tests on simulated data,
Atmos. Res., 239, 104924, https://doi.org/10.1016/j.atmosres.2020.104924,
2020b.
Alexandrov, M. D., Cairns, B., Sinclair, K., Wasilewski, A. P., Ziemba, L.,
Crosbie, E., Moore, R., Hair, J., Scarino, A. J., and Hu, Y.: Retrievals of
cloud droplet size from the research scanning polarimeter data: Validation
using in situ measurements, Remote Sens. Environ., 210, 76–95, 2018.
Arabas, S., Pawlowska, H., and Grabowski, W.: Effective radius and droplet
spectral width from in-situ aircraft observations in trade-wind cumuli
during RICO, Geophys. Res. Lett., 36, L11803, https://doi.org/10.1029/2009GL038257, 2009.
Bréon, F.-M. and Goloub, P.: Cloud droplet effective radius from
spaceborne polarization measurements, Geophys. Res. Lett., 25,
1879–1882, https://doi.org/10.1029/98gl01221, 1998.
Breon, F. M. and Doutriaux-Boucher, M.: A comparison of cloud droplet radii
measured from space, IEEE T. Geosci. Remote, 43,
1796–1805, https://doi.org/10.1109/TGRS.2005.852838, 2005.
Burns, D., Kollias, P., Tatarevic, A., Battaglia, A., and Tanelli, S.: The
performance of the EarthCARE Cloud Profiling Radar in marine stratiform
clouds, J. Geophys. Res.-Atmos., 121, 14525–514537,
https://doi.org/10.1002/2016JD025090, 2016.
Carey, L. D., Niu, J., Yang, P., Kankiewicz, J. A., Larson, V. E., and Haar,
T. H. V.: The Vertical Profile of Liquid and Ice Water Content in
Midlatitude Mixed-Phase Altocumulus Clouds,
J. Appl. Meteorol. Clim., 47, 2487–2495, https://doi.org/10.1175/2008jamc1885.1, 2008.
Chang, F. L. and Li, Z.: Estimating the vertical variation of cloud droplet
effective radius using multispectral near-infrared satellite measurements,
J. Geophys. Res.-Atmos., 107, AAC 7-1–AAC 7-12, 2002.
Chang, F. L. and Li, Z.: Retrieving vertical profiles of water-cloud droplet
effective radius: Algorithm modification and preliminary application, J. Geophys. Res.-Atmos., 108, 4763, https://doi.org/10.1029/2003JD003906, 2003.
Chen, R., Wood, R., Li, Z., Ferraro, R., and Chang, F.-L.: Studying the
vertical variation of cloud droplet effective radius using ship and space-borne remote sensing data, J. Geophys. Res., 113, D00A02,
https://doi.org/10.1029/2007jd009596, 2008.
Chen, Y., Chen, G., Cui, C., Zhang, A., Wan, R., Zhou, S., Wang, D., and Fu, Y.: Retrieval of the vertical evolution of the cloud effective radius from the Chinese FY-4 (Feng Yun 4) next-generation geostationary satellites, Atmos. Chem. Phys., 20, 1131–1145, https://doi.org/10.5194/acp-20-1131-2020, 2020.
Colorado State University: Cloud Processes Research Group, https://vandenheever.atmos.colostate.edu/vdhpage/rams/indexregister.php, last access: 22 February 2023.
Cornet, C., C.-Labonnote, L., Waquet, F., Szczap, F., Deaconu, L., Parol, F., Vanbauce, C., Thieuleux, F., and Riédi, J.: Cloud heterogeneity on cloud and aerosol above cloud properties retrieved from simulated total and polarized reflectances, Atmos. Meas. Tech., 11, 3627–3643, https://doi.org/10.5194/amt-11-3627-2018, 2018.
