characteristics of caliop attenuated backscatter noise ......clouds in the tropopause layer (e.g.,...

Atmos. Chem. Phys., 11, 2641–2654, 2011www.atmos-chem-phys.net/11/2641/2011/doi:10.5194/acp-11-2641-2011© Author(s) 2011. CC Attribution 3.0 License.

AtmosphericChemistry

and Physics

Characteristics of CALIOP attenuated backscatter noise:implication for cloud/aerosol detection

D. L. Wu1, J. H. Chae1,2, A. Lambert1, and F. F. Zhang3

1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA2Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, California, USA3Harvard University, Cambridge, Massachusetts, USA

Received: 8 May 2010 – Published in Atmos. Chem. Phys. Discuss.: 15 July 2010Revised: 28 February 2011 – Accepted: 17 March 2011 – Published: 22 March 2011

Abstract. A research algorithm is developed for noise eval-uation and feature detection of the CALIOP (Cloud-AerosolLidar with Orthogonal Polarization) Level 1 (L1) backscatterdata with an emphasis on cloud/aerosol features in the uppertroposphere and lower stratosphere (UT/LS). CALIOP mea-surement noise of the version v2.01 and v2.02 L1 backscatterdata aggregated to (5 km) horizontal resolution is analyzedwith two approaches in this study. One is to compare theobserved and modeled molecular scatter profiles by scalingthe modeled profile (with a fitted scaling factorα) to the ob-served clear-sky backscatter profiles. This scalingα value issensitive to errors in the calibrated backscatter and the atmo-spheric model used. Most of the nighttime 532-nmα valuesare close to unity, as expected, but an abrupt drop occurredin October 2008 in the daytime 532-nmα, which is likelyindicative of a problem in the v2.02 daytime calibrated data.The 1064-nm nightα is generally close to 2 while its dayα is ∼3. The other approach to evaluate the lidar measure-ment noise is to use the calibrated lidar backscatter data ataltitudes above 19 km. With this method, the 532-nm and1064-nm measurement noises are analyzed and character-ized individually for each profile in terms of the mean (µ)and standard deviation (σ ), showing largerσ values in gen-eral over landmasses or bright surfaces during day and inradiation-hard regions during night. A significant increas-ing trend is evident in the nighttime 1064-nmσ , which islikely responsible for the increasing difference between thefeature occurrence frequencies (532-nm vs. 1064-nm) de-rived from this study. For feature detection with the re-search algorithm, we apply aσ–based method to the aggre-

Correspondence to:D. L. Wu([email protected])

gated L1 data. The derived morphology of feature occur-rence frequency is in general agreement with that obtainedfrom the Level 2 (L2) 05 kmCLAY+05 km ALAY productsat 5 km horizontal resolution. Finally, a normalized proba-bility density function (PDF) method is employed to eval-uate the day-night backscatter data in which noise levels arelargely different. CALIOP observations reveal a higher prob-ability of daytime cloud/aerosol occurrence than nighttimein the tropical UT/LS region for 532-nm total backscatters>0.01 km−1 sr−1.

1 Introduction

Cirrus and aerosol properties in the upper troposphere andlower stratosphere (UT/LS) region play an important rolein climate-feedback processes (e.g., Jensen et al., 1996;Dessler et al., 2008). However, many aspects of cirrusremain unclear, including formation and lifecycle of theseclouds in the tropopause layer (e.g., Jensen and Acker-man, 2006; Fueglistaler et al., 2009) and UT microphysics(Comstock et al., 2007). Potential impacts of changingUT aerosols are likely to further complicate interactions be-tween cloud and water vapor in this region (Lohmann andRoeckner, 1995; Sherwood, 2002; Mishchenko et al., 2007).Thus, global observations of cloud/aerosol properties fromthe CALIOP (Cloud-Aerosol Lidar with Orthogonal Polar-ization) on CALIPSO (Cloud-Aerosol Lidar and InfraredPathfinder Satellite Observation) are highly valuable for con-straining this problem.

Rich information on cloud/aerosol properties and dis-tributions has been obtained from CALIOP observations(Winker et al., 2003, 2007, 2009). Flying in the A-trainorbit, the nadir dual-wavelength (532 and 1064 nm) and

Published by Copernicus Publications on behalf of the European Geosciences Union.

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2642 D. L. Wu et al.: Implication for cloud/aerosol detection

dual-polarization (perpendicular and parallel at 532 nm) li-dar has footprints collocated with CloudSat 94 GHz CloudProfiling Radar (CPR) (Stephens et al., 2002) and a suiteof passive imagers/sounders. Since May 2008, Aura satel-lite moved up slightly in the A-Train such that the CALIOPand CloudSat measurements are aligned within the samecurtain plane (±10 km in cross-track distance) where AuraMLS (Microwave Limb Sounder) makes limb measure-ments, which extended collocated cloud measurements tomicrowaves at submillimeter wavelengths (e.g., 640 GHzand 2.5 THz). Together with the infrared (IR) and visibleimagers/sounders, the collection of nearly-coincident-and-collocated A-train observations opens an unprecedented op-portunity for cloud/aerosol research.

Like with other remote sensing techniques, cloud/aerosoldetection with CALIOP depends on measurement noise,measurement volume, and threshold used. For unbiasedcloud/aerosol detection, one would like to have a sensorwith stable noise so that a constant threshold could be ap-plied globally for feature detection. It is nearly the casefor CloudSat reflectivity noise (Tanelli et al., 2008), but notfor the CALIOP noise. The latter can vary largely fromprofile to profile, which makes the cloud/aerosol detectionmore challenging. A method with variable thresholds forfeature detection must be used with care, because it couldaffect the inferred distribution, trend, and day-night differ-ence of the features detected. Thus, maps/distributions aswell as long-term variations of the measurement noise canprovide a valuable diagnosis in interpreting the patterns andchanges of detected features. Some discussions on false de-tection and misclassification of feature detection with theCALIOP data can be found in Liu et al. (2006) and Vaughanet al. (2009). CALIOP instrument calibration and perfor-mance can be found in Hunt et al. (2009).

