A new method for retrieval of the extinction coefﬁcient of water … · 2020. 7. 31. · J. Li et al.: New method for retrieval of the extinction coefﬁcient of water clouds 2905

Atmos. Chem. Phys., 11, 2903–2916, 2011www.atmos-chem-phys.net/11/2903/2011/doi:10.5194/acp-11-2903-2011© Author(s) 2011. CC Attribution 3.0 License.

AtmosphericChemistry

and Physics

A new method for retrieval of the extinction coefficient of waterclouds by using the tail of the CALIOP signal

J. Li 1,2, Y. Hu3, J. Huang1, K. Stamnes2, Y. Yi4, and S. Stamnes2

1Key Laboratory for Semi-Arid Climate Change of the Ministry of Education College of Atmospheric Sciences,Lanzhou University, Lanzhou, China2Dept. of Physics and Engineering, Stevens Institute of Tech., Hoboken, NJ, USA3Climate Science Branch, NASA Langley Research Center, Hampton, Virginia, USA4Science Systems and Applications Inc., Hampton, Virginia, USA

Received: 9 September 2010 – Published in Atmos. Chem. Phys. Discuss.: 17 November 2010Revised: 22 February 2011 – Accepted: 24 March 2011 – Published: 29 March 2011

Abstract. A method is developed based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observa-tions (CALIPSO) level 1 attenuated backscatter profiledata for deriving the mean extinction coefficient of wa-ter droplets close to cloud top. The method is applicableto low level (cloud top

2904 J. Li et al.: New method for retrieval of the extinction coefficient of water clouds

et al., 1987; Albrecht et al., 1988; Kiehl, 1994). For exam-ple, Slingo (1990) estimated that reducing the effective di-ameter of stratus cloud droplet sizes from 20 to 16 µm wouldbalance the warming due to a doubling of atmospheric CO2.Randall et al. (1984) estimated that a 4% increase in the areaof the globe covered by these clouds could also potentiallycompensate for the estimated warming due to a doubling ofatmospheric CO2. Therefore, it is very important to know theglobal distribution of water cloud microphysical, macrophys-ical and radiative properties and their relationship in order toassess the impact of these clouds on the climate system.

Ground based (e.g. Fox and Illingworth, 1997; Wang etal., 2004; O’Connor et al., 2005; Illingworth et al., 2007)and satellite observations (e.g. Masunaga et al., 2002a, b;Scḧuller et al., 2003, 2005; Wood et al., 2005a, b, 2006)can help diagnose cloud microphysical and macrophysicalproperties and their link to cloud radiative and precipitationproperties. However, although the cloud properties can be re-trieved relatively accurately from ground based lidar or radarsignals (e.g. Derr, 1980; Wang and Sassen, 2001; Westbrooket al., 2010), only one-dimensional observations are possi-ble, and the sites are sparsely distributed, almost non-existentover the oceans. So, results from ground observational mea-surements are commonly used to validate and evaluate satel-lite remote sensing retrievals (e.g. Tao et al., 2008; Mamouriet al., 2009; Kim et al., 2008; Mona et al., 2007). The ad-vantage of remote sensing observations from instruments de-ployed on satellites is that high-resolution, two-dimensionaldistributions of the micro and macrophysical properties ofclouds may be retrieved on a global scale. In this investiga-tion, we will develop a novel method to assess the extinc-tion coefficient of low-level water clouds on a global scaleby using space-based lidar (CALIPSO) attenuated backscat-ter data.

Previous studies (e.g. Boers and Mitchell, 1994;Duynkerke et al., 1995; Pawlowska and Brenguier, 2000)show that profiles of liquid water content in actual strati-form boundary layer clouds follow the so-called adiabaticcloud model. That is, for many water clouds the liquid wa-ter content increases linearly with height. But, the dropletnumber concentration within the cloud has an approximatelyconstant value. As a result, the extinction coefficient anddroplet radius in water clouds both increase with heightabove cloud base. However, since boundary layer clouds fre-quently exceed CALIOP’s detection limit of effective opticaldepth (ητ< 3, η is multiple scattering factor andτ is opticaldepth ) (Hu et al., 2007b; Chand et al., 2008), the lidar sig-nal can be completely attenuated within a penetration depthof about 100 m for most boundary layer clouds with modestand low extinctions. So, in this paper, only mean microphys-ical properties in the top part of water cloud can be derivedfrom the new method. However, the vertical change of theextinction coefficient within the top 100 m is relatively smallcompared with the mean extinction coefficient value. Al-though, the veritcal profile of the extinction coefficient within

the entire water cloud layer cannot be derived by the methoddeveloped here, this study about the microphysical proper-ties of the top part of the water cloud is still meaningful andvalid. It can help retrieve the droplet number concentration,which has less vertical variation.

Hu et al. (2007a) already derived the mean extinction co-efficient, liquid water content and droplet number concen-tration of low-level water cloud tops by using collocatedwater cloud droplet sizes retrieved from MODIS data andCALIPSO level 2 cloud products. Nonetheless, the watercloud measurements made by active remote sensing instru-ments (such as, space-based lidar) are very different fromthose made by passive remote sensing instruments (such asMODIS). Passive remote sensing of water clouds, based onmeasured spectral differences of reflected sunlight and ther-mal emissions, is used to retrieve values of optical depthfor the entire vertical column. The passive sensors providethe effective droplet radius using the absorption at near in-frared wavelengths in the solar spectrum, and are based onthe single-layer cloud assumption. So, retrievals of watercloud extinction properties based on MODIS effective radiusmeasurements are limited to the daytime and are valid onlywhen the single-layer cloud condition is satisfied. However,a space-based lidar (such as CALIPSO) obtains informationabout the cloud from the backscattered lidar signal. Thus, alidar can provide the atmospheric attenuated backscatter pro-file, and is not confined to daytime conditions and a single-layer cloud structure.

The objective of this study is to provide better knowledgeof water cloud physical properties and their impact on thesurface energy budget from a comparison of optical prop-erties of water clouds between day and night. The globalstatistics of nighttime water cloud optical properties derivedin this study is a valuable supplement to daytime retrieval re-sults based on passive remote sensing of scattered sunlight,and can provide additional information about cloud proper-ties such day-night variations.

This study is organized as follows. The retrieval methodis introduced in Sect. 2. In Sect. 3 we compare results be-tween the new method and previous studies. Finally, a briefdiscussion and conclusions are provided in Sect. 4.

2 Methodology

Monte Carlo simulations indicate that by using layer inte-grated depolarization ratios and the slope of the exponen-tial decay in the water cloud backscatter due to multiplescattering, both extinction coefficients and effective radii ofwater clouds can be derived from CALIPSO lidar measure-ments (Hu et al., 2007a). In view of the multiple scatteringeffect, the attenuated backscatter can be expressed as (Platt,1979, 1981):

β = β0e−2ησr (1)

Atmos. Chem. Phys., 11, 2903–2916, 2011 www.atmos-chem-phys.net/11/2903/2011/

J. Li et al.: New method for retrieval of the extinction coefficient of water clouds 2905

whereσ is the mean extinction coefficient near cloud top,ris the range within the water cloud top, andη is the corre-sponding multiple scattering factor.β0 is the peak value ofthe attenuated backscatter within water cloud. By taking thenatural logarithm on both sides of Eq. (1), we have

ησ =lnβ − lnβ0

−2r(2)

where β and β0 can be obtained from CALIPSO level 1and level 2 datasets. The CALIPSO lidar probes cloud andaerosol layers to a maximum effective optical depth (ητ )of 3 (Hu et al., 2007b; Chand et al., 2008), and the layerswith larger optical depths are opaque. However, boundarylayer clouds frequently exceed this optical depth, thereforein this study we focus on cloud properties near the top ofopaque, low level water clouds. As a result, for these opaqueand dense water clouds, the limitation of an effective opti-cal depth (ητ< 3) below cloud top corresponds to a penetra-tion depth of about 100 m within the cloud, that is, near thecloud top.

