Abstract

Cloud droplet effective radius (CER) and number concentration (CDNC) are two critical microphysical properties of liquid water clouds, which play essential roles in the Earth’s radiative energy balance and atmospheric hydrological cycle. Even though many satellite remote sensing techniques have been developed to obtain these two properties, the observations are often limited to the daytime. In this study, a method to estimate CER and CDNC of liquid water clouds over global ocean area during both daytime and nighttime from CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) measurements is presented. The size sensitivity of the dual-wavelength (532 nm & 1064 nm) layer-integrated attenuated backscattering signals from CALIOP is checked and information content for liquid water cloud CER retrieval is found. Taking use of the artificial neural network (ANN) technique, the CER and then the CDNC are estimated from CALIOP by combining the polarization ratio and the dual wavelength attenuated backscattering signals. The comparisons with CER and CDNC estimated from MODIS (Moderate Resolution Imaging Spectroradiometer) during daytime demonstrate the feasibility of this new method. Both the daytime and nighttime CER and CDNC derived from CALIOP observations are presented in this paper and the day-night variation of liquid water cloud is discussed which would provide useful day-night variation of liquid water cloud properties.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2019 (1)

M. Vaughan, A. Garnier, D. Josset, M. Avery, K. P. Lee, Z. Liu, W. Hunt, J. Pelon, Y. Hu, S. Burton, J. Hair, J. L. Tackett, B. Getzewich, J. Kar, and S. Rodier, “CALIPSO lidar calibration at 1064 nm: version 4 algorithm,” Atmos. Meas. Tech. 12(1), 51–82 (2019).
[Crossref]

2018 (8)

V. Noel, H. Chepfer, M. Chiriaco, and J. Yorks, “The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station,” Atmos. Chem. Phys. 18(13), 9457–9473 (2018).
[Crossref]

X. Yang, L. Zhou, C. Zhao, and J. Yang, “Impact of aerosols on tropical cyclone-induced precipitation over the mainland of China,” Clim. Change 148(1-2), 173–185 (2018).
[Crossref]

J. Li, B. Jian, J. Huang, Y. Hu, C. Zhao, K. Kawamoto, S. Liao, and M. Wu, “Long-term variation of cloud droplet number concentrations from space-based Lidar,” Remote Sens. Environ. 213, 144–161 (2018).
[Crossref]

G. Stephens, D. Winker, J. Pelon, C. Trepte, D. Vane, C. Yuhas, T. L’Ecuyer, and M. Lebsock, “CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System,” Bull. Am. Meteorol. Soc. 99(3), 569–581 (2018).
[Crossref]

D. J. Miller, Z. Zhang, S. Platnick, A. S. Ackerman, F. Werner, C. Cornet, and K. Knobelspiesse, “Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator,” Atmos. Meas. Tech. 11(6), 3689–3715 (2018).
[Crossref]

D. P. Grosvenor, O. Sourdeval, P. Zuidema, A. Ackerman, M. D. Alexandrov, R. Bennartz, R. Boers, B. Cairns, J. C. Chiu, M. Christensen, H. Deneke, M. Diamond, G. Feingold, A. Fridlind, A. Hünerbein, C. Knist, P. Kollias, A. Marshak, D. McCoy, D. Merk, D. Painemal, J. Rausch, D. Rosenfeld, H. Russchenberg, P. Seifert, K. Sinclair, P. Stier, D. B. van, M. Wendisch, F. Werner, R. Wood, Z. Zhang, and J. Quaas, “Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives,” Rev. Geophys. 56(2), 409–453 (2018).
[Crossref]

S. A. Young, M. A. Vaughan, A. Garnier, J. L. Tackett, J. D. Lambeth, and K. A. Powell, “Extinction and optical depth retrievals for CALIPSO's Version 4 data release,” Atmos. Meas. Tech. 11(10), 5701–5727 (2018).
[Crossref]