Dadashazar, H., Corral, A. F., Crosbie, E., Dmitrovic, S., Kirschler, S., McCauley, K., Moore, R., Robinson, C., Schlosser, J. S., Shook, M., Thornhill, K. L., Voigt, C., Winstead, E., Ziemba, L., and Sorooshian, A.: Organic enrichment in droplet residual particles relative to out of cloud over the northwestern Atlantic: analysis of airborne ACTIVATE data, Atmos. Chem. Phys., 22, 13897–13913, https://doi.org/10.5194/acp-22-13897-2022, 2022.
Davis, A. B., Merlin, G., Cornet, C., Labonnote, L. C., Riédi, J.,
Ferlay, N., Dubuisson, P., Min, Q., Yang, Y., and Marshak, A.: Cloud
information content in EPIC/DSCOVR's oxygen A- and B-band channels: An
optimal estimation approach,
J. Quant. Spectrosc. Ra., 216, 6–16, https://doi.org/10.1016/j.jqsrt.2018.05.007,
2018.
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.
Dong, X. and Mace, G. G.: Profiles of Low-Level Stratus Cloud Microphysics
Deduced from Ground-Based Measurements,
J. Atmos. Ocean. Tech., 20, 42–53, https://doi.org/10.1175/1520-0426(2003)020<0042:Pollsc>2.0.Co;2, 2003.
Donovan, D. P. and van Lammeren, A. C. A. P.: Cloud effective particle size
and water content profile retrievals using combined lidar and radar
observations: 1. Theory and examples, J. Geophys. Res.-Atmos., 106, 27425–27448, https://doi.org/10.1029/2001JD900243, 2001.
Fougnie, B., Marbach, T., Lacan, A., Lang, R., Schlüssel, P., Poli, G.,
Munro, R., and Couto, A. B.: The multi-viewing multi-channel
multi-polarisation imager – Overview of the 3MI polarimetric mission for
aerosol and cloud characterization, J. Quant. Spectrosc. Ra., 219, 23–32, https://doi.org/10.1016/j.jqsrt.2018.07.008,
2018.
Frisch, A. S., Fairall, C. W., and Snider, J. B.: Measurement of Stratus
Cloud and Drizzle Parameters in ASTEX with a Kα-Band Doppler Radar
and a Microwave Radiometer, J. Atmos. Sci., 52, 2788–2799,
https://doi.org/10.1175/1520-0469(1995)052<2788:Moscad>2.0.Co;2, 1995.
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, 2018a.
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, 2018b.
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.
Kokhanovsky, A. and Rozanov, V. V.: Droplet vertical sizing in warm clouds using passive optical measurements from a satellite, Atmos. Meas. Tech., 5, 517–528, https://doi.org/10.5194/amt-5-517-2012, 2012.
Kollias, P., Bharadwaj, N., Widener, K., Jo, I., and Johnson, K.: Scanning
ARM Cloud Radars. Part I: Operational Sampling Strategies, J. Atmos. Ocean. Tech., 31, 569–582, https://doi.org/10.1175/jtech-d-13-00044.1,
2014.
Lawson, R. P., Baker, B. A., Schmitt, C. G., and Jensen, T. L.: An overview
of microphysical properties of Arctic clouds observed in May and July 1998
during FIRE ACE, J. Geophys. Res., 106, 14989–15014, https://doi.org/10.1029/2000JD900789, 2001.
Letu, H., Yang, K., Nakajima, T. Y., Ishimoto, H., Nagao, T. M., Riedi, J.,
Baran, A. J., Ma, R., Wang, T., Shang, H., Khatri, P., Chen, L., Shi, C.,
and Shi, J.: High-resolution retrieval of cloud microphysical properties and
surface solar radiation using Himawari-8/AHI next-generation geostationary
satellite, Remote Sens. Environ., 239, 111583,
https://doi.org/10.1016/j.rse.2019.111583, 2020.
Lhermitte, R. M.: Cloud and precipitation remote sensing at 94 GHz, IEEE T. Geosci. Remote, 26, 207–216, https://doi.org/10.1109/36.3024,
1988.
Lu, C., Niu, S., Liu, Y., and Vogelmann, A. M.: Empirical relationship
between entrainment rate and microphysics in cumulus clouds, Geophys. Res.