The importance of multiple scattering of polarizedlight in the atmosphere has been recognized for a longtime (e.g. Hansen, 1970a, 1970b). For space lidars suchas CALIPSO (Winker et al., 2003), which has a footprintsize of 90 m at the Earths surface, water clouds can exhibita strong depolarization signal due to the presence of mul-tiple scattering (Hu et al., 2001). Thus, multiple scatteringplays an important role in the analysis of the lidar signal. Huet al. (2006) proposed a relationship between the integratedsingle scattering fraction and the accumulated linear depo-larization ratio for water droplets. A simplified version ofthis relation is: η = (1−δ1+δ )

2 (Hu et al., 2007c), where,δ =∫ basetop β⊥ (r)dr/

∫ basetop β‖ (r)dr is the layer-integrated depolar-

ization ratio. This relationship is very valid when the layer-integrated depolarization ratio is smaller than 0.35. Cao etal. (2009) extended the idea of the accumulated depolariza-tion for circular polarization and proposed a unique relationbetween the integrated single scattering fraction and the de-polarization parameter, which does not depend on whetherlinear or circular lidar polarization is being used. This re-lation is independent of the measurement geometry, and themean droplet size, and is insensitive to the width of the sizedistribution for most water cloud lidar returns (Hu et al.,2007a). By their studies, the multiple scattering effect of wa-ter cloud was characterized very well.

Generally speaking, if we adopt the multiple scatteringrelationship: η = (1−δ1+δ )

2 in Eq. (2), we can easily derivethe extinction coefficientσ from the slope of the exponen-tial decay of the water-cloud attenuated backscatterβ andmultiple scattering factorη (hereafter, we call it the “slopemethod”). However, the 532 nm photo multiplier tube (PMT)detectors (parallel and perpendicular) of CALIOP both ex-hibit a non-ideal recovery of the lidar signal after a strongbackscattering target has been observed. In the absence ofa strong backscattering signal, an ideal detector will return

Altit

ude

(km

)

532 nm Total Attenuated Backscatter, km −1 Sr−1 (2006−09−03 Nighttime)

Lat: −25.75 −31.82 −37.87 −43.90 −49.89 −55.83 −61.70 −67.46 −72.94Lon:107.68 106.12 104.40 102.45 100.16 97.34 93.66 88.48 80.42

Log scale

Stratus

Cirrus

AntarcticSurface

15

10

5

0 −8

−7

−6

−5

−4

−3

−2

−1

Altit

ude

(km

)

1064 nm Attenuated Backscatter, km −1 Sr−1 (2006−09−03 Nighttime)

Lat: −25.75 −31.82 −37.87 −43.90 −49.89 −55.83 −61.70 −67.46 −72.94Lon:107.68 106.12 104.40 102.45 100.16 97.34 93.66 88.48 80.42

Log scale

Stratus

Cirrus

AntarcticSurface

15

10

5

0 −8

−7

−6

−5

−4

−3

−2

−1

Fig. 1. CALIPSO data images of 532 nm (top panel) and 1064 nm (bottom panel) total

attenuated backscatter.

30

Fig. 1. CALIPSO data images of 532 nm (top panel) and1064 nm (bottom panel) total attenuated backscatter.

immediately to its baseline state. However, the transient re-sponse of the CALIPSO PMTs is non-ideal. Following astrong impulse signal, such as from the Earths surface or adense water cloud, the signal initially falls off as expectedbut at some point begins decaying at a slower rate that is ap-proximately exponential with respect to time (distance). Inextreme cases, the non-ideal transient recovery can make itwrongly appear as if the laser signal is penetrating the sur-face to a depth of several hundreds of meters (e.g. McGillet al., 2007; Hunt et al., 2009). So, because of the non-ideal transient recovery, the return from strong targets willbe spread by the instrument response function over severaladjacent range bins, implying that the vertical distributionof the attenuated backscatterβ in the water cloud will bechanged. It is unlikely that the lidar receiver electronics arethe source of the problem because the 1064 nm channel usesa similar design and is performing well. To demonstratethis phenomenon, Fig. 1 shows CALIPSO data images of532 nm (top panel) and 1064 nm (bottom panel) total atten-uated backscatter. The 532 nm non-ideal transient recoveryis seen in the 532 nm image as a gradual transition of col-ors from high attenuated backscatter values to lower onesfor strong backscatter targets (e.g. stratus deck on the left,and the Antarctic surface return on the right). Compare thesefeatures to the 1064 nm image, where the detector responseis normal, and these features appear as an almost solid bandof white. For example, the right parts of the 532 nm and1064 nm images (that is, the Antarctic surface) clearly il-lustrate that the 532 nm signal appears to continue hundredsof m beneath the ice surface while the 1064 nm signal does

www.atmos-chem-phys.net/11/2903/2011/ Atmos. Chem. Phys., 11, 2903–2916, 2011


not exhibit this behavior. However, it is worth noticing thatthe cirrus cloud structure (center right) looks about the samein both the 532 nm and 1064 nm images, because there is lit-tle to no contribution from the transient response artifact inthese weak scattering features. This non-ideal transient re-covery is well documented in the literature on photon count-ing applications, and is likely due to the after-pulsing of thePMT (ionization of residual gas). The time scale of the ef-fect depends on gas species, and PMT voltage and internalgeometry.

So, in view of the non-ideal transient recovery of theCALIOP PMTs, profiles of attenuated backscatterβ in thewater cloud were contaminated and can not directly be usedto calculate the extinction coefficient of water cloud by theslope method. To retrieve a valid extinction coefficientσ , wewill take the following three steps:

1. In view of the above discussions, the transient responsefunction of CALIOP is a very important parameter andthe basis of this study. Since a hard land surface cannoteasily be penetrated by the CALIOP signal, the returnfrom a land surface should be distributed in single ver-tical bin under ideal conditions. Therefore, a hard landsurface should be a good target for studies of the tran-sient response function. The strong return within onesingle vertical lidar bin from a hard land surface can beused to quantify how the return from a dense cloud wasspread by the instrument response function over severaladjacent range bins. So, in the first step, we obtain theresponse function by studying CALIOP lidar signals re-turned from land surfaces.

2. Second, we apply a simple de-convolution process tothe attenuated backscatter lidar signal and the transientresponse function of CALIOP in order to remove anyimpacts on the attenuated backscatter profile of watercloud imparted by a non-ideal transient response of thePMTs and get the corrected attenuated backscatter lidarsignal of the water cloud.

3. Finally, after obtaining a valid and corrected attenuatedbackscatter profile of the water cloud by the former twosteps, we can retrieve the extinction coefficientσ of wa-ter cloud from Eq. (2).

2.1 Transient response function of CALIOP

Prior to launch, extensive laboratory characterization of theflight detectors and their associated electronics demonstratedthat the CALIPSO PMTs transient response remains thesame for lidar surface returns with varing surface reflectance.This result can be independently verified using on-orbit databy studying CALIPSO’s lidar signal from surfaces. It isworth noticing that the strongest of the CALIPSO backscattersignals are generated by ocean and land surfaces that are cov-ered by snow or ice (see the Antarctic surface return part of

Fig. 1). In the 532 nm parallel channel, the peak signals fromsnow and ice surfaces under clear skies are so strong that theyusually saturate the detectors. Unlike the parallel component,the cross-polarized (perpendicular) component of the groundreturns for most land and ocean surfaces are generally notsaturated. As a result, in this study, only land surfaces thatare not covered by snow or ice were used to assess the tran-sient response of CALIOP at three channels. We analyze theCALIOP transient response for different land surface typesusing on-orbit CALIPSO Level-1 data (July 2006, October2006, January 2007) at different regions by using a low-passfilter. As shown in a previous study (Hu et al., 2007d) morethan 90 % the surface return energy comes from the three 30meter vertical range bins including the bin that contains thesurface echo. These bins correspond to that of the peak returnitself as well as one bin before and one after the peak return.Thus, we may calculate the transient response functionF ofCALIOP as follows:

Fj =βi∑i=p+10

i=p−1 βi(j = 1,2,3,4,...,12) (3)

by using twelve adjacent lidar bins of land surface returns.The twelve range bins starting from the one range bin beforethe peak to the tenth range bin after the surface peak return.Hereβi is the attenuated backscatter of each bin, which isthe sameβ as in Eqs. (1) and (2), i is the range bin number,andp is the peak surface return range bin. Hu et al. (2007d)already presented a technique to provide improved lidar al-timetry from CALIPSO lidar data by using the transient re-sponse of CALIOP, and verified that the tail-to-peak signalratios are independent of the surface reflectance.

Figure 2 shows the transient response functionF ofCALIOP derived from the land surface return at three chan-nels. The different colors are for different regions (sur-face types are different) and seasons. The left, middle andright panels are for the parallel channel (P532), perpendic-ular channel (S532) and T532 channel (perpendicular andparallel components), respectively. It is clear that the tran-sient response of CALIOP for different months and surfacetypes are almost same. Although the method described in thisstudy can be applied to both 532 nm channels (parallel andperpendicular polarization), only the results from the 532 nmparallel channel are presented in this paper.

2.2 Corrected water cloud attenuated backscatter

Actually, the current water cloud attenuated backscatter sig-nal measured by CALIOP results from a convolution be-tween the corrected cloud attenuated backscatter and thetransient response functionF of CALIOP. This convolutionprocess can be described mathematically as follows

β1corrected×F1 = β0current (4)

β1corrected×F2+β2corrected×F1 = β

1current (5)



10−4

10−3

10−2

10−1

100

0

50

100

150

200

250

300

350

400

CALIPSO Transient Response for surface

Ran

ge

(m)

P532 Channel

West coast US at July/2006West coast US at October/2006West coast SA at January/2007

10−4

10−3

10−2

10−1

100

0

50

100

150

200

250

300

350

400


Ran

ge

(m)

S532 Channel


10−4

10−3

10−2

10−1

100

0

50

100

150

200

250

300

350

400


Ran

ge

(m)

T532 Channel


Fig. 2. Transient response of CALIOP derived from the land surface return at different

months and different regions for three channels.

31

Fig. 2. Transient response of CALIOP derived from the land surface return at different months and different regions for three channels.

... =... (6)

n∑i=1

βicorrected×Fn−i+1 = βi−1current (n = 1,2,3,4,...) (7)

After obtaining the transient response functionF ofCALIOP, we can use it in conjunction with the currentlidar signal to retrieve the corrected water cloud attenu-ated backscatter signal by reversing the convolution pro-cess described by Eqs. (4)–(7), which corresponds to a de-convolution process. Before the de-convolution process, wemust do some horizontal averaging of the vertical lidar pro-files (using for example, 30 profiles) in order to eliminatepossible negative values in the water cloud profiles due tofilter noise. Then, we may start the de-convolution pro-cess from several bins (here, we only use one bin) whichhave very weak air backscatter value above the water clouds.For example, in Eq. (4), β0current consists of weak backscat-ter from the air just above the cloud, as well as backscat-ter from the first bin within the water cloud. Comparedto the backscatter from the first cloud bin, the backscatterfrom the air just above the cloud is very weak and can beneglected. Thus,β0current is the backscatter signal from thefirst bin within the water cloud, andβ1correctedis the correctedbackscatter value of first bin of the water cloud profile, andβ1current is the current backscatter value of first bin of the wa-ter cloud profile. By continuing this de-convolution process,eventually, the corrected backscatter signals of all bins canbe derived.

Figure 3 shows the cloud attenuated backscatter signal re-trieved beneath the water cloud peak return and the observedattenuated backscatter signal by CALIOP. The red line isobserved (current) water cloud attenuated backscatter signaland the blue line is the retrieval (corrected or real) cloud sig-nal. The results show that the transient response of CALIOPPMTs can affect the vertical distribution (that is, the wave-form) and magnitude of the water cloud attenuated backscat-ter signal. After the de-convolution process, the slope of the

exponential decay of the water cloud attenuated backscatter,may be obtained by using a simple linear fit to the severalrange bins underneath the peak of the water cloud lidar re-turn and the peak return bin itself. According to Eq. (2), theextinction coefficient of the low-level water cloud top thuscan be derived from the slope and multiple scattering factorof the water cloud.

3 Results

3.1 Comparison of extinction coefficients derived fromdifferent methods

Hu et al. (2007a) derived the mean extinction coefficientσof water cloud top by combining the cloud effective radiusRe reported by MODIS with the lidar depolarization ratiosmeasured by CALIPSO:

σ = (Re

Re0)1/3

{1+135

δ2

(1−δ)2

}(8)

whereRe0 equals 1 µm, andδ is the layer-integrated depolar-ization ratio from CALIPSO Level 2 cloud products. Equa-tion (8) is derived from Monte Carlo simulations that incor-porate the CALIPSO instrument specifications, viewing ge-ometry, and footprint size. This method (hereafter, we callit “Hu’s method”) needs collocated water cloud droplet sizesretrieved from MODIS 3.7-µm data for CERES (Minnis etal., 2006). The number of photons scattered into the forwarddirection increases with particle size. Thus, the chance ofa photon at the near-infrared 3.7-µm wavelength being ab-sorbed rather than backscattered to space increases with size.For the same optical depths, water clouds with larger dropletsare darker in the near-infrared wavelengths. The effectivedroplet radius derived from the absorption at 3.7-µm reflectsthe average size information from the very top part of wa-ter clouds (Platnick, 2000), with a vertical penetration depthsimilar to the CALIPSO lidar signal. So, Hu’s method is a



3.pdf

10−2

10−1

100

20

40

60

80

100

120

140

160

180

200

220

Attenuated Backscatter for P532 Channel

Ran

ge (

m)

Water cloud real signal and observational signal

Observational water cloud signalRetrieval real water cloud signal

Fig. 3. The retrieved attenuated backscatter signal beneath the water cloud peak return and

the observed attenuated backscatter by CALIOP. The red line is the observed (current) water

cloud signal and the blue line is the retrieved (corrected or real) cloud signal.

32

Fig. 3. The retrieved attenuated backscatter signal beneath the wa-ter cloud peak return and the observed attenuated backscatter byCALIOP. The red line is the observed (current) water cloud signaland the blue line is the retrieved (corrected or real) cloud signal.

simple and reliable technique that can be used to evaluate andverify the results of the slope method developed in this studyduring daytime.

In this study, the results of Hu’s method are based on fourmonths (January 2008, April 2008, July 2007 and October2007) MODIS 1 km cloud data from Aqua and CALIPSOLevel 2 cloud dataset. The results of the slope method arebased on CALIPSO Level 1 and Level 2 data for the samemonths. Figure 4 shows a comparison of extinction coeffi-cients derived from the two methods. Thex-axis is for slopemethod, and they-axis is for Hu’s method. The color val-ues represent the sample numbers. In addition, the blackdots are mean values and horizontal thin black lines are theerror bars. It is very clear that the differences between theextinction coefficients derived from the two methods are rel-ative larger just when extinctions exceed 40 km−1. We de-fine the absolute relative difference as:h = |σslope method−σHu′s method|/σHu′s method. The mean absolute relative dif-ference ranges from 11.4% to 15% for the four differentmonths. The difference is largest for January 2008, reach-ing about 15%; and smallest for July 2007, reaching about11.4%. Overall, the average value of the mean absolute rela-tive differences for the four months is about 13.4%.

Figure 5 shows the global distributions of low-level wa-ter cloud top extinction coefficient for different months. Theleft panel depicts Hu’s method, and the right panel is for theslope method. It is clear that the global distributions of theextinction coefficients are very similar for two methods. Thelarger extinction values are located along the coastal regionsof the continents, such as the west coasts of South America,North America and Africa. We also found that the frequencyof occurrence of water clouds is higher in these coastal re-gions. Because the MODIS effective radius is more reliableunder single-layer cloud conditions, the results of Figs. 4 and5 are all derived from single-layer cloud samples.

Overall, the global mean extinction coefficients derivedfrom Hu’s method are about 31, 33, 31, 32 km−1 for July2007, October 2007, January 2008 and April 2008, respec-tively. The corresponding values derived from the slopemethod are 29, 30, 30 and 31 km−1. Their global mean rel-ative differences are all smaller than 9%, about 1–3 km−1.Thus, we may conclude that the mean extinction values de-rived from these two methods agree well with each other.However, it is worth noticing that the results in this paperdo not include contributions from two kinds of water cloudsamples. The first one consists of samples with higher depo-larization ratio (>0.35). As stated at Sect. 2, a very importantparameter in this work is the layer integrated depolarizationof water cloud. But, Hu’s multiple scattering scheme whichwe adopted is valid only when the layer-integrated depolar-ization ratio is smaller than 0.35. So, in our study, we fo-cus exclusively on water cloud samples with layer-integrateddepolarization ratio are smaller than 0.35. The second kindof water cloud samples that need to be excluded consists ofsamples with higher extinction coefficients.

A reasonable estimate of the limit of the slope method isthat the cloud effective optical depth (ητ ) should be less than3 for the top 100 m. The lidar signal will be completely atten-uated within only one vertical range bin of CALIOP when theextinction coefficient of the water cloud is beyond 100 km−1.Water cloud samples with such extreme extinction coeffi-cients were not included in our study. Overall, consideringthat multiple scattering help reduce the attenuation and en-hance the detectability, we can estimate that the upper limitof extinction coefficient retrieval from this approach is about60 km−1 if we have good SNR (nighttime measurements, lotsof averaging). On the safe side, the limit is 30 km−1. Thisalso is the possible reason that caused the relative larger dif-ference between slope method and Hu’s method when ex-tinctions exceed 40 km−1. On the other hand, extinction co-efficients derived from the Hu’s method is less sensitive tothe transient response since that method depends only on thedepolarization ratio.

3.2 Comparison of daytime and nighttimeextinction coefficients

In this paper, we assessed the global information of watercloud extinction coefficient during daytime and nighttime byusing the slope method developed for this purpose. It is im-portant to notice that day and night differences is differentfrom the diurnal cycle. The CALIPSO data are not able toprovide diurnal cycle of clouds. So, the extinction coeffi-cient of water cloud at daytime and nighttime are the all-timemean value for day and night conditions. However, a com-parison of daytime and nighttime values is still meaningful.Global statistics of nighttime water cloud optical propertiesderived in this study constitute a valuable supplement to day-time retrievals from passive remote sensing that depends on



oo 10 20 30 40 50 60CALIPSO L1 data (km"1)

Low Level Water Cloud Extinction Coefficient April 200860 3600

Low Level Water Cloud Extinction Coefficient October 200760 3600

~ 50

~ ~2700

-e 40 -e 40

'" '"..J ..J0 30 1800 0 30 1800en en0- 0-:J 20 :J 20« «t? 900 t? 900'" 10 '" 10'0 '00 0:;: :;:

oo 10 20 30 40 50 60CALIPSO L1 data (km"1)

Low Level Water Cloud Extinction Coefficient Jan 200860 3600

Low Level Water Cloud Extinction Coefficient July 200760 3600

~ 50

~ 50 ~ 50~ ~

2700

-e 40 -e 40

'" '"..J ..J0 30 1800 0 30 1800en en0- 0-:J 20 :J 20« «t? 900 t? 900'" '"'0 10 '0 100 0:;: :;:

0 00 10 20 30 40 50 60 0 10 20 30 40 50 60

CALIPSO L1 data (km"1) CALIPSO L1 data (km"1)

Fig. 4. Comparison of water cloud top mean extinction coefficient by using slope method

and Hu’s method. The x-axis is for slope method, y-axis is for Hu’s method. Black dots are

mean values and horizontal black shorter lines are the error bars.

33

Fig. 4. Comparison of water cloud top mean extinction coefficient by using slope method and Hu’s method. Thex-axis is for slope method,y-axis is for Hu’s method. Black dots are mean values and horizontal black shorter lines are the error bars.

Table 1. The averaged extinction coefficients and depolarization ratios of low level water cloud from slope method at different subtropicalstratocumulus regions.

Extinction (km−1) Depolarization

Region Jul Oct Jan Apr Jul Oct Jan Apr

(1) Californian D:33 D:32 D:30 D:40 D:0.224 D:0.224 D:0.212 D:0.24610◦ N–30◦ N; 150◦ W–110◦ W N:35 N:35 N:30 N:40 N:0.23 N:0.235 N:0.21 N:0.248(2) Namibian D:37 D:37 D:34 D:33 D:0.239 D:0.239 D:0.23 D:0.22630◦ S–0◦ S; 25◦ W–15◦ E N:39 N:40 N:37 N:35 N:0.244 N:0.252 N:0.242 N:0.229(3) Canarian D:34 D:35 D:26 D:38 D:0.236 D:0.253 D:0.206 D:0.24310◦ N–30◦ N; 45◦ W–20◦ W N:42 N:35 N:27 N:41 N:0.261 N:0.252 N:0.206 N:0.254(4) Peruvian D:31 D:34 D:32 D:31 D:0.218 D:0.228 D:0.22 D:0.21930◦ S–0◦ S; 120◦ W–70◦ W N:32 N:37 N:35 N:33 N:0.219 N:0.24 N:0.235 N:0.223

reflected sunlight, and provide additional information aboutcloud properties.

The global distributions of water cloud extinction coeffi-cients and depolarization ratio at daytime and nighttime ina 2◦ by 2◦ grid are shown in Figs. 6 and 7. The left panelis for daytime, and the right panel is for nighttime. Thereare several obvious features in Fig. 6. First, global dis-tributions of the extinction coefficient over the ocean dur-ing daytime are very similar to those obtained during night-time. For example, the larger extinction values (may be reach40 km−1) are located along the coastal regions of the con-tinents, and coincide with the major marine stratocumulusregions. In addition, these regions also exhibit larger clouddroplet number concentrations and smaller mean liquid water

paths (Bennartz, 2007). Leon et al. (2008) showed that stra-tocumulus (Sc) dominated regions exhibit larger day-nightdifference in cloud properties. And the dynamics and struc-ture of low clouds may exhibit regional differences (Wood etal., 2002). As a result, we picked up four classic subtropicalstratocumulus regions (the Californian, Canarian, Namibian,and Peruvian), where strong trade inversions limit mixingbetween the boundary layer and the free atmosphere, to ex-amine the day-night difference of the extinction coefficients.The geographical definitions of these four regions are thesame as those in the study of Leon (Leon et al., 2008). Ta-ble 1 lists the extinction coefficients and depolarization ratiosof water clouds at day and night for the four regions. The ex-tinction coefficient differences between day and night have



60° S

30° S

0°

30° N

60° N

Low Level Water Cloud Extinction Coefficient Jan 2008

0

20

40

60

60° S

30° S

0°

30° N

60° N

Low Level Water Cloud Extinction Coefficient April 2008

0

20

40

60

60° S

30° S

0°

30° N

60° N

Low Level Water Cloud Extinction Coefficient July 2007

0

20

40

60

60° S

30° S

0°

30° N

60° N

Low Level Water Cloud Extinction Coefficient October 2007

0

20

40

60

60° S

30° S

0°

30° N

60° N

Low Level Water Cloud Extinction Coefficient Jan 2008

0

20

40

60

60° S

30° S

0°

30° N

60° N

Low Level Water Cloud Extinction Coefficient April 2008

0

20

40

60

60° S

30° S

0°

30° N

60° N

Low Level Water Cloud Extinction Coefficient July 2007

0

20

40

60

60° S

30° S

0°

30° N

60° N

Low Level Water Cloud Extinction Coefficient October 2007

0

20

40

60

Fig. 5. The global distribution of Low level water cloud mean extinction coefficient at different

months derived from the slope method (right) and Hu’s method (left).

34

Fig. 5. The global distribution of Low level water cloud mean extinction coefficient at different months derived from the slope method (right)and Hu’s method (left).

clear seasonal variability and are mostly negative at theseregions. Obvious difference exists for July in the Canarianregion, where the difference is about 24% (−8 km−1). Zerodifference exists for January and April in the Californian re-gion, and for October in the Canarian region. In the Califor-nian region, minimum extinctions of day and night both oc-cur in January. Maximum extinctions of day and night bothoccur in April (about 40 km−1), but maximum difference oc-curs in October (about 9%). The Namibian region is similarto the Peruvian, the maximum difference between day andnight both occur in January, reaching 9% (about−3 km−1).Maximum extinctions of day and night both are found in Oc-tober, but the magnitudes are different. The minimum differ-ences between day and night in these two regions both occurin July (


−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud extinction coefficient at daytime of Jan/2008

0

20

40

60

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud extinction coefficient at daytime of April/2008

0

20

40

60

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud extinction coefficient at daytime of July/2007

1

2

3

4

0

20

40

60

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud extinction coefficient at daytime of October/2007

0

20

40

60

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud extinction coefficient at nighttime of Jan/2008

0

20

40

60

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud extinction coefficient at nighttime of April/2008

0

20

40

60

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud extinction coefficient at nighttime of July/2007

0

20

40

60

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud extinction coefficient at nighttime of October/2007

0

20

40

60

Fig. 6 The global distribution (2◦ by 2◦) of Low level water cloud mean extinction coefficient

at different months derived from the slope method at day (left) and night(right). Sc regions

are marked by blue boxes and numbered in Fig. 6. They are: 1, Californian; 2, Namibian; 3,

Canarian; 4, Peruvian.

35

Fig. 6. The global distribution (2◦ by 2◦) of Low level water cloud mean extinction coefficient at different months derived from the slopemethod at day (left) and night(right). Sc regions are marked by blue boxes and numbered in Fig. 6. They are: 1, Californian; 2, Namibian;3, Canarian; 4, Peruvian.

mean depolarization ratios. Overall, the depolarization ra-tios at four Sc regions are larger than the global mean val-ues, and the differences in depolarization between day andnight are negative. However, the differences are positive onthe global scale. In addition, the regional depolarization dif-ferences are relative smaller (


−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud depolarization at daytime of Jan/2008

0.1

0.15

0.2

0.25

0.3

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud depolarization at daytime of April/2008

0.1

0.15

0.2

0.25

0.3

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud depolarization at daytime of July/2007

0.1

0.15

0.2

0.25

0.3

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud depolarization at daytime of October/2007

0.1

0.15

0.2

0.25

0.3

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud depolarization at nighttime of Jan/2008

0.1

0.15

0.2

0.25

0.3

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud depolarization at nighttime of April/2008

0.1

0.15

0.2

0.25

0.3

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud depolarization at nighttime of July/2007

0.1

0.15

0.2

0.25

0.3

−180 −120 −60 0 60 120 180

60

30

0

−30

−60

Low Level Water Cloud depolarization at nighttime of October/2007

0.1

0.15

0.2

0.25

0.3

Fig. 7. The global distribution (2◦ by 2◦) of Low level water cloud depolarization ratio at

different months CALIPSO level-2 333m cloud products. The left panel is for daytime; the

right panel is for nighttime. Individual Sc regions are outlined in blue boxed and are identified

in Fig. 6 and listed in Table 1.

36

Fig. 7. The global distribution (2◦ by 2◦) of Low level water cloud depolarization ratio at different months CALIPSO level-2 333 m cloudproducts. The left panel is for daytime; the right panel is for nighttime. Individual Sc regions are outlined in blue boxed and are identified inFig. 6 and listed in Table 1.

the slope method is not confined to daytime light conditionsand single-layer cloud vertical structure, multi-layered cloudsamples are also included in this section. Thus, the numberof samples considered in Sect. 3.2 is about three times thenumber of samples in Sect. 3.1. In view of statistics, the re-sults in Tables 1 and 2 are more reasonable and are expectedto reflect the mean conditions of low-level water cloud.

4 Conclusions and discussion

Boundary layer clouds play an very important role in mod-ulating Earth’s climate. In this study, a method based onCALIPSO level 1 attenuated backscatter profile was devel-oped to derive the mean extinction coefficient of low-levelwater cloud droplets close to cloud top (cloud top


0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.160

1

2

3

4

5

6

7July/2007

Clo

ud

to

p h

eig

ht

(km

)

Deplorization ratio

Daytime (0.144)Nightime (0.125)Difference (0.019)

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.160

1

2

3

4

5

6

7October/2007

Clo

ud

to

p h

eig

ht

(km

)

Deplorization ratio


0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.180

1

2

3

4

5

6

7January/2008

Clo

ud

to

p h

eig

ht

(km

)

Deplorization ratio


0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.180

1

2

3

4

5

6

7April/2008

Clo

ud

to

p h

eig

ht

(km

)

Deplorization ratio


Fig. 8. The height dependency of global mean depolarization ratio for all level water clouds.

The solid lines are for daytime, thicker dashed lines are for nighttime, thinner dashed lines

are for the difference between day and nighttime. The values in the brackets are the global

mean depolarization ratio for all water clouds.

37

Fig. 8. The height dependency of global mean depolarization ratio for all level water clouds. The solid lines are for daytime, thicker dashedlines are for nighttime, thinner dashed lines are for the difference between day and nighttime. The values in the brackets are the global meandepolarization ratio for all water clouds.

Table 2. The global mean extinction coefficient, eta (multiple-scattering factor) and slope (extinction coefficient×eta) of low levelwater cloud from slope method.

Para. Jan/2008 Apr/2008 Jul/2007 Oct/2007

Extinction coefficient(km−1)

Day-time 29 30 27 29Night-time 28 28 26 28difference 1 2 1 1

Multiple scattering factor

Day-time 0.411 0.41 0.426 0.41Night-time 0.426 0.43 0.448 0.42difference -0.015 -0.02 -0.022 -0.01

Slope (km−1)

Day-time 11.3 11.3 10.6 10.8Night-time 11.5 11.6 10.6 11.0difference −0.2 −0.3 0.0 −0.2

Depolarization ratio

Day-time 0.22 0.225 0.215 0.224Night-time 0.21 0.212 0.203 0.217difference 0.01 0.013 0.012 0.007

number concentration. Such a combination of methods couldprovide a more effective means of deriving number concen-trations under multilayered water cloud conditions or whenan absorbing aerosol layer is located above the low levelwater cloud.

Overall, the new method is useful for retrieving extinc-tion coefficients in clouds with modest and low extinc-tions (extinction coefficient maybe below 60 km−1) whenlayer-integrated depolarization ratios are smaller than 0.35.The novel method also was evaluated and compared withthe previous method developed by Hu et al. (2007a; “Hu’smethod”). Comparisons of results show that the extinctionvalues derived from the new method agree well with thosederived from Hu’s method. The mean absolute relative dif-ference is about 13.4%, and the global mean relative differ-ences are all smaller than 9%, or about 1–3 km−1. We alsocompared differences in extinction coefficients between dayand night at global as well as regional scales. The resultsshowed clearly that the stratocumulus dominated regions ex-hibit larger day-night differences that are all negative andseasonal. However, a contrary tendency occurs for the globalmean results. The global mean extinction coefficients of wa-ter clouds at night are relative lower than those at day. Thedaytime extinctions are about 29, 30 27, 29 km−1 for Jan-uary, April, July and October, respectively. The correspond-ing nighttime values are 28, 28, 26 and 28 km−1. The dif-ferences between day and night are all positive and about1–2 km−1. The maximum difference occurs in July (about7%). The seasonal variation in global mean multiple scat-tering factor of water clouds ranges from 0.41 to 0.45, anddifferences between day and night are small, about−0.015.The corresponding global mean depolarization ratio of low-level water clouds ranges from 0.2 to 0.23, and the differ-ences between day and night are also small, about 0.01. Forall-level water clouds (cloud top


day and night remain small, ranging from 0.009 to 0.019.Moreover, the global mean depolarization decreases with in-creasing height/decreasing temperature.

In addition, Sassen et al. (2009) showed that there aresignificant (about 0.11) average depolarization differencesof ice clouds between day and night, which are inconsis-tent with earlier ground-based data. The significant differ-ence indicates the presence of artifacts in the data set relatedto the effects of background signals from scattered sunlightin the green laser channel; the gain selection may be oneof the reasons. To investigate if the differences in the de-polarization ratio between day and night are related to thegain selection, background noise or other factors, we chosedifferent targets (such as water cloud, ice cloud, commonaerosol and dust) to analyze their depolarization differencebetween day and night. Preliminary results indicate that thedepolarization differences of spherical particles (water cloudor common aerosols, such as clean continental aerosol) aresmall (0.04) are found for non-spherical particles (ice clouds or dust). Moreover, the depo-larization ratios of targets may be more reliable after April of2007 (improved data quality). So, we conclude that the largerdepolarization differences of ice cloud or dust may be real,and perhaps related to the cloud dynamics. However, theseare just preliminary results, and further research is needed tobetter understand the day-night differences in the CALIPSOdepolarization values.

Many studies had shown that aerosols (such as, dust andsmoke) have important impact on the variation of cloud prop-erties (such as, effective droplet radius, number concentra-tion and radiation forcing ) (e.g. DeMott et al., 2003; Huanget al., 2006a,b; Su et al., 2008). In this study, the effect ofaerosols on cloud properties was not considered. That is, theslope of the exponential decay of the validated water cloudattenuated backscatter profile may be somewhat influencedby the aerosol loading, particularly over the Western coast ofAfrica (smoke is abundant due to frequent burning activities).Hence, more studies about the interaction between aerosoland clouds over these regions (higher aerosol optical depth)would be needed in the future.

Acknowledgements.This work is supported by the NASA radiationscience program and the CALIPSO project. In addition, this workis also supported by the National Science Foundation of Chinaunder Grant No. 40725015 and 40633017. Here, the authorswant to also thank Hal Maring and David Considine of NASAHeadquarters for discussions and support.

Edited by: Q. Fu

References

Albrecht, B. A., Randall, D. A., and Nicholls, S.: Observations ofmarine stratocumulus clouds during FIRE, B. Am. Meteor. Soc.,69, 618–626, 1988.

Bennartz, R.: Global assessment of marine boundary layer clouddroplet number concentration from satellite, J. Geophys. Res.,112, D02201,doi:10.1029/2006JD007547, 2007.

Betts, A. K. and Boers, R.: A cloudiness transition in a marineboundary layer, J. Atmos. Sci., 47, 1480–1497, 1990.

Boers, R. and Mitchell, R. M.: Absorption feedback in stratocumu-lus clouds: Influence on cloud top albedo, Tellus, 46A, 229–241,1994.

Brenguier, J., Pawlowska, H., Schuller, 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, 2000.

Cao, X., Roy, G., Roy, N., and Bernier, R.: Comparison of the rela-tionships between lidar integrated backscattered light and accu-mulated depolarization ratios forlinear and circular polarizationfor water droplets, fog-oil and dust, Appl. Opt., 48, 4130–4141,2009.

Chand, D., Anderson, T. L., Wood, R., Charlson, R. J., Hu, Y., andLiu, Z.: Quantifying above-cloud aerosol using spaceborne lidarfor improved understanding of cloudy-sky direct climate forcing,J. Geophys. Res., 113, D13206,doi:10.1029/2007JD009433,2008.

Charlson, R. J., Lovelock, J. E., Andreae, M. O., and Warren, S. G.:Oceanic phytoplankton, atmospheric sulphur, cloud albedo andclimate, Nature, 326, 655–661, 1987.

DeMott, P. J., Sassen, K., Poellot, M. R., Baumgardner, D., Rogers,D. C., Brooks, S. D., Prenni, A. J., and Kreidenweis, S. M.:African dust aerosols as atmospheric ice nuclei, Geophys. Res.Lett., 30(14), 1732,doi:10.1029/2003GL017410, 2003.

Derr, V. E.: Estimation of the extinction coefficient of clouds frommultiwavelength lidar backscatter measurements, Appl. Opt., 19,2310–2314, 1980.

Duynkerke, P., Zhang, H., and Jonker, P.: Microphysical and tur-bulent structure of nocturnal stratocumulus as observed duringASTEX, J. Atmos. Sci., 52, 2763–2777, 1995.

Fouquart, Y., Buriez, J. C., and Herman, M.: The influence ofclouds on radiation: A climate modeling perspective, Rev. Geo-phys., 28, 145–166, 1990.

Fox, N. I. and Illingworth, A. J.: The retrieval of stratocumuluscloud properties by ground-based cloud radar, J. Appl. Meteorol.,36, 485-492, 1997.

Hansen, J. E.: Multiple scattering of polarized light in planetaryatmospheres: Part I. The doubling Method, J. Atmos. Sci., 28,120–125, 1971a.

Hansen, J. E.: Multiple scattering of polarized light in planetary at-mospheres: Part II. Sunlight reflected by terrestrial water clouds,J. Atmos. Sci., 28, 1400–1426, 1971b.

Hartmann, D. L. and Short, D. A.: On the use of Earth radiationbudget statistics for studies of clouds and climate, J. Atmos. Sci.,37, 1233–1250, 1980.

Hartmann, D. L., Ockert-Bell, M. E., and Michelsen, M. L.: Theeffect of cloud type on Earth’s enery balance: Global analysis, J.Clim., 5, 1281–1304, 1992.

Hu, Y., Winker,D. W., Yang, P., Baum, B., Poole, L., and Vann,L.: Identification of cloud phase from PICASSO-CENA lidar


http://dx.doi.org/10.1029/2006JD007547http://dx.doi.org/10.1029/2007JD009433http://dx.doi.org/10.1029/2003GL017410


depolarization: A multiple scattering sensitivity study, J. Quant.Spectrosc. Radiat. Trans., 70, 569–579, 2001.

Hu, Y., Liu, Z., Winker, D., Vaughan, M., Noel, V., Bissonnette, L.,Roy, G., and McGill, M.: A simple relation between lidar multi-ple scattering and depolarization for water clouds, Opt. Lett., 31,1809–1811, 2006.

Hu, Y., Vaughan, M., McClain, C., Behrenfeld, M., Maring, H., An-derson, D., Sun-Mack, S., Flittner, D., Huang, J., Wielicki, B.,Minnis, P., Weimer, C., Trepte, C., and Kuehn, R.: Global statis-tics of liquid water content and effective number concentrationof water clouds over ocean derived from combined CALIPSOand MODIS measurements, Atmos. Chem. Phys., 7, 3353–3359,doi:10.5194/acp-7-3353-2007, 2007a.

Hu, Y., Vaughan, M., Liu, Z., Powell, K., and Rodier, S.: Re-trieving Optical Depths and Lidar Ratios for Transparent Lay-ers Above Opaque Water Clouds From CALIPSO Lidar Mea-surements, IEEE Trans. Geosci. Remote Sens. Lett., 4, 523–526,2007b.

Hu, Y., Vaughan, M., Liu, Z., Lin, B., Yang, P., Flittner, D., Hunt,W., Kuehn, R., Huang, J., Wu, D., Rodier, S., Powell, K., Trepte,C., and Winker, D.: The depolarization-attenuated backscatterrelation: CALIPSO lidar measurements vs. theory, Opt. Express,15, 5327–5332, 2007c.

Hu, Y., Powell, K., Vaughan, M., Tepte, C., Weimer, C., Beheren-feld, M., Young, S., Winker, D., Hostetler, C., Hunt, W., Kuehn,R., Flittner, D., Cisewski, M., Gibson, G., Lin, B., and MacDon-nell, D.: Elevation-In-Tail (EIT) technique for laser altimetry,Opt. Express, 15, 14504–14515, 2007d.

Huang, J., Minnis, P., Lin, B., Wang, T., Yi, Y., Hu, Y., Sun-Mack, S., and Ayers, K.: Possible influences of Asian dustaerosols on cloud properties and radiative forcing observedfrom MODIS and CERES, Geophys. Res. Lett., 33, L06824,doi:10.1029/2005GL024724, 2006a.

Huang, J., Lin, B., Minnis, P., Wang, T., Wang, X., Hu, Y., Yi,Y., and Ayers, J. R.: Satellite-based assessment of possible dustaerosols semi-direct effect on cloud water path over East Asia,Geophys. Res. Lett., 33, L19802,doi:10.1029/2006GL026561,2006b.

Hunt, W. H., Winker, D. M., Vaughan, M. A., Powell, K. A., Lucker,P. L., and Weimer, C.: CALIPSO lidar description and perfor-mance assessment, J. Atmos. Oceanic Technol., 26, 1214–1228,2009.

Illingworth, A. J., Hogan, R. J., O’Connor, E. J., Bouniol,D., De-lano, J.,Pelon, J., Protat, A.,Brooks, M. E., Gaussiat, N., Wil-son, D. R.,Donovan, D. P.,Klein Baltink, H.,van Zadelhoff, G.J.,Eastment, J. D., Goddard, J. W. F.,Wrench, C. L.,Haeffelin,M., Krasnov, O. A., Russchenberg, H. W. J., Piriou, J. M., Vinit,F., Seifert, A.,Tompkins, A. M., and Willn, U.: Cloud-Net: Con-tinuous evaluation of cloud profiles in seven operational mod-els using ground-based observations, Bull. Amer. Meteorol. Soc.,88, 883–898, 2007.

Kiehl, J. T.: Sensitivity of a GCM climate simulation to differencesin continental versus maritime cloud drop size, J. Geophys. Res.,99(23), 107–123, 1994.

Kim, S. W., Berthier, S., Raut, J. C., Chazette, P., Dulac, F.,and Yoon, S. C.: Validation of aerosol and cloud layer struc-tures from the space-borne lidar CALIOP using a ground-basedlidar in Seoul, Korea, Atmos. Chem. Phys., 8, 3705–3720,doi:10.5194/acp-8-3705-2008, 2008.

Leon, D. C., Wang, Z., and Liu, D.: Climatology of drizzle inmarine boundary layer clouds based on 1 year of data fromCloudsat and Cloud-Aerosol Lidar and Infrared Pathfinder Satel-lite Observations (CALIPSO), J. Geophys. Res., 113, D00A14,doi:10,1029/2008JD009835, 2008.

Mamouri, R. E., Amiridis, V., Papayannis, A., Giannakaki, E.,Tsaknakis, G., and Balis, D. S.: Validation of CALIPSO space-borne-derived attenuated backscatter coefficient profiles using aground-based lidar in Athens, Greece, Atmos. Meas. Tech., 2,513–522,doi:10.5194/amt-2-513-2009, 2009.

Masunaga, H., Nakajima, T. Y., Nakajima, T., Kachi, M., Oki,R., and Kuroda, S.: Physical properties of maritime lowclouds as retrieved by combined use of Tropical RainfallMeasurement Mission Microwave Imager and Visible/InfraredScanner: 1. Algorithm, J. Geophys. Res., 107(D10), 4083,doi:10.1029/2001JD000743, 2002a.

Masunaga, H., Nakajima, T. Y., Nakajima, T., Kachi, M., andSuzuki, K.: Physical properties of maritime low clouds as re-trieved by combined use of TRMM Microwave Imager and Vis-ible/Infrared Scanner: 2. Climatology of warm clouds and rain,J. Geophys. Res., 107(D19), 4367,doi:10.1029/2001JD001269,2002b.

McGill, M. J., Vaughan, M., Trepte, C. R., Hart, W. D., Hlavka,D. L., Winker, D. M., Kuehn, R.: Airborne validation of spatialproperties measured by the CALIPSO lidar, J. Geophys. Res.,112, D20201,doi:10.1029/2007JD008768, 2007.

Minnis, P., Geier, E., Wielicki, B., Mack, S. S., Chen, Y., Trepte,Q. Z., Dong, X. Q., Doelling, D. R., Ayers, J. K., and Khaiyer,M. M.: Overview of CERES cloud properties from VIRS andMODIS data, Proc. AMS 12th Conf. Atmos. Radiation, Madi-son, WI, July 10–14, CD-ROM, J2.3., 2006.

Mona, L., Amodeo, A., D’Amico, G., and Pappalardo, G.: Firstcomparisons between CNR-IMAA multi-wavelength Raman li-dar measurements and CALIPSO measurements, Proc. SPIE,6750, 675010,doi:10.1117/12.738011, 2007.

O’Connor, E. J., Hogan, R. J., and Illingworth, A. J.: Retrievingstratocumulus drizzle parameters using Doppler radar and lidar,J. Appl. Meteorol., 44, 14–27, 2005.

Platnick, S.: Vertical photon transport in cloud remote sensing prob-lems, J. Geophys. Res., 105(22), 919–935, 2000.

Platt, C. M. R.: Remote sounding of high clouds: I. Calculation ofvisible and infrared optical properties from lidar and radiometermeasurements, J. Appl. Meteor., 18, 1130–1143, 1979.

Platt, C. M. R.: Remote sounding of high clouds: III. Monte Carlocalculations of multiple-scattered lidar returns, J. Atmos. Sci.,38, 156–167, 1981.

Randall, D. A., Coakley Jr., J. A., Fairall, C. W., Kropfli, R. A., andLenschow, D. H.: Outlook for research on subtropical marinestratiform clouds, B. Am. Meteor. Soc., 65, 1290–1301, 1984.

Sassen, K. and Zhu, J.: A global survey of CALIPSO linear depo-larization ratios in ice clouds: Initial findings, J. Geophys. Res.,114, D00H07,doi:10.1029/2009JD012279, 2009.

Scḧuller, L., Brenguier, J., and Pawlowska, H.: Retrieval of mi-crophysical, geometrical, and radiative properties of marine stra-tocumulus from remote sensing, J. Geophys. Res., 108(D15),8631,doi:10.1029/2002JD002680, 2003.

Scḧuller, L., Bennartz, R., Fischer, J., and Brenguier, J.: An algo-rithm for the retrieval of droplet number concentration and ge-ometrical thickness of stratiform marine boundary layer clouds


http://dx.doi.org/10.5194/acp-7-3353-2007http://dx.doi.org/10.1029/2005GL024724http://dx.doi.org/10.1029/2006GL026561http://dx.doi.org/10.5194/acp-8-3705-2008http://dx.doi.org/10.5194/amt-2-513-2009http://dx.doi.org/10.1029/2001JD000743http://dx.doi.org/10.1029/2001JD001269http://dx.doi.org/10.1029/2007JD008768http://dx.doi.org/10.1117/12.738011http://dx.doi.org/10.1029/2009JD012279http://dx.doi.org/10.1029/2002JD002680


applied to MODIS radiometric observations, J. Appl. Meteorol.,44, 28–38, 2005.

Slingo, A.: Sensitivity of the Earths radiation budget to changes inlow clouds, Nature, 343, 49–51, 1990.

Su, J., Huang, J., Fu, Q., Minnis, P., Ge, J., and Bi, J.: Estimationof Asian dust aerosol effect on cloud radiation forcing using Fu-Liou radiative model and CERES measurements, Atmos. Chem.Phys., 8, 2763–2771,doi:10.5194/acp-8-2763-2008, 2008.

Tao, Z., Mccormick, M. P., and Wu, D.: A comparison methodfor spaceborne and ground-based lidar and its application to theCALIPSO lidar, Apply Phys. B, 91, 639–644, 2008.

Wang, Z. and Sassen, K.: Cloud type and property retrieval usingmultiple remote sensors, J. Appl. Meteor., 40, 1665–1682, 2001.

Wang, Z., Sassen, K., Whiteman, D., and Demoz, B.: Studyingaltocumulus plus virga with ground-based active and passive re-mote sensors, J. Appl. Meteor., 43, 449–460, 2004.

Westbrook, C. D., Hogan, R. J., O’Connor, E. J., and Illingworth,A. J.: Estimating drizzle drop size and precipitation rate usingtwo-colour lidar measurements, Atmos. Meas. Tech., 3, 671–681, doi:10.5194/amt-3-671-2010, 2010.

Winker, D. M., Pelon, J. R., and McCormick, M. P.: The CALIPSOmission: Spaceborne lidar for observation of aerosols and clouds,Proc. SPIE, 4893, 1–11,doi:10.1117/12.466539, 2003.

Wood, R.: Drizzle in stratiform boundary layer clouds. Part I: Ver-tical and horizontal structure, J. Atmos. Sci., 62, 3011–3033,2005a.

Wood, R.: Drizzle in stratiform boundary layer clouds. Part II: Mi-crophysical aspects, J. Atmos. Sci., 62, 3034–3050, 2005b.

Wood, R. and Hartmann, D. L.: Spatial variability of liquid waterpath in marine low cloud: The importance of mesoscale cellularconvection, J. Clim., 19, 1748–1764, 2006.

Wood, R., Bretherton, C. S., and Hartmann, D. L.: Diurnal cycle ofliquid water path over the subtropical and tropical oceans, Geo-phys. Res. Lett., 29,doi:10.1029/2002GL015371, 2002.

http://dx.doi.org/10.5194/acp-8-2763-2008http://dx.doi.org/10.1117/12.466539http://dx.doi.org/10.1029/2002GL015371

Documents

A new method for retrieval of the extinction coefﬁcient of water … · 2020. 7. 31. · J. Li et al.: New method for retrieval of the extinction coefﬁcient of water clouds 2905