A Study of the Characteristics of Vertical Cloud Base Height Distribution Over Eastern China

atmosphere

Article A Study of the Characteristics of Vertical Cloud Base Height Distribution over Eastern China

Jiwei Xu 1,2 , Dong Liu 1,2,*, Zhenzhu Wang 1,2 , Decheng Wu 1, Siqi Yu 1,2 and Yingjian Wang 1,2

1 Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, Anhui, China; [email protected] (J.X.); [email protected] (Z.W.); [email protected] (D.W.); [email protected] (S.Y.); [email protected] (Y.W.) 2 University of Science and Technology of China, Hefei 230026, Anhui, China * Correspondence: [email protected]

Received: 9 May 2019; Accepted: 31 May 2019; Published: 4 June 2019

Abstract: Cloud is an important factor that affects weather and climate, and the vertical distribution of cloud determines its role in the atmospheric radiation transfer process. In this paper, the characteristics of different cloud types and their vertical cloud base height distributions over Eastern China are investigated with a four-year 2B-CLDCLASS-LIDAR product. The intercomparison of cloud base height distribution from ground-based lidar, CloudSat and CALIPSO measurements was studied with observations over the Hefei and Jinhua areas. The 2B-CLDCLASS-LIDAR product has the potential to uncover geographical and seasonal changes in cloud base height distribution over the Hefei area and Jinhua area, which may be beneficial for local climate models, although the CPR on CloudSat suffers from surface clutter or blind-zones. The results show that for non-precipitation cloud over the defined region (Eastern China), the occurrence frequencies of altocumulus, stratocumulus, and cirrus clouds are 29.4%, 21.0%, and 18.9%, respectively. The vertical occurrence frequencies of their cloud base heights are 0.5–8.5 km, below 3.5 km, and 5.5–17.0 km. The precipitation clouds are dominated by nimbostratus (48.4%), cumulus (17.9%), and deep convective clouds (24.2%), and their cloud base heights are all below 3.0 km. The cloud base height distributions have large differences below 3 km between the satellite measurement and ground-based measurement over Hefei site. Between the Hefei site and Jinhua site, the difference in cloud base height distribution measured by ground-based lidar is in good agreement with that measured by satellite over their matched grid boxes. Over the Hefei site, the vertical occurrence frequencies of cloud base height measured by ground-based lidar are higher than the satellite measurement within 0–0.5 km during all the seasons. It is suggested that more cloudy days may result from the sufficient water vapor environment in Hefei. In summer, the occurrence frequency of the cloud base height distribution at a height of 0–2.0 km is lower than other seasons over Jinhua city, which may be associated with the local weather system. Over the Jinhua site, the difference in seasonal cloud base height distribution based on satellite is in good agreement with that based on ground-based lidar. However, it does not appear over Hefei site. Thus, a multi-platform observation of cloud base height seems to be one of the essential ways for improvement in the observation of cloud macroscopic properties.

Keywords: cloud base height; cloud type; Eastern China; 2B-CLDCLASS-LIDAR; lidar

1. Introduction The extensive distribution of cloud macro-physical properties has an important eﬀect on the cloud radiative eﬀect associated with the atmospheric radiation transfer process, and on the cloud