Lett., 40, 2333–2338, https://doi.org/10.1002/grl.50445, 2013.
Lu, C., Liu, Y., Zhang, G. J., Wu, X., Endo, S., Cao, L., Li, Y., and Guo,
X.: Improving Parameterization of Entrainment Rate for Shallow Convection
with Aircraft Measurements and Large-Eddy Simulation, J. Atmos. Sci., 73, 761–773, https://doi.org/10.1175/jas-d-15-0050.1, 2016.
Lu, M.-L., Conant, W. C., Jonsson, H. H., Varutbangkul, V., Flagan, R. C.,
and Seinfeld, J. H.: The Marine Stratus/Stratocumulus Experiment (MASE):
Aerosol-cloud relationships in marine stratocumulus, J. Geophys. Res.-Atmos., 112, D10209, https://doi.org/10.1029/2006JD007985, 2007.
Lu, M.-L., Sorooshian, A., Jonsson, H. H., Feingold, G., Flagan, R. C., and
Seinfeld, J. H.: Marine stratocumulus aerosol-cloud relationships in the
MASE-II experiment: Precipitation susceptibility in eastern Pacific marine
stratocumulus, J. Geophys. Res.-Atmos., 114, D24203, https://doi.org/10.1029/2009JD012774, 2009.
Mace, G. G. and Sassen, K.: A constrained algorithm for retrieval of
stratocumulus cloud properties using solar radiation, microwave radiometer,
and millimeter cloud radar data, J. Geophys. Res., 105, 29099–29108,
https://doi.org/10.1029/2000JD900403, 2000.
Magaritz-Ronen, L., Pinsky, M., and Khain, A.: Drizzle formation in stratocumulus clouds: effects of turbulent mixing, Atmos. Chem. Phys., 16, 1849–1862, https://doi.org/10.5194/acp-16-1849-2016, 2016.
Marbach, T., Phillips, P., Lacan, A., and Schlüssel, P.: The
Multi-Viewing, -Channel, -Polarisation Imager (3MI) of the EUMETSAT Polar
System – Second Generation (EPS-SG) dedicated to aerosol characterisation,
SPIE Remote Sensing, Proc. SPIE, 8889, 88890I, https://doi.org/10.1117/12.2028221, 2013.
Merlin, G., Riedi, J., Labonnote, L. C., Cornet, C., Davis, A. B., Dubuisson, P., Desmons, M., Ferlay, N., and Parol, F.: Cloud information content analysis of multi-angular measurements in the oxygen A-band: application to 3MI and MSPI, Atmos. Meas. Tech., 9, 4977–4995, https://doi.org/10.5194/amt-9-4977-2016, 2016.
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.
Nagao, T. M., Suzuki, K., and Nakajima, T. Y.: Interpretation of
Multiwavelength-Retrieved Droplet Effective Radii for Warm Water Clouds in
Terms of In-Cloud Vertical Inhomogeneity by Using a Spectral Bin
Microphysics Cloud Model, J. Atmos. Sci., 70,
2376–2392, https://doi.org/10.1175/jas-d-12-0225.1, 2013.
Nakajima, T. and King, M. D.: Determination of the optical-thickness and
effective particle radius of clouds from reflected solar-radiation
measurements, J. Atmos. Sci., 47, 1878–1893,
https://doi.org/10.1175/1520-0469(1990)047<1878:dotota>2.0.co;2, 1990.
Nakajima, T. Y., Suzuki, K., and Stephens, G. L.: Droplet Growth in Warm
Water Clouds Observed by the A-Train. Part II: A Multisensor View, J. Atmos. Sci., 67, 1897–1907, https://doi.org/10.1175/2010jas3276.1, 2010.
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., 116, D24206, https://doi.org/10.1029/2011jd016155, 2011.
Pawlowska, H., Grabowski, W. W., and Brenguier, J.-L.: Observations of the
width of cloud droplet spectra in stratocumulus, Geophys. Res. Lett., 33, L19810, https://doi.org/10.1029/2006GL026841, 2006.