B. J. Getzewich, M. A. Vaughan, W. H. Hunt, M. A. Avery, K. A. Powell, J. L. Tackett, D. M. Winker, J. Kar, K. P. Lee, and T. D. Toth, “CALIPSO lidar calibration at 532 nm: version 4 daytime algorithm,” Atmos. Meas. Tech. 11(11), 6309–6326 (2018).
[Crossref]

2017 (2)

J. Yin and A. Porporato, “Diurnal cloud cycle biases in climate models,” Nat. Commun. 8(1), 2269 (2017).
[Crossref]

R. Bennartz and J. Rausch, “Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations,” Atmos. Chem. Phys. 17(16), 9815–9836 (2017).
[Crossref]

2016 (3)

P. Minnis, G. Hong, S. Sun-Mack, W. L. Smith, Y. Chen, and S. D. Miller, “Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method,” J. Geophys. Res.: Atmos. 121(9), 4907–4932 (2016).
[Crossref]

P. Ceppi, D. T. McCoy, and D. L. Hartmann, “Observational evidence for a negative shortwave cloud feedback in middle to high latitudes,” Geophys. Res. Lett. 43(3), 1331–1339 (2016).
[Crossref]

J. H. Seinfeld, C. Bretherton, K. S. Carslaw, H. Coe, P. J. DeMott, E. J. Dunlea, G. Feingold, S. Ghan, A. B. Guenther, R. Kahn, I. Kraucunas, S. M. Kreidenweis, M. J. Molina, A. Nenes, J. E. Penner, K. A. Prather, V. Ramanathan, V. Ramaswamy, P. J. Rasch, A. R. Ravishankara, D. Rosenfeld, G. Stephens, and R. Wood, “Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system,” Proc. Natl. Acad. Sci. 113(21), 5781–5790 (2016).
[Crossref]

2014 (3)

D. Rosenfeld, S. Sherwood, R. Wood, and L. Donner, “Climate Effects of Aerosol-Cloud Interactions,” Science 343(6169), 379–380 (2014).
[Crossref]

S. Zeng, J. Riedi, C. R. Trepte, D. M. Winker, and Y. X. Hu, “Study of global cloud droplet number concentration with A-Train satellites,” Atmos. Chem. Phys. 14(14), 7125–7134 (2014).
[Crossref]

X. Dong, B. Xi, and P. Wu, “Investigation of the Diurnal Variation of Marine Boundary Layer Cloud Microphysical Properties at the Azores,” J. Clim. 27(23), 8827–8835 (2014).
[Crossref]

2013 (4)

M. W. Christensen, G. L. Stephens, and M. D. Lebsock, “Exposing biases in retrieved low cloud properties from CloudSat: A guide for evaluating observations and climate data,” J. Geophys. Res.: Atmos. 118(21), 12,120–12,131 (2013).
[Crossref]

T. J. Garrett and C. Zhao, “Ground-based remote sensing of thin clouds in the Arctic,” Atmos. Meas. Tech. 6(5), 1227–1243 (2013).
[Crossref]

C. F. Schueler, T. F. Lee, and S. D. Miller, “VIIRS constant spatial-resolution advantages,” Int. J. Remote Sens. 34(16), 5761–5777 (2013).
[Crossref]

A. Walther, A. K. Heidinger, and S. Miller, “The expected performance of cloud optical and microphysical properties derived from Suomi NPP VIIRS day/night band lunar reflectance,” J. Geophys. Res.: Atmos. 118(23), 13,230–13,240 (2013).
[Crossref]

2012 (1)

C. Zhao, S. Xie, S. A. Klein, A. Protat, M. D. Shupe, S. A. McFarlane, J. M. Comstock, J. Delanoë, M. Deng, M. Dunn, R. J. Hogan, D. Huang, M. P. Jensen, G. G. Mace, R. McCoy, E. J. O’Connor, D. D. Turner, and Z. Wang, “Toward understanding of differences in current cloud retrievals of ARM ground-based measurements,” J. Geophys. Res.: Atmos. 117(D10), 1–21 (2012).
[Crossref]

2011 (2)