Atmosphere 2019, 10, 307; doi:10.3390/atmos10060307 www.mdpi.com/journal/atmosphere Atmosphere 2019, 10, 307 2 of 11 precipitation associated with the water cycle process. The cloud amount, cloud height (base and top), and the occurrence time determine the cloud radiative effect [1–3]. During daytime, the cloud type and cloud height determine the cool or warm effect of a cloud to the surface [4]. At night, almost all types of clouds have a warming effect. However, our knowledge of cloud processes, which is one of the largest uncertainties in cloud parameterization for simulating models in atmospheric circulation and climate change [5,6], is inadequate. A long-term observation of the cloud is essential. Satellite observations provide an opportunity to assess cloud processes from a global perspective [7–9]. Cloud profiling radar on-board the CloudSat satellite combined with cloud-aerosol lidar on-board the CALIPSO satellite can scan the cloud from space and produce cloud products with complementary advantages of laser and microwave measurement for the cloud vertical structure [10–12]. Cloud vertical structure is relevant to the cloud micro-physical properties as well [13]. Cloud base height is one of the fundamental cloud variables of cloud vertical structure [14]. Except for detection in space, ground-based detections by using lidar have made a sizeable effort and contribution to monitoring the macro-physical properties of clouds, based on lidar networks [14,15]. Several researchers have studied the remote sensing of cloud vertical structure using CloudSat, CALIPSO, and ground-based observation together. Kim et al. reported that the cloud base height derived from the Cloud Profiling Radar (CPR) and ground-based lidar are generally in good agreement with each other (coefficient of determination in linear relationship, ~0.996) [16]. A good agreement in cloud top height and cloud base height with cirrus cloud optical depth between ground-based lidar and space-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP ) was found in Seoul, Korea [17]. Blanchard et al. [18] reported that cloud fraction occurrences from ground-based instruments correlated well with both CALIPSO operational products and combined CALIPSO-CloudSat retrievals, with a hit rate of 85%, and misdetections were mainly attributed to sensitivity loss and distance between the satellite track and the station. Comparisons of cloud base height detected by ceilometers and CALIPSO were also carried out in the southern Appalachian mountains [19]. With regard to the accurate estimation of variations in local cloud and the parameterization of local cloud models, observation samples of satellites are comparatively sparse. It is necessary to compare the cloud base height measured by the satellite with the ground-based detection. Thus, we collect the 2B-CLDCLASS-LIDAR product data and ground-based lidar data measured over Jinhua site or Hefei site to investigate the distribution of cloud base height over Eastern China. Section2 describes the 2B-CLDCLASS-LIDAR product and the method used to determine the cloud base height from lidar data. In Section3, the relationship between cloud base height and cloud type is investigated. The comparisons of vertical cloud base height distribution measured by 2B-CLDCLASS-LIDAR with ground-based lidar observations are given in Section3. Finally, a summary and conclusions are presented in Section4.

2. Experiments and Methods The cloud measurement products CloudSat and CALIPSO were collected for study. The level 2 cloud scenario classiﬁcation product 2B-CLDCLASS-LIDAR [8], which combines CPR and CALIOP from CloudSat and CALIPSO, provided the information on cloud type, cloud base height, and cloud precipitation (the product is available from the website http://www.cloudsat.cira.colostate.edu/data- products/level-2b/2b-cldclass-lidar). Four-year 2B-CLDCLASS-LIDARdata from 2007 to 2010 was used in this paper. The parameter “PrecipitationFlag” of 2B-CLDCLASS-LIDAR can be used to classify the precipitation situations of cloud layers. The number “0” for “PrecipitationFlag” represents a cloud with no precipitation. Numbers “1, 2, 3” for “PrecipitationFlag” mark the cloud layers with liquid precipitation, solid precipitation, and possible drizzle, respectively, which are regarded as precipitation cloud layers. The cloud types in 2B-CLDCLASS-LIDAR are identiﬁed as stratus (St), stratocumulus (Sc), cumulus (Cu, including cumulus congestus), nimbostratus (Ns), altocumulus (Ac), altostratus (As), deep convective (cumulonimbus), or high (cirrus and cirrostratus) cloud based on the “CloudLayerType” parameter. The “CloudLayerBase” stores the cloud base height information of a Atmosphere 2019, 10, 307 3 of 11