Penide, G., Shang, H., Hioki, S., Cornet, C., Letu, H., and Riedi, J.: Simulations of stratocumulus cloud fields using RAMS model, Zenodo [data set], https://doi.org/10.5281/zenodo.7578991, 2023.
Pielke, R. A., Cotton, W. R., Walko, R. L., Tremback, C. J., Lyons, W. A.,
Grasso, L. D., Nicholls, M. E., Moran, M. D., Wesley, D. A., Lee, T. J., and
Copeland, J. H.: A comprehensive meteorological modeling system – RAMS,
Meteorol. Atmos. Phys., 49, 69–91, https://doi.org/10.1007/BF01025401, 1992.
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.: Approximations for horizontal photon transport in cloud remote
sensing problems, J. Quant. Spectrosc. Ra., 68, 75–99, https://doi.org/10.1016/S0022-4073(00)00016-9, 2001.
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N.,
Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang,
P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical
Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE T. Geosci. Remote, 55, 502–525,
https://doi.org/10.1109/TGRS.2016.2610522, 2017.
Rémillard, J., Kollias, P., and Szyrmer, W.: Radar-radiometer retrievals of cloud number concentration and dispersion parameter in nondrizzling marine stratocumulus, Atmos. Meas. Tech., 6, 1817–1828, https://doi.org/10.5194/amt-6-1817-2013, 2013.
Roebeling, R. A., Placidi, S., Donovan, D. P., Russchenberg, H. W. J., and
Feijt, A. J.: Validation of liquid cloud property retrievals from SEVIRI
using ground-based observations, Geophys. Res. Lett., 35, L05814, https://doi.org/10.1029/2007GL032115, 2008.
Rosenfeld, D. and Lensky, I. M.: Satellite-based insights into precipitation
formation processes in continental and maritime convective clouds, B. Am. Meteorol. Soc., 79, 2457–2476,
https://doi.org/10.1175/1520-0477(1998)079<2457:sbiipf>2.0.co;2, 1998.
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.
Saleeby, S. M. and Cotton, W. R.: A Large-Droplet Mode and Prognostic Number
Concentration of Cloud Droplets in the Colorado State University Regional
Atmospheric Modeling System (RAMS). Part I: Module Descriptions and
Supercell Test Simulations, J. Appl. Meteorol., 43, 182–195,
https://doi.org/10.1175/1520-0450(2004)043<0182:Almapn>2.0.Co;2, 2004.
Saleeby, S. M. and van den Heever, S. C.: Developments in the CSU-RAMS
Aerosol Model: Emissions, Nucleation, Regeneration, Deposition, and
Radiation, J. Appl. Meteorol. Clim., 52, 2601–2622,
https://doi.org/10.1175/jamc-d-12-0312.1, 2013.
SciPy: Fundamental algorithms for scientific computing in Python, https://scipy.org, last access: 22 February 2023.
Shang, H., Letu, H., Bréon, F.-M., Riedi, J., Ma, R., Wang, Z.,
Nakajima, T. Y., Wang, Z., and Chen, L.: An improved algorithm of cloud
droplet size distribution from POLDER polarized measurements, Remote Sens. Environ., 228, 61–74, 2019.
Shepherd, J. M., Pierce, H., and Negri, A. J.: Rainfall Modification by
Major Urban Areas: Observations from Spaceborne Rain Radar on the TRMM
Satellite, J. Appl. Meteorol., 41, 689–701,
https://doi.org/10.1175/1520-0450(2002)041<0689:Rmbmua>2.0.Co;2, 2002.
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., and Mitrescu, C.: THE CLOUDSAT
MISSION AND THE A-TRAIN: A New Dimension of Space-Based Observations of
Clouds and Precipitation, B. Am. Meteorol. Soc.,
83, 1771–1790, https://doi.org/10.1175/bams-83-12-1771, 2002.
Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist,
B., Brenguier, J.-L., Bretherton, C. S., Burnet, F., Campos, T., Chai, S.,
Faloona, I., Friesen, D., Haimov, S., Laursen, K., Lilly, D. K., Loehrer, S.
M., Malinowski, S. P., Morley, B., Petters, M. D., Rogers, D. C., Russell,
L., Savic-Jovcic, V., Snider, J. R., Straub, D., Szumowski, M. J., Takagi,
H., Thornton, D. C., Tschudi, M., Twohy, C., Wetzel, M., and van Zanten, M.
C.: Dynamics and Chemistry of Marine Stratocumulus – DYCOMS-II, B. Am. Meteorol. Soc., 84, 579–594, 2003.
Suzuki, K., Nakajima, T. Y., and Stephens, G. L.: Particle Growth and Drop
Collection Efficiency of Warm Clouds as Inferred from Joint CloudSat and MODIS
Observations, J. Atmos. Sci., 67, 3019–3032,
https://doi.org/10.1175/2010jas3463.1, 2010.
van der Dussen, J. J., de Roode, S. R., Dal Gesso, S., and Siebesma, A. P.:
An LES model study of the influence of the free tropospheric thermodynamic
conditions on the stratocumulus response to a climate perturbation,
J. Adv. Model. Earth Sy., 7, 670–691,
https://doi.org/10.1002/2014MS000380, 2015.
Wang, J., Daum, P. H., Yum, S. S., Liu, Y., Senum, G. I., Lu, M.-L.,
Seinfeld, J. H., and Jonsson, H.: Observations of marine stratocumulus
microphysics and implications for processes controlling droplet spectra:
Results from the Marine Stratus/Stratocumulus Experiment, J. Geophys. Res., 114, https://doi.org/10.1029/2008jd011035, 2009.
Wood, R.: CLOUDS AND FOG | Stratus and Stratocumulus, in:
Encyclopedia of Atmospheric Sciences (Second Edition), edited by: North, G.
R., Pyle, J., and Zhang, F., Academic Press, Oxford, 196–200,
https://doi.org/10.1016/B978-0-12-382225-3.00396-0, 2015.
Wu, P., Dong, X., Xi, B., Tian, J., and Ward, D. M.: Profiles of MBL Cloud
and Drizzle Microphysical Properties Retrieved From Ground-Based
Observations and Validated by Aircraft In Situ Measurements Over the Azores,
J. Geophys. Res.-Atmos., 125, e2019JD032205,
https://doi.org/10.1029/2019JD032205, 2020.
Xu, X., Lu, C., Liu, Y., Luo, S., Zhou, X., Endo, S., Zhu, L., and Wang, Y.: Influences of an entrainment–mixing parameterization on numerical simulations of cumulus and stratocumulus clouds, Atmos. Chem. Phys., 22, 5459–5475, https://doi.org/10.5194/acp-22-5459-2022, 2022.
Zhang, Z., Ackerman, A. S., Feingold, G., Platnick, S., Pincus, R., and Xue,
H.: Effects of cloud horizontal inhomogeneity and drizzle on remote sensing
of cloud droplet effective radius: Case studies based on large-eddy
simulations, J. Geophys. Res.-Atmos., 117, D19208, https://doi.org/10.1029/2012jd017655, 2012.
Zhao, C., Qiu, Y., Dong, X., Wang, Z., Peng, Y., Li, B., Wu, Z., and Wang,
Y.: Negative Aerosol-Cloud re Relationship From Aircraft Observations Over
Hebei, China, Earth Space Sci., 5, 19–29, https://doi.org/10.1002/2017EA000346, 2018.
Short summary
We find that cloud profiles can be divided into four prominent patterns, and the frequency of these four patterns is related to intensities of cloud-top entrainment and precipitation. Based on these analyses, we further propose a cloud profile parameterization scheme allowing us to represent these patterns. Our results shed light on how to facilitate the representation of cloud profiles and how to link them to cloud entrainment or precipitating status in future remote-sensing applications.
We find that cloud profiles can be divided into four prominent patterns, and the frequency of...
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