In this study we will carry out further analyses on noisecharacteristics of the calibrated CALIOP backscatter (Level1, or L1) data in the provisional release (versions 2.01 and2.02), and evaluate impacts of these noise properties on fea-ture detection (e.g., clouds and aerosols). Through a betterunderstanding and characterization of the CALIOP measure-ment noise, we hope to apply our research algorithm for jointanalyses of CALIOP and other A-train observations (e.g.,Aura MLS) in UT/LS cloud/aerosol studies. To achieve thisgoal, we first reduce the L1 CALIOP data to a manageablesize by aggregating them to a coarser spatial resolution, andestimate the measurement noise in terms of mean (µ) andstandard deviation (σ ). We then study features in the aggre-gated data using the research algorithm with various thresh-old schemes to gain more insights about measurement noiseeffects. The derived atmospheric features are often referredas to Level 2 (L2) products. Some of the initial results fornoise evaluation and feature detection with the v2.01 andv2.02 CALIOP L1 and L2 data at 5 km horizontal resolutionare presented here.

2 Data and methods

For the CALIOP L1 data, we use the provisional re-lease (v2.01 and v2.02) of the attenuated backscatter coeffi-cients in km−1sr−1, β ’(z), of total (TOT) and perpendicular-polarization (PER) signals at 532 and 1064 nm. The L1 filesalso contain auxiliary data such as molecular and ozone den-sity profiles provided by NASA’s GMAO (Global Modelingand Assimilation Office), which are needed for estimatingthe molecular scatter background. The original L1 data have583 vertical levels with resolutions from 30 m near the sur-face to 300 m in the stratosphere, and horizontal resolutionof 300 m [Winker et al., 2009]. To reduce the data file size toa manageable volume, we aggregate the data vertically (194levels) and horizontally (5 km). The vertical resolutions ofthe aggregated L1 data become 90 m at−0.5–8.2 km, 180 mat 8.2–20.2 km, 540 m at 20.2–30.1 km, and 900 m above30.1 km; and the horizontal resolution, originally 300 m, be-comes 5 km. The non-uniform vertical resolution will be fac-tored as the weighted measurement error in our analysis be-cause of different integration time (e.g., Liu et al., 2006).

For the CALIPSO L2 layer products (i.e., 05 kmCLay and05 km ALay), we use only the features detected at the 5-kmhorizontal resolution. The CALIPSO L2 algorithm employsa detection scheme with variable horizontal lengths on theattenuated total 532-nm backscatter (Vaughan et al., 2009).By taking advantage of feature signal strength and spatialcorrelation, the L2 algorithm is able to detect weak fea-tures, such as thin cirrus, polar stratospheric clouds (PSCs),and aerosol layers, using an adaptive multi-profile averagingscheme to search for coherent features in consecutive pro-files within 5, 20, and 80 km in distance. It starts a fea-ture search with the 80-km window along track, and thenproceeds the searching with the 20-km and 5-km window.Each detected cloud/aerosol feature is associated with a flagthat contains information on the searching window size(s)whereby the feature was obtained. In the L2 algorithm thedetected cloud/aerosol layers must meet a requirement forminimum thickness.

In Fig. 1 are two examples of the attenuated backscatterprofiles from the CALIOP L1 data. The clear-sky backscat-ters should be close to the molecular scattering background(solid line) if measurement noise is small. However, mostof the data points scattered around this background is dueto noise, and those outstanding positive outliers (e.g., in thenighttime profiles) are cloud/aerosol features. In the night-time 532-nm TOT profile, the sharply reduced backscattersat altitudes<∼5 km are due to strong attenuation of a cloudlayer at∼5 km. The backscatter measurements are gener-ally noisier at lower altitudes primarily because of the finervertical bins (i.e., less integration time per bin), and the day-time measurements are generally noisier than nighttime datadue to additional background scattering from sunlight. Themolecular scattering background can be seen clearly in thebottom of 532-nm TOT data, but barely in the 1064-nm

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D. L. Wu et al.: Implication for cloud/aerosol detection 2643

Fig. 1. Attenuated CALIOP backscatter profiles (in10−3 km−1 sr−1) for a tropical daytime (top two panels) andnighttime (bottom two panels) case on 1 January 2008. Thethick grey curve is the background molecular backscatter profileestimated from the atmosphere density data provided in the Level 1file. For fitting the scaling factorα, the data are screened with a 2σ

rejection method to remove outliers due to cloud/aerosol (see text),and the retained data are shown in the panel below the originalprofiles. There are no 1064-nm backscatter data at heights above∼30 km.

profiles. Measurement noise is smaller in the 532-nm PERdata with little molecular scattering, but the cloud/aerosolsignals in this channel are also weaker. The daytime 1064-nm and 532-nm TOT noise is similar, but in nighttime the1064-nm data are noisier than 532-nm.

For each backscatter profile, the clear-sky molecular scat-ter profile,βw(z), is estimated using the atmospheric vari-ables (e.g., air and ozone number density profiles) providedin the CALIOP L1 file. To mimic the lidar measurement, itis further attenuated by the two-way molecular and ozonetransmissionT 2

mol and T 2o3, namely, βw(z)T 2

molT2o3, as de-

scribed in the CALIPSO algorithm theoretical basis docu-ment [Hostetler et al., 2006]. This modeled molecular scat-ter profile can be used to verify the calibrated clear-sky lidar

backscatters. Imperfectly calibrated backscatter data may bedetectable when compared to the modeled molecular back-ground. For the 532-nm channel, the current calibration re-lies on primarily on nighttime data and assumes no aerosolat altitudes between 30–34 km. As revealed in Vernier etal. (2009), the presence of stratospheric aerosols may ac-count for 5–12% of error on the calibration coefficient. Toinvestigate potential error in the calibrated backscatters, foreach profile we fit the clear-sky portion of the data to themodeled molecular profile,α·βw(z)T 2

molT2o3, with a scaling

factorα. For the perfect clear-sky atmosphere (i.e., free fromaerosol and cloud) and perfectly calibrated data, the fittingshould yieldα=1.