detected cloud layer. The research region, Eastern China, is limited from 25.0◦ N to 35.0◦ N and 110.0◦ E to 122.5◦ E, which mostly consists of land regions (see Figure1). We divided this area into grid boxes of 1.0 1.0 in latitude and longitude. Atmosphere◦ 2019× , 10◦ , x FOR PEER REVIEW 3 of 11 In the deﬁned region, two lidar observation sites (see Figure1) had been set up to measure theIn vertical the defined structure region, of macro-two lidar and observation micro-physical sites properties(see Figure of1) aerosolshad been and set clouds.up to measure The National the verticalInstitute structure for Environmental of macro- and Studies micro (NIES)-physical lidar inproperties the Hefei of site aerosols had thirty and months clouds. of observation The National data Institutefrom January for Environmental 2012 to October Studies 2014 (NIES) [20,21 lidar]. This inlidar the Hefei had three site h channelsad thirty atmonths wavelengths of observation of 532 nm dataand from 1064 January nm and 2012 depolarization to October at2014 532 [20,21] nm. The. This vertical lidar resolutionhad three waschannels 7.5 m at and wavelengths time resolution of 532 was nm15 and min, 1064 with nm a signaland depolarization accumulation at of 532 3000 nm. shots The in vertical 5 minutes. resolution The operated was 7.5 lidar m and in Jinhuatime resolution site was the wasDual-wavelength 15 min, with a signal Mie Polarization accumulation Raman of 3000 Lidar shots (DMPRL) in 5 minutes system. [The22], operat emittinged laserlidar pulsesin Jinhua at 532 site nm waands the 1064 Dual nm,-wavelength with a repetition Mie Polarization rate of 20 Hz.Raman The Lidar elastic (DMPRL scattered) system signals, [22] including, emitting depolarization laser pulses at at 532 532 nm nm and and Raman 1064 nm scattered, with signal a repetition at 607 nm, rate can of 20be received Hz. The simultaneously. elastic scattered The signal verticals, including resolution depolarizationwas 7.5 m and at 532 time nm resolution and Raman was scattered 30 s. The signal DMPRL at 607 was nm, used can be for received detection simultaneously. during June 2013 The to verticalJuly 2014 resolution in Jinhua was area. 7.5 m and time resolution was 30 s. The DMPRL was used for detection during June 2013 to July 2014 in Jinhua area.

FigureFigure 1. The 1. The region region defined deﬁned in this in this study study is shaded is shaded by bya rectangle a rectangle with with dashed dashed lines. lines. The The two two lidar lidar observationobservation sites sites are are marked marked with with the the blue blue dot dot (Hefei) (Hefei) and and red red dot dot (Jinhua) (Jinhua),, respectively. respectively. The The smaller smaller rectanglerectangle is used is used in Figure in Figure 7c.7c.

ThereThere are are many many methods methods to to determine determine the the cloud cloud base base height height from from lidar lidar signal signalss such such as as the the differentialdifferential zero zero-crossing-crossing method method [23] [23, threshold], threshold method method [23 [–2325]–25, multiscale], multiscale detection detection algorithm algorithm [26] [26, ], semidiscretizationsemidiscretization processing processing technique technique [27] [27, and], and so soon on[12,28] [12,28. One]. One of ofthe the tough tough problems problems of ofcloud cloud layerlayer determination determination from from lidar lidar signal signal profiles profiles is the is the treatment treatment of ofnoise. noise. All All the the above above methods methods more more or orless less eliminat eliminatee noise noise in indifferent different ways. ways. In Inthis this study, study, every every tiny tiny feature feature is isdistinguished distinguished using using a a differentialdifferential zero zero-crossing-crossing method method,, as asbefore before [29] [29. Then]. Then,, the the feature feature layers layers can can be beidentified identified as ascloud cloud layerslayers or oraerosol aerosol layers. layers. Fu Furthermore,rthermore, the the volume volume depolarization depolarization ratio ratio (VDR) (VDR) profiles profiles at 532 at 532 nm nm are are usedused in insome some scenes scenes to toimprove improve the the accuracy accuracy of of identified identified cloud cloud results. results. The The detailed detailed algorithm algorithm flow flow is is shown shown inin FigureFigure2 . 2 What. What needs needs to be to mentioned be mentioned is that is that we set we three set three thresholds thresholds for clouds for cloud in di ffs erentin differentsituations. situation In termss. In of terms general of general cloud (middle-level cloud (middle cloud),-levelit cloud), has the it apparent has the apparent differentia differentia of the value of of thefeature value of peak feature return peak signal return (RS) signal minus (RS feature) minus base feature RS, compared base RS, tocompared the aerosol to the layer. aerosol For lower layer. clouds, For lowerthey cloud alwayss, they have always an obvious have attenuationan obvious toattenuation lidar signal, to andlidar their signal, effective and their cloud effective top height, cloud which top is height,lower which than theis lower real cloud than the top real height, cloud can top be measuredheight, can due be measured to the fact due that to the the laser fact goes that through the laser the goescloud through and decreasesthe cloud toand the decreases background to the signal background level. For signal thin cloud level. appearingFor thin cloud at high appearing altitude, at it is highhard altitude, to be discriminated it is hard to frombe discriminated the aerosol layer from due the to aerosol the weak layer return due signal, to the however, weak return the VDR signal, has a however,great advantage the VDR inhas this a great task [advantage30]. Figure 3in illustrates this task the[30] process. Figure of3 illustrates cloud layer the detection. process Theof cloud initial layer detection. The initial search height is 150 m, which is the top height of the blind area. The threshold value of an averaged cloud layer VDR is 0.1 for high cloud or cirrus cloud. The cloud occurrence frequency (Fcloud) is derived from the ratio of the number of cloud occurrences Ncloud per grid box over the number of observations Nprofiles in that grid box: Fcloud = Ncloud / Nprofiles. It can be used to derive the occurrence frequency FX of a given cloud type X. The occurrence frequency of cloud base height is derived by the same method for each grid box: FCBH = Nbin / Ntotal. The Nbin and Ntotal represent the number of cloud base of eight types in each bin, divided from vertical