P. Minnis, S. Sun-Mack, D. F. Young, P. W. Heck, D. P. Garber, Y. Chen, D. A. Spangenberg, R. F. Arduini, Q. Z. Trepte, W. L. Smith, J. K. Ayers, S. C. Gibson, W. F. Miller, G. Hong, V. Chakrapani, Y. Takano, K. Liou, Y. Xie, and P. Yang, “CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms,” IEEE Trans. Geosci. Electron. 49(11), 4374–4400 (2011).
[Crossref]

J. Li, Y. Hu, J. Huang, K. Stamnes, Y. Yi, and S. Stamnes, “A new method for retrieval of the extinction coefficient of water clouds by using the tail of the CALIOP signal,” Atmos. Chem. Phys. 11(6), 2903–2916 (2011).
[Crossref]

2009 (3)

W. H. Hunt, D. M. Winker, M. A. Vaughan, K. A. Powell, P. L. Lucker, and C. Weimer, “CALIPSO lidar description and performance assessment,” J. Atmos. Oceanic Technol. 26(7), 1214–1228 (2009).
[Crossref]

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO mission and CALIOP data processing algorithms,” J. Atmos. Oceanic Technol. 26(11), 2310–2323 (2009).
[Crossref]

C. Hoose, J. E. Kristjánsson, T. Iversen, A. Kirkevåg, Ø. Seland, and A. Gettelman, “Constraining cloud droplet number concentration in GCMs suppresses the aerosol indirect effect,” Geophys. Res. Lett. 36(12), L12807 (2009).
[Crossref]

2008 (3)

K. Sassen, Z. Wang, and D. Liu, “Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements,” J. Geophys. Res.: Atmos. 113, D00A12 (2008).
[Crossref]

S. Tanelli, S. L. Durden, E. Im, K. S. Pak, D. G. Reinke, P. Partain, J. M. Haynes, and R. T. Marchand, “CloudSat's Cloud Profiling Radar After Two Years in Orbit: Performance, Calibration, and Processing,” IEEE Trans. Geosci. Electron. 46(11), 3560–3573 (2008).
[Crossref]

D. C. Leon, Z. Wang, and D. Liu, “Climatology of drizzle in marine boundary layer clouds based on 1 year of data from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO),” J. Geophys. Res.: Atmos. 113, D00A14 (2008).
[Crossref]

2007 (2)

R. Bennartz, “Global assessment of marine boundary layer cloud droplet number concentration from satellite,” J. Geophys. Res.: Atmos. 112(D2), D02201 (2007).
[Crossref]

Y. Hu, M. Vaughan, C. McClain, M. Behrenfeld, H. Maring, D. Anderson, S. Sun-Mack, D. Flittner, J. Huang, B. Wielicki, P. Minnis, C. Weimer, C. Trepte, and R. Kuehn, “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(12), 3353–3359 (2007).
[Crossref]

2006 (2)

T. J. Garrett and C. Zhao, “Increased Arctic cloud longwave emissivity associated with pollution from mid-latitudes,” Nature 440(7085), 787–789 (2006).
[Crossref]

Y. Hu, Z. Liu, D. Winker, M. Vaughan, V. Noel, L. Bissonnette, G. Roy, and M. McGill, “Simple relation between lidar multiple scattering and depolarization for water clouds,” Opt. Lett. 31(12), 1809–1811 (2006).
[Crossref]

2002 (1)

R. Wood, C. S. Bretherton, and D. L. Hartmann, “Diurnal cycle of liquid water path over the subtropical and tropical oceans,” Geophys. Res. Lett. 29(23), 7 (2002).
[Crossref]

1999 (1)

C. M. R. Platt, D. M. Winker, M. A. Vaughan, and S. D. Miller, “Backscatter-to-Extinction Ratios in the Top Layers of Tropical Mesoscale Convective Systems and in Isolated Cirrus from LITE Observations,” J. Appl. Meteorol. 38(9), 1330–1345 (1999).
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1998 (1)

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D. P. Grosvenor, O. Sourdeval, P. Zuidema, A. Ackerman, M. D. Alexandrov, R. Bennartz, R. Boers, B. Cairns, J. C. Chiu, M. Christensen, H. Deneke, M. Diamond, G. Feingold, A. Fridlind, A. Hünerbein, C. Knist, P. Kollias, A. Marshak, D. McCoy, D. Merk, D. Painemal, J. Rausch, D. Rosenfeld, H. Russchenberg, P. Seifert, K. Sinclair, P. Stier, D. B. van, M. Wendisch, F. Werner, R. Wood, Z. Zhang, and J. Quaas, “Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives,” Rev. Geophys. 56(2), 409–453 (2018).
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Appl. Opt. (1)

Atmos. Chem. Phys. (5)

V. Noel, H. Chepfer, M. Chiriaco, and J. Yorks, “The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station,” Atmos. Chem. Phys. 18(13), 9457–9473 (2018).
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[Crossref]

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[Crossref]

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[Crossref]

Atmos. Meas. Tech. (5)

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Figures (8)

Fig. 1.
Fig. 1. Two-dimensional histogram of CER-Rη. The black dashed line gives the linear regression results.
Fig. 2.
Fig. 2. Two-dimensional histogram of CER-S532. The black dashed line gives the linear regression results.
Fig. 3.
Fig. 3. Two-dimensional histogram of CER-χ. The black dashed line gives the linear regression results.
Fig. 4.
Fig. 4. Two-dimensional histogram of the CERs retrieved from MODIS and CALIOP for the four-month matched data samples. The red line is the 1-1 line, black dots are the mean values and vertical shorter lines are the corresponding standard deviations.
Fig. 5.
Fig. 5. Geographical distributions of the CER estimated from MODIS (a), CALIOP daytime measurements (b), and CALIOP nighttime measurements(c). The data used to generate this figure are both from year of 2015. (units: µm)
Fig. 6.
Fig. 6. Monthly variations of MODIS (solid line) and CALIOP daytime(dashed line) and nighttime(dot-dash line) CERs of year 2015 for four different regions: (a)Californian (10°N-30°N; 150°W-110°W), (b)Namibian (30°S-0°S; 25°W-15°E), (c)Canarian (10°N-30°N; 45°W-20°W) and (d) Peruvian (30°S-0°S; 120°W-70°W). These four regions are corresponding to the four regions shown in blue box of Fig. 5.
Fig. 7.
Fig. 7. Two-dimensional histogram of the CDNCs estimated form CALIOP using the CER retrieved from the ANN and CER from MODIS (the Hu’s method). The data used are the four-month matched data samples. The red line is the 1-1 line, black dots are the mean values and vertical shorter lines are the corresponding standard deviations.
Fig. 8.
Fig. 8. Geographical distributions of the CDNC derived from MODIS (a), CALIOP daytime measurements (b), and CALIOP nighttime measurements(c). The data used to generate this figure are both from year of 2015. (units: cm-3)

Tables (1)

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Table 1. The day-night variations of the optical and micro-physical properties of the cloud layers for four classic subtropical stratocumulus regions

Equations (10)

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γ = z t o p z b a s e β ( z ) d z = z t o p z b a s e [ β ( z ) + β ( z ) ] d z ,
γ = [ 1 T 2 ] / [ 1 T 2 ] 2 η S 2 η S ,
η = 1 / 1 2 S γ 2 S γ .
η = [ ( 1 δ ) / ( 1 δ ) ( 1 + δ ) ( 1 + δ ) ] 2 ,
α r e 1 / 1 3 3 = 1 + 135 δ 2 / δ 2 ( 1 δ ) 2 ( 1 δ ) 2 ,
n ( r ) = N Γ ( a ) r m ( r r m ) a 1 exp ( r r m ) ,
N = ( a + 2 ) 2 ( a + 1 ) a 1 + 135 δ 2 / δ 2 ( 1 δ ) 2 ( 1 δ ) 2 2 π r e 5 / 5 3 3 .
R η = 0.01768 r e + 0.9351 ,
S 532 = 0.372 r e + 23.76 ,
y = F ( 1 n W i x i + b ) ,

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