In the α calculation, the key is to obtain the clear-skydata points for each profile by screening out cloud/aerosoldata points as well as those measurements affected by theirattenuation. However, this is not trivial because of largefluctuations in the lidar backscatters. We employ the it-erative approach used in CloudSat data for the clear-skyscreening (Tanelli et al., 2008; Wu et al., 2009), exceptin the CALIOP case the clear-sky molecular backgroundis a profile. The procedure for obtaining the “clear-sky”profile with the screening-and-fitting iteration is as follows.We first exclude those profiles if any atmospheric value ofthe 532-nm TOT backscatter exceeds 0.004 km−1 sr−1, forwhich an atmospheric measurement is one at an altitude atleast 400 m above the surface. This initial screening helpsto eliminate the profiles with strong cloud scattering. Forthe remaining profiles, we further screen them to minimizecloud/aerosol contamination. We start with the modeledmolecular profileβw(z)T 2

molT2o3 and subtract it from the ob-

served backscatter profileβ ’(z). The difference, i.e.,1β(z)= β ′(z) - β0(z)T 2

molT2o3, which may contain cloud/aerosol fea-

tures (positive values) and strongly-attenuated backscatter(negative values) as seen in Fig. 1, is further screened by aso-called 2σ rule. According to this rule, those points thatare>2σ away from the estimated molecular background onboth sides will be rejected as spikes. From the remaining1β(z) as illustrated in Fig. 1, the mean and standard devi-ation (µ andσ ), as well as the scaling factorα, are calcu-lated. Note that scaling factorα andµ are related throughµ≡< 1β(z)> = (α -1)<β0(z)T 2

molT2o3 >, where〈〉 is the ver-

tical average weighted by the estimated measurement errorprofile. The above screening-fitting procedures are repeatedfor several times in order to improve the estimate values forµ, σ , andα. Convergence (i.e., negligible change) is usuallyachieved within 3-4 iterations, and non-convergent cases arediscarded. For each iteration, we replace1β(z) with β ′(z) -α ·β0(z)T 2

molT2o3 and use it for calculating the newµ andσ

before the next screening-fitting. The finalµ, σ andα areoutput as the measurement error for this profile. If the num-ber of remaining data points in the last iteration is less than100, the profile is discarded as well. We have applied thesame screening-fitting procedure for CALIOP 532-nm and1064-nm TOT data.

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Fig. 2. Time series of daily meanα, the ratio of estimated vs. mod-eled clear-sky molecular backscatter, averaged into six latitude binsfor day (black) and night (red) data at 532-nm (left) and 1064-nm(right) channels. The reference ofα = 1 andα = 2 are provided re-spectively for the 532-nm and 1064-nm channels to guide compar-ison. Since individualα values vary substantially due to measure-ment noise, large spikes (|α|>5 for 532-nm,|α|>10 for 1064-nm)are excluded in the daily mean calculations. The year boundary isindicated by dashed lines, whereas the dotted line marks the versionchange from v2.01 to v2.02.

To monitor the calibrated CALIOP backscatters using themodeled molecular background, we compute daily meanα

of the 532-nm and 1064-nm data, and present the day andnight series separately in Fig. 2. The estimatedα values aresensitive to errors in instrument calibration as well as in themodel atmospheric variables (e.g., pressure, ozone density,and stratospheric aerosols). Because these errors may de-pend on latitude, we divide the daily meanα into six latitudebins. As shown in Fig. 2, the nighttime 532-nmα values atlow and middle latitudes are close to unity with only small(<5%) variability. However,α exhibits a larger seasonalvariation at high latitudes, especially in the Southern Hemi-sphere (SH), of which the larger 532-nmα values in the SHsummer are due to PSC contamination in the fittedα. Com-pared to the nighttime values, the daytime 532-nmα values

are noisier with a larger (<10%) seasonal variation, which isexpected for the noisier backscatter measurements. A large(∼10%) drop in the daytimeα occurred around 6 October2008, which is not associated with the version change fromv2.01 to v2.02. The fact that the drop occurred only in thedaytime data suggests that it was not caused by error in theatmosphere model. Thus, the drop likely reveals a calibra-tion change in the daytime data. Beside the larger seasonalvariation, the daytime 532-nmα values before October 2008are generally around unity, in line with the nighttime values,except at the 30◦ N-60◦ N bin where the daytime values arelower by 5–10%.

We also evaluate the new version (v3.01) CALIOP data us-ing the same algorithm [not shown], and no sharp drop in thedaytime 532-nmα is found during this period of time, con-firming that this calibration error is in the v2 data. The mor-phology ofµ andσ estimated from the v3.01 data is similarto what is shown here for the v2 data. The derived night-time 532-nmα is close to unity in the tropics with slightlylarger seasonal variations. The daytime 532-nmα are gener-ally lower than the nighttime values by 0.05–0.1 in the trop-ics.

Unlike in the 532-nm case, it is more challenging to fitthe 1064-nm molecular scatter background, not only becausethe measurement is noisier but also because the molecularbackground profile is∼16× weaker. As seen in Fig.2, thederivedα values vary generally between 2 and 3, and thenoisier day values are systematically higher than the night.At low and middle latitudes both day and night series re-veal a comparable seasonal variation. There appears a slightincreasing trend in the nighttimeα at these latitudes. Toevaluate how the fittedα depends on theσ–based screening-fitting approach, we tried theα fitting with different multi-ples of theσ–value for the “clear-sky” screening, and foundthat the derivedα values would decrease slightly associatedwith a slight increase in seasonal variability. The increase inseasonal variability can be explained by more cloud/aerosolcontamination in loosening the screening criteria. This is de-tailed further in the following.

It is not trivial to extract “clear-sky” atmospheric molec-ular background from the noisy data. In addition to themeasurement noise, cloud/aerosol features (above the atmo-spheric molecular background) and attenuated values (belowthe background) in the data, as shown in Fig. 1, can be chal-lenging to screen. We tested various noise thresholds (from1σ to 50σ ) for screening the outliers, and found that the 2σ

approach produces the best results in terms of minimizingdaily and seasonal fluctuations. Since the cloud/aerosol con-tamination would manifest itself with a significant seasonalvariation, minimizing the seasonal variability for the derivedα values is necessary for the reliableα estimation. The 1σapproach would leave too few data points for the fitting. Onthe other hand, with a larger (looser) threshold, the contam-inations from cloud/aerosol features and attenuated valueswould tend to bias the fittedα values.



The scalingα approach has demonstrated some promisingcapabilities in monitoring and diagnosing trends and abruptchanges in the calibrated CALIOP backscatter data. It can befurther refined to produce more robust results with less de-pendence on the screening for “clear-sky”. For example, thescreening-fitting can be restricted to middle-to-high altitudesor certain latitudinal/geographical areas where cloud/aerosolcontributions are smaller and have low occurrence. Also,it might be limited to the oceanic regions where the mea-surement noise is low. Nevertheless, more dedicated inves-tigations are needed to improve the method by better under-standing the latitudinal and seasonal variations seen in theestimatedα values.

Because the estimatedα values are noisy and can be af-fected by both measurement and model errors, which arenon-trivial to distinguish at this point, in the analysis here-after we ignore the seasonal variations in the fittedα anduseα = 1.0 to remove the background molecular backscat-ter to compare the feature detection from this work with theL2 layer products. In the subsequent analyses, theµ andσ

of the measurement noise are estimated from1β(z) only atz>19 km.

3 Morphology of CALIOP backscatter noise

The CALIOP backscatter measurement noise has high-degree transiency, varying largely from profile to profile.This is evident in theσ estimated from the data atz>19 km(Fig. 3). The largeσ variation can affect the cloud/aerosoldetection throughout a backscatter profile. Theµ value ofthe measurement error is usually smaller thanσ . As shownin Fig. 3, the nighttime 532-nm PERσ values are muchlower than other cases, which could yield a higher signal-to-noise ratio (SNR) for weak cloud/aerosol signals if thesefeatures produce a significant depolarization ratio. The flatnoise floors in the nighttime noise reflect the detector noise,whereas large fluctuations in the daytime noise are indica-tive of the additive photon noise from bright objects. Thelow nighttimeσ makes additional noise sources stand out,which would not be able to be seen otherwise. For exam-ples, the dark count increases in the radiation-hard regions,such as the South Atlantic Anomaly (SAA) and auroral ovals,are evident in this orbit [Hunt et al., 2009]. Unlike the 532-nm measurements, the 1064-nm backscatters do not exhibitobvious dependence on the SAA because the 532 nm detec-tors are photomultiplier tubes (PMTs) and more susceptibleto radiation effects than the 1064 nm avalanche photodiodes(APDs). As discussed in Hunt et al. (2009), PMTs have mod-est quantum efficiencies with relatively low dark noise thatshould obey the Poisson statistics, whereas APDs have highquantum efficiencies but with high Gaussian-like dark noise.

To characterize the CALIOP backscatter noise distribu-tion, which can affect the distribution of feature detectabil-ity, we map the monthly-meanµ andσ separately for day

Fig. 3. Orbital variations of the estimatedσ of the 532-nm PER(top), 532-nm TOT (middle), and 1064-nm TOT (bottom) backscat-ter from an orbit (embedded map) on 1 January 2008. The A-Trainorbit has a 98◦ inclination angle and the flight direction is indicatedby the arrows on the map. The day (night) portion of the orbit isdepicted by black (grey) line. The nighttime 532-nm noises revealhigher σ values at latitudes of∼30◦ S due to the South AtlanticAnomaly (SAA).

and night. January 2008 is chosen to illustrate the derivednoise properties, but other months reveal similar character-istics. Unlike the nighttime, the daytime CALIOP backscat-ter noise is dominated by the background scattering sunlight,which is additive and Gaussian-like. It is highly variabledepending on surface/atmospheric albedo. The daytimeµ

(Fig. 4) is nearly zero globally for all the CALIOP chan-nels, except in the SAA region where it is slightly negativein the 532-nm PER and TOT data. However, theσ valuesvary highly with surface and cloud albedo. The albedo ef-fects appear to modulate the 532-nmσ by a factor of 2–5 (higher in cloudy/snowy/icy regions) or over landmasses(e.g., desert). Compared to the 532-nm measurements, the1064-nmσ map shows more distinct contrast between landand ocean mostly in the SH. The nighttimeµ and σ val-ues (Fig. 5), although generally lower than the daytime, re-veals features related to the detector response to radiation-hard (e.g., SAA, high-latitude geomagnetic activities). These



Fig. 4. Monthly (January 2008) meanµ and σ maps of day-time 532-nm PER (top), 532-nm TOT (middle), and 1064-nm TOT(bottom) backscatter noise (in km−1 sr−1) on a 2◦×2◦ longitude-latitude grid. Most part of the daytime orbits is ascending (i.e., lat-itude increasing with time). The descending portion of the orbitsis excluded to minimize the mixed statistics between the two localtimes.

geomagnetic features are more pronounced in the 532-nmthan in the 1064-nm measurements. The 532-nm PERand TOT are respectively∼50× and∼5× less noisy thanthe measurements in the geomagnetic-active regions. Theradiation-induced dark counts in PMT become only impor-tant during nighttime when the solar background noise is ab-sent.

Althoughσ from altitudes>19 km provides the first-orderestimate of the lidar measurement noise, it is also useful toexamine noise properties as a function of height, becauseadditional measurement error might appear at different al-titudes. If theσ estimated from altitudes>19 km can rep-resent the noise well for each profile, one may apply a uni-form σ -based threshold to the entire profile for feature de-tection. More complete statistical properties of the CALIOPnoises are presented in Figs. 6–8 in terms of the normalizedprobability density function (PDF). The PDF is normalizedin the way such that its integration (i.e., the area under eachcurve) is unity. For the noisy data where signal and noisehave a comparable probability (or weak signals), the nor-malized PDF provides a powerful means to evaluate the sub-tle differences between two data sets (e.g., day-night differ-ences) without imposing a clear-cut detecting threshold (Wuet al., 2009). The PDFs in Figs. 6–8 are derived from theattenuated backscatters with the molecular background sub-tracted. The number of samples in each backscatter valuebin is first divided by the total number for normalizationand then by the bin sizes to yield probability density. Sincecloud/aerosol scatterings often produce positive backscat-ters at most of these altitudes, the negative PDF domain re-flects characteristics of the measurement noise, which can

Fig. 5. As in Fig. 4 but for the nighttime backscatter noise, and theascending portion of the orbits is excluded.

be Gaussian or non-Gaussian depending on noise sources.However, clouds/aerosols also attenuate the molecular back-ground scattering below them, which could skew the negativePDF domain. This effect is evident in the nighttime 532-nmdata at lower altitudes.

The rising portion of PDF at small values in Figs. 6–8 isindicative of the measurement noise. For the noise with zeromean, the positive and negative PDF domains should yield aconsistent distribution in the noise portion of PDF. Hence, wefold the negative PDF domain to the positive side for compar-ison. In the case where measurements are dominated by themeasurement noise (usually at altitudes>19 km), the posi-tive and negative PDF domains overlap, as seen in Figs. 6–8at 21.7 km. In the presence of clouds/aerosols, the positivePDF domain will deviate from the negative one, showing anextended distribution at large values. The estimatedσ showsa slight increase with decreasing altitude, which is expectedfor the increasing vertical resolution (or decreasing measure-ment integration time) at lower altitudes. No significant addi-tional noise source is added to theσ estimated from altitudes>19 km, and therefore theσ–based threshold for feature de-tection can be applied to the entire profile. Some of the mea-surement noise may not be Gaussian, as seen in the night-time 532-nm data (Fig. 6). Non-Gaussian or multi-Gaussiancases require special care for cloud/aerosol detection, be-cause feature detection from theσ -based method could en-counter a higher false-positive rate than what it produces withthe Gaussian case.

For feature detection in this study, a conservative thresh-old (>5σ is applied to the daytime data on a profile-by-profile basis (Table 1), which would yield a very small (1out of 1.7 million) false detection rate if the measurementnoise is Gaussian. For the nighttime data, however, dif-ferent thresholds are used. As seen in Fig. 6, the night-time 532-nm PER data have complicated noise characteris-tics when the measurements are associated with very low



Fig. 6. Normalized PDFs of the attenuated 532-nm PER backscatterfor a tropical (10◦ S–10◦ N) bin in January 2008. The PDF is nor-malized such that its integration over all backscatter bins is equal tounity. Theσ values estimated from the negative PDF domain fordaytime (upper panels) and nighttime (lower panels) are given ineach panel. For the 532-nm PER measurement, the nighttime noiseis composed of two Gaussian functions with theσ values shownin each panel. Vertical dotted lines indicate the 5σ value for day,and 1σ , 5σ , and 10σ for night. The grey curve is the negativePDF domain folded onto the positive side to evaluate the CALIOPnoise. The dashed line is the analytical Gaussian function for the es-timatedσ . All PDFs are plotted in a log-log scale with backscatterin km−1 sr−1 and PDF in km sr. The SAA contribution is excludedin the statistics at this latitude bin.

Fig. 7. As in Fig. 6 but for the 532-nm TOT backscatter. The sharpcutoff in the negative PDF domain of the nighttime data (at 2.96 and7.27 km) results from cloud/aerosol attenuation.

photon counts. Non-Gaussian or multi-Gaussian noise be-come important for backscatter values<10 σ . Part of thesecond Gaussian with a higherσgalue is due to contribu-tions from the SAA because this tropical bin (10◦ S–10◦ N)retains a small portion of the SAA. We find that the second

Fig. 8. As in Fig. 6 but for the 1064-nm TOT backscatter.

Gaussian noise is still present but significantly reduced in thestatistics of the equator-20◦ N bin. Because of this multi-Gaussian noise character, a higher (>80σ ) threshold is usedfor the cloud/aerosol detection with the nighttime 532-nmPER data. For the 532-nm TOT data (Fig. 7), the negativeand positive PDF domains agree well with each other in thenoise portion. Unlike the 532-nm PER case, the nighttime532-nm TOT PDFs are mostly single-Gaussian. Althoughstratospheric aerosols are difficult to detect, their contribu-tions can be inferred from differences between the PDFs ofpositive- and negative-value backscatters at night. For exam-ple, at 21.7 km the positive PDF domain extends above thenegative one, suggesting potential contributions from strato-spheric aerosols. At lower altitudes the negative-value PDFcan rise above the positive-value one because there exist asignificant number of attenuated backscatter cases. In thesesituations, the backscatters beneath cloud/aerosol layers areseverely attenuated such that these values can fall below theestimated molecular scattering background, resulting in neg-ative 1β(z) values. This attenuation effect is more evidentin the PDFs from the night data than from the day.

A significant change in the PDF slope is evident in thenighttime data at 16.4 km (Figs. 6–7), which occurs at thevalues of∼10−2.5 km−1sr−1. This PDF property is beyonddetection by the daytime measurements. The attenuation isunlikely the cause of this slope transition since it is observedat all altitudes between 14 and 18 km. The PDF slope be-tween the values of 10−3.5 and 10−2.5 km−1 sr−1 is similarto those observed at a lower altitudes, while the one at val-ues greater than 10−2.5 km−1 sr−1 is much steeper, indicatinglack of sources for these clouds.

The 1064-nm data bear many similarities to the 532-nmTOT results in terms of PDF characteristics, except for aslightly higherσ (Fig. 8). Unlike the 532-nm data, the night-time 1064-nm data do not have a spike in the negative PDFdomain at lower altitudes because the molecular scatteringbackground is∼16× weaker at 1064 nm.



Table 1. Cloud/Aerosol detection thresholds (in km−1 sr−1) used in this study.

532-nm PER 532-nm TOT 1064-nm TOT

Daytime Min(5σ , 1×10−4) Min(5σ , 1×10−4) Min(5σ , 1×10−3)Nightime Max(80σ , 3×10−4) Max(16σ , 3×10−4) Max(8σ , 3×10−4)

Fig. 9. From left to right: Examples of the nighttime total attenuated 532 and 1064 backscatters from 1 January 2008 (the same orbit as inFig. 3); the backscatters with the molecular background removed; the features identified with theσ thresholds in Table 1; and the featuredetected in the L2 products. The curve at the top of each left panel is the same measurement noiseσ as shown in Fig. 3 to illustrate relativechanges in noise.

4 Implications for feature detection

For feature detection, we tested a range of theσ thresh-old with the σ–based method, and the thresholds listed inTable 1 produce the results similar to the L2 05kmCLayand 05kmALay products. These thresholds (5σ or greater)allow us to aggressively exclude the clear-sky backgroundnoise with a low false-positive rate for both Gaussian andnon-Gaussian noise cases. The false-positive rate canbe estimated from the stratospheric measurements wherecloud/aerosol features have a very low occurrence frequency.To prevent the threshold from reaching too high or toolow, we amended theσ -based detection rule with a maxi-mum/minimum bound (Table 1). In addition, we also requirethat a feature must appear in two or more consecutive ver-tical bins. This additional requirement further reduces thefalse-positive error, which helps the cloud detection near thetropopause where cloud occurrence frequency drops sharply

with height. Because theσ value used for feature detectionis estimated on a profile-by-profile basis, the region with ahigherσ , such as the SAA and landmasses, will inevitablyhave poorer detecting ability, leading to potential samplingartifacts in the observed cloud/aerosol pattern.

Theσ -based feature detection has less impact on the pat-tern or time series of feature backscatter than on feature oc-currence frequency. Since small values do not contributemuch to the average backscatter, the pattern or time seriesassociated with feature backscatter is not very sensitive tothe threshold used. However, feature occurrence frequency issensitive to the threshold used. Figure 9 compares some ex-amples of the features detected by the research algorithm andthe L2 algorithm. The algorithm using the Table 1 thresholdsmay reject some of the tenure aerosol and cirrus features inthe scene. To study weak stratospheric aerosol features, onemay need to lower the values used in Table 1 for aggres-sive detection. The thresholds may have to be modified if



Fig. 10. Daytime zonal mean backscatter and cloud/aerosol occurrence frequency for January 2008.(a)–(c): the zonal mean 532-nm PER,532-nm TOT, and 1064-nm TOT attenuated backscatter in km−1 sr−1; (d)–(f): the corresponding occurrence frequencies by altitude (in%per km) for cloud/aerosol features detected from these channels; and (g): the total occurrence frequency of clouds and aerosols from theL2 CLay and L2ALay data. The colors for backscatter and frequency have units of km−1 sr−1 and% per km, respectively.

different spatial averaging is used. For example, studying theCALIOP data jointly with other A-Train sensors (e.g., MLS)may require further aggregating the CALIOP data to have amatched measurement volume. In that case, the feature de-tection thresholds need to be re-evaluated.

Shown in Figs. 10–11 are zonal mean statistics of the Jan-uary 2008 cloud/aerosol features detected with the Table 1σ

method. The mean feature backscatter is computed from thebackground-corrected values, i.e.,1β(z), and averaged intoa 2◦ latitude and 0.5 km altitude bin, along with the meanfeature occurrence frequency. The zonal mean backscatterand occurrence frequency exhibit a similar, consistent dis-tribution in general. Compared to the results from the L205 km CLay and 05 kmALay products (Figs. 10g and 11g),the occurrence frequencies obtained in this study (Figs. 10eand 11e) are slightly higher.

However, the occurrence frequencies from the two meth-ods differ significantly in the UT/LS where feature occur-rence frequency and signal-to-noise ratio are low. These fea-tures are important for studying cloud, aerosol, and water va-por changes related to troposphere-stratosphere exchanges.In particular, their spatial/temporal variations and day-nightdifferences provide valuable insights to the problem, but im-pacts of the measurement noise must be fully understood andcharacterized at first. Figures 12–14 summarize the monthlyfeature and noise statistics in the tropical UT/LS.

Figure 12 shows the seasonal variation of monthly meanfeature backscatter and occurrence frequency at 15 km fromthe research algorithm presented in this study and from theL2 algorithm. The daytime 532-nm and 1064-nm TOT havesimilar monthly mean backscatters. A 1064/532 color ratiois calculated from the monthly averages, varying between 1.0and 1.2 with a slight decreasing trend in both day and nightdata. In addition, the daytime color ratio exhibits a large sea-sonal cycle, which is absent in the nighttime data. Similarly,a 532-nm depolarization ratio (δ) is derived from the monthlymean 532-nm PER and TOT data, showing∼0.3 for day and∼0.45 for night with a very small seasonal variation. Sassenand Zhu (2009) reported a slightly larger (∼0.41) daytimeδat 15 km in the tropics, and a smaller (∼0.28) nighttimeδ,using the attenuated backscatters not corrected by the molec-ular background. They also reported an enhanced nighttimeδ values in the SAA region. Since the nighttime featuredetection with the 532-nm PER channel, the nighttimeδ issensitive to the measurement noise and the threshold used.Similarly, the day-night difference in feature occurrence fre-quency also depends strongly on the threshold used. As seenin Fig. 12, despite the similar mean backscatter values, theoccurrence frequencies differ by approximately a factor of 2.

An increasing trend is evident in the 1064-nm noise during2006–2008 (Fig. 13), which may explain the decreasing colorratio seen at 15 km and the increasing occurrence frequency



Fig. 11. As in Fig. 10 but for the nighttime data. In the nighttime 532-nm PER statistics, despite the aggressive screening threshold (80σ), afraction (∼0.5%) of stratospheric features (not PSCs) are still present at middle latitudes, which warrants further investigation.

differences between the 532- and 1064-nm channels as seenin Fig. 12. Although the daytime noises (σ 1064 TOT >

σ 532 TOT>σ 532 PER) have a similar seasonal variation, thenighttimeσ values differ by more than an order of magni-tude. The increasing trend in nighttimeσ 1064 TOT is consis-tent with the expectation of the dark count increase in thechannel (Hunt et al., 2009). Important for a trend analysis,the increasingσ 1064 TOT implies a reduced ability in featuredetection with this channel. The slightly increasing differ-ence between the 532- and 1064-nm occurrence frequenciesat 15 km, as well as the decreasing color ratio in Fig. 12, islikely resulted from this degraded sensitivity in the 1064-nmchannel.

At altitudes near and above the tropopause, where featureoccurrence frequency drops sharply with height, various de-tection methods begin to show large differences. As seen inFig. 14, this study produces a larger daytime frequency at 18–19 km than the L2. At 19 km the research algorithm producesa residual frequency of 0.04%/km for day and 0.06%/km fornight in the 532-nm TOT data, compared to 0.0005%/km and0.001%/km from the L2 data. To a large extent, the valuesat 19 km reflect the false-positive detection rate. As an in-dependent estimate for the false detection rate, the negativePDF domain is used to compute the probability in the dis-tribution tail beyond the Table 1 threshold values. This esti-mation yields a rate of∼0.1%/km for day and 0.05%/km fornight, which is comparable to the values obtained at 19 km,but much higher than what result from a pure Gaussian noise.

Fig. 12. Time series of day (top) and night (bottom) monthlymean backscatter (left) and feature occurrence frequency (right) at15 km altitude in the 10◦S-10◦ N latitude bin. For the 532-nm and1064-nm TOT backscatters, the molecular background have beenremoved. The 532-nm depolarization ratio (δ = β⊥ /βll) and the1064/532 color ratio are calculated from the monthly means. Timeseries of the occurrence frequencies from 532-nm and 1064-nmchannels, as well as the frequency difference, are shown in the rightpanels where the L2 05 km (CLay+ALay) results are included forcomparison.



Fig. 13. Time series of monthly meanσ of the 532-nm PER, 532-nm TOT and 1064-nm TOT backscatter for the 10◦ S–10◦ N lati-tude bin. An increasing trend is evident in the nighttime 1064-nmσ , consistent with the dark count increase reported by Hunt et al.[2009].

Fig. 14. Seasonal variations of the features detected from the 532-nm TOT backscatter near the tropical (10◦ S–10◦ N) tropopausewhere the occurrence frequency drops sharply with height. Monthlymean backscatters (left panels) are plotted for altitudes 18, 18.5and 19 km with an increment offset of 0.002 km−1 sr−1, whereasmonthly mean occurrence frequencies (right panels) are offset byan increment of 0.4%/km for the different altitudes. The day-time (nighttime) mean occurrence frequencies at 19 km are 0.04(0.06)%/km from this study, compared to 0.005 (0.001) %/km fromthe L2 data.

Since the nighttime measurement has lower noise, its season-ality at 18 km is believed to be more reliable. In this study wefind that the seasonal variations of the night backscatter andthe occurrence frequency are very similar at these altitudes.Analyzing the CALIOP data from June 2006 to February2007, Fu et al. (2007) found no clouds above 19 km and ob-served a∼0.05% occurrence frequency at 18.5 km and 0.5%at 18 km. This study shows that the 0.1% per km level isclose to the noise floor or the stratospheric background, and

the TTL (tropical tropopause layer) top has a strong seasonalvariation, as expected for deep convective climatology. In thecase where the desired signals are close to the noise floor, theconvolution of signal and noise statistics would make com-parisons of different data sets more challenging.

The day-night differences at 18 km in Fig. 14 are mostlycaused by the different detection thresholds used for the day-time and nighttime data. For the weak signals like these,Wu et al. [2009] suggested to compare statistics of the twodatasets with the normalized PDF approach. This methodrequires that two data sets have the same measurement vol-ume and the same ensemble. One of the advantages with thismethod is avoidance of artificially-imposed feature detectionthresholds. As long as these data sets represent the samestatistical ensemble with a sufficient number of samples, thePDF comparisons will yield useful characterization on mea-surement noise, bias, sensitivity limitation, and cloud/aerosolstatistics of the ensemble.

In Fig. 15 we compare the normalized PDFs of CALIOPday and night backscatter measurements. Because theCALIOP noise differs substantially between day and night,we compare the day-night difference of probability densityat each backscatter value above the daytime (or larger) noiselevel. As in Fig. 7, the noise PDF in Fig. 15 is the rising prob-ability density at small values with a Gaussian-like distribu-tion. At the backscatter values greater than the daytime noise(e.g., 10−2–10−1.5 km−1sr−1), as shown in Fig. 15, the dayPDF stands clearly above the night one at altitudes>10 km,suggesting that these daytime features have a higher occur-rence frequency. At very large backscatter values, the PDF istoo low and the difference becomes noisier, whereas at verysmall values the daytime noise prohibits the comparison.

Thus, the day-night difference of feature occurrence fre-quency inferred from Fig. 15 is opposite to the threshold-based results as shown in Fig. 14. In an early study withthe ICESat data [Dessler et al., 2006], the nighttime cirrusfraction was found to be higher than the daytime, which wasresulted mostly from the different detection thresholds used.Recently, by the same token, Liu and Zipser (2009) ana-lyzed the CALIOP L2 cloud data and reported the similarhigher nighttime cloud fraction. As aforementioned, if the>5σ threshold were used for CALIOP day and night mea-surements, the nighttime feature fraction would be higherthan the daytime, which is not consistent with the PDF anal-ysis in Fig. 15. The day-night difference is likely depen-dent on water vapor and temperature, and therefore on cloudice water content (IWC). As revealed in Fig. 15, if one usesthe backscatter as a proxy of IWC, the day-night differencevaries with the backscatter value, which can be related to thetype of ice clouds and/or to the process controlling cloud for-mation. In addition, the PDF analysis may provide a promis-ing alternative to characterize other cloud/aerosol statistics,such as depolarization and color ratios, where the measure-ment noise is highly non-stationary and different betweentwo data sets.



Fig. 15. Day-night differences at the tropics (10◦ S–10◦ N) as revealed in the normalized PDFs of the attenuated 532-nm TOT backscatterfor January 2008. Day and night PDFs are denoted by the dark and grey curve, respectively, whereas the thin line beneath PDF curves is thepercentage PDF difference. The rising PDF at small values is a manifestation of the noise distribution. The day-night PDF differences arenot shown when the backscatter is<5σ of the daytime noise. The day PDFs are generally higher than those at night except at 7.99 km.

5 Conclusions and future work

Noise characteristics of the calibrated CALIOP 532-nm and1064-nm attenuated backscatters were investigated with twoapproaches. One is to fit the CALIOP clear-sky backscat-ter data by scaling them to the molecular scatter model, andstudy variations of the scaling factor (α) derived from thev2.01 and v2.02 data. The other is to estimate the measure-ment noise using the calibrated backscatter data at altitudesof 19–40 km and assumingα = 1. Maps and time series ofthe measurement noise properties are analyzed in terms ofthe mean (µ) and standard deviation (σ ). The derivedσ

provides a reliable representation of the lidar measurementnoise at lower altitudes, and we it to develop aσ -based re-search algorithm for cloud/aerosol feature detection with thethresholds given in Table 1. This method is applied to the ag-gregated CALIOP L1 data at the fixed 5-km horizontal res-olution. Variations of the measurement noises and their im-pacts on cloud/aerosol detection are discussed. In addition,the normalized PDF approach is employed to evaluate day-night differences in the lidar data of which the noise proper-ties are quite different. The major findings and conclusionsfrom this study are summarized as follows.

1. The lidar backscatter noises exhibit large profile-to-profile variability inσ , more pronounced in the daytimedata. Such transiency was found in all the CALIOP

channels. Maps of the January 2008 noise propertiesshow that the daytime noises are significantly higherover landmasses and bright surfaces (e.g., snow, ice anddesert), reflecting large variability in additional sunlightscattering during day. This large noise variability ap-pears to be more pronounced in the 1064-nm TOT mea-surements than in the 532-nm data. On the other hand,the nighttime noises, although generally lower than day-time, are enhanced in the radiation-hard regions (e.g.,SAA and auroral ovals), but only in the 532-nm not1064-nm data.

2. The scalingα approach demonstrated some promisingcapabilities for evaluating and diagnosing error in thecalibrated 532-nm and 1064-nm data. The nighttime532-nmα values are mostly close (within 5%) to unity,whereas the daytime 532-nm values are within 10%.The daytime 532-nmα values show an abrupt drop inOctober 2008, which is likely associated with somechanges in the 532-nm backscatter calibration. The1064-nmα values are mostly between 2 and 3 with thedaytime numbers being systematically higher.

3. Most of the CALIOP backscatter noises have aGaussian-like distribution, except for the nighttime 532-nm PER measurements. The latter have the lowestσ but with a double Gaussian-like distribution. The



distribution of the measurement noise is a combinationof detector/calibration error and enhanced photon noisefrom the sunlight.

4. At ∼16 km the nighttime PDFs from the 532-nm and1064-nm TOT backscatters show a drastic change inslope near the value of∼10−2.5 or ∼0.003 km−1sr−1,which appears not due to the attenuation by feature lay-ers. This transition in the PDF slope is difficult to ob-serve with the daytime data due to the large solar back-ground noise. This PDF transition divides the featurestatistics into the domains, which might be useful forstudying cirrus generation and life cycle in the TTL(tropical tropopause layer) region. Further investiga-tions with the CALIOP, CloudSat, and MLS data couldprovide more insights to this transition.

5. As a research algorithm, aσ–based detection methodusing the thresholds in Table 1 was developed to detectcloud/aerosol features from the aggregated CALIOPdata. The method was able to produce morphology sim-ilar to that from the CALIPSO L2 products but showedslightly higher percentages than the Level 2 results.

6. The observed feature seasonal variations in the TTL re-gion are generally consistent among all the CALIOPchannels. However, there is an increasing differencebetween the 532-nm and 1064-nm occurrence frequen-cies, which may be associated with the increasing noisein the 1064-nm channel.

7. An analysis with the normalized PDFs on the day-night difference in January 2008 suggest a higher oc-currence frequency at 1:30 p.m. than at 1:30 a.m. inthe TTL region. This is opposite to the result inferredfrom theσ–based detection method if different thresh-olds are used, but would be consistent with the resultif a fixed and larger-than-daytime-noise threshold wereused (e.g., 0.003 km−1sr−1).

By reducing the data volume of the original CALIOP Level 1data, we are able to process a large quantity of the lidar dataand study long-term variability of cloud/aerosol features aswell as the measurement noise. With a better understandingof the measurement noise in the aggregated data, we are ableto further investigate the CALIOP data in conjunction withother A-train observations (e.g., CloudSat and Aura MLS) tostudy cloud/aerosol variations in the UT/LS region. The re-search algorithm developed here can be readily applied to fu-ture multi-sensor studies with different spatial averaging anddetection thresholds if needed. As suggested in Jiang et al.[2008], cloud ice particle sizes may vary with polluted andclean environments as pollution aerosols can make their wayinto the UT/LS (Li et al., 2005; Fu et al., 2006; Park et al.,2009; Wu et al., 2010). Because cloud/aerosol occurrence re-duces sharply near the tropopause, accurate and careful sta-tistical analyses are required to extract these weak signals.

This study provides an initial evaluation on the CALIOPmeasurement noise and sensitivity in the UT/LS region, ofwhich the results will used to guide future studies involvingdifferent spatial averaging and detection thresholds.

Acknowledgements.This work was performed at the Jet PropulsionLaboratory, California Institute of Technology, under contract withthe National Aeronautics and Space Administration (NASA). Wewould like to thank Z. Liu, Y. Hu, W. Hunt, C. Trepte, M. Vaughan,and D. Winker for helpful discussions on CALIPSO instrument anddata analysis. The data processing and distribution by the NASALangley Research Center Atmospheric Sciences Data Center aregratefully acknowledged.

Edited by: T. J. Dunkerton

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characteristics of caliop attenuated backscatter noise ......clouds in the tropopause layer (e.g.,...

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