Atmosphere 2019, 10, x FOR PEER REVIEW 4 of 11 space (0–20 km) at a step of 0.5 km, and the total number in all bins, respectively. This method is applied for every season to derive a seasonally averaged frequency in every grid box. In our study, if multi-layer cloud appeared, we only considered the first cloud layer. This is because, on the one hand, the lidar signal suffers from extinction, preventing it from detecting any other upper cloud layers. On the other hand, the combined CALIPSO and CloudSat cloud product FigureFigure 2. F 2.lowFlow chart chart of the of thecloud cloud layer layer detection detection algorithm. algorithm. (2B-CLDCLASS-LIDAR) can describe multi-layer cloud depending on CPR, due to its advantage of cloud penetrationThe cloud occurrence capability frequency. The satellite (Fcloud observation) is derived from time the over ratio the of the defined number region of cloud wa occurrencess limited (alwaysNcloud midnightper grid boxand overnoon the at local number standard of observations time), henceNprofiles, the cloudin that fraction grid box: measuredFcloud = byN cloudthe satellite/ Nprofiles . or Itground can be-based used to observation derive the occurrencewas unmatched. frequency Therefore, FX of a to given reduce cloud this type influence X. The,occurrence the ground frequency-based observationof cloud base time height was limited is derived during by the13:00 same–15:00 method and 1:00 for– each3:00 at grid local box: standardFCBH = timeNbin (LST)./ Ntotal The. The yearNbin timeframeand Ntotal differencerepresent and the its number influence of cloud among base the of three eight observation types in each databases bin, divided is illustrated from vertical in Section space 3. (0–20 km) at a step of 0.5 km, and the total number in all bins, respectively. This method is applied for every season to derive a seasonally averaged frequency in every grid box. In our study, if multi-layer cloud appeared, we only considered the first cloud layer. This is because, on the one hand, the lidar signal suffers from extinction, preventing it from detecting any other upper cloud layers. On the other hand, the combined CALIPSO and CloudSat cloud product (2B-CLDCLASS-LIDAR) can describe multi-layer cloud depending on CPR, due to its advantage of cloud penetration capability. The satellite observation time over the defined region was limited (always midnight and noon at local standard time), hence, the cloud fraction measured by the satellite or ground-based observation was unmatched. Therefore, to reduce this influence, the ground-based observation time was limited during 13:00–15:00 and 1:00–3:00 at local standard time (LST). The year timeframeFigure 3. diA ffcaseerence of cloud andFigure itslayer 2. influence F detection:low chart among of(a) the Thethe cloud range three layer-corrected observation detection signal algorithm. databases at 532 nm; is (b) illustrated the potential in Section 3. cloud mask after feature identification; (c) the cloud mask represents the cloud layers. This case was measured in Jinhua on June 5, 2013.

3. Results The geographical distributions of normal profile number and cloud fraction derived over 2007– 2010 are shown in Figure 4. The normal profile number is defined as the ratio of the amount of profiles in a single 1.0° × 1.0° grid box to the maximal amount of all grid boxes in the defined region. The maximal amount of profiles is 15237. In Figure 4a, it clearly shows that the seasonal variation can be derived based on about 2000–3000 profiles in each grid box. Figure 4b shows the heterogeneity in geographical distribution. The grid containing Jinhua site has a larger cloud fraction than that of the grid containing Hefei site.

FigureFigure 3. A 3. caseA case of ofcloud cloud layer layer detection: detection: (a) (Thea) The range range-corrected-corrected signal signal at 532 at 532 nm; nm; (b) ( bthe) the potential potential cloudcloud mask mask after after feature feature identification; identiﬁcation; (c) (thec) the cloud cloud mask mask represents represents the the cloud cloud layers. layers. Th Thisis case case wa wass measuredmeasured in inJinhua Jinhua on onJune June 5, 2013. 5, 2013.

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Atmosphere 2019, 10, 307 5 of 11

Figure 4. (a) Normal number of profiles per grid box. The maximal amount of profiles is 15237; (b) 3. ResultsThe geographical distribution of cloud occurrence frequency; the cross symbols correspond to the lidar sites. The geographical distributions of normal profile number and cloud fraction derived over 2007–2010 are shown in Figure4. The normal profile number is defined as the ratio of the amount of profiles in a It is well known that the cloud base height of non-precipitation cloud has an extensive single 1.0 1.0 grid box to the maximal amount of all grid boxes in the defined region. The maximal distribution◦ × in the◦ vertical direction. In contrast, precipitation cloud has a lower height especially for amount of profiles is 15237. In Figure4a, it clearly shows that the seasonal variation can be derived convective precipitation cloud. Figure 5 shows the occurrence frequency of cloudless, non- precipitationbased on about cloud 2000–3000 or precipitation profiles in eachcloud grid (Figure box. Figure5a) and4b the shows occurrence the heterogeneity frequency in of geographical eight cloud distribution. The grid containing Jinhua site has a larger cloud fraction than that of the grid containing types of non-precipitation cloud or precipitation cloud (Figure 5b) over the defined region. In Figure Atmosphere5Hefeia, it is site. obvious2019, 10, x thatFOR PEERnon- precipitationREVIEW cloud dominates the cloud fraction. The cloudless occurrence5 of 11 frequencies of ground-based lidars over Hefei site (which is very close to the result in a previous study [21]) and Jinhua site are higher than that of 2B-CLDCLASS-LIDAR over the defined region. However, the grid box containing Hefei site has a better agreement with the ground-based lidar measurement than the equivalent comparison for Jinhua. The difference in Jinhua site is because the grid box area is larger than a single-point and because of the variability in year-average cloud fraction among the different year timeframes. In Figure 5b, for precipitation cloud, the occurrence frequency of cumulus, nimbostratus, and deep convection cloud, collectively, is higher than 17%. As for non- precipitation cloud, the occurrence frequency of cirrus, altocumulus, and stratocumulus cloud, collectively, is higher than 18%. That is to say, the contributions to cloud base height of cirrus, altocumulus, and stratocumulus clouds of non-precipitation cloud cannot be ignored in the cloud FigureFigure 4. 4. (a)(a )Normal Normal number number of of profiles profiles per gridgrid box.box. TheThe maximalmaximal amount amount of of profiles profiles is is 15237; 15237 (b; )(b) The base distribution. Thegeographical geographical distribution distribution of cloud of cloud occurrence occurrence frequency; frequency the cross; the symbols cross symbols correspond correspond to the lidar to sites.the lidar sites.

It is well known that the cloud base height of non-precipitation cloud has an extensive distribution in the vertical direction. In contrast, precipitation cloud has a lower height especially for convective precipitation cloud. Figure 5 shows the occurrence frequency of cloudless, non- precipitation cloud or precipitation cloud (Figure 5a) and the occurrence frequency of eight cloud types of non-precipitation cloud or precipitation cloud (Figure 5b) over the defined region. In Figure 5a, it is obvious that non-precipitation cloud dominates the cloud fraction. The cloudless occurrence frequencies of ground-based lidars over Hefei site (which is very close to the result in a previous study [21]) and Jinhua site are higher than that of 2B-CLDCLASS-LIDAR over the defined region. However, the grid box containing Hefei site has a better agreement with the ground-based lidar measurement than the equivalent comparison for Jinhua. The difference in Jinhua site is because the Figure 5. (a) Occurrence frequencies of cloudless, non-precipitation cloud, and precipitation cloud. grid box area is larger than a single-point and because of the variability in year-average cloud fraction FigureThe blue 5. cross(a) Occurrence sign corresponds frequenc toies the of cloudless cloudless, occurrence non-precipitation frequency cloud for Hefei, and lidar precipitation site, and thecloud. red among the different year timeframes. In Figure 5b, for precipitation cloud, the occurrence frequency Theone forblue Jinhua cross sign lidar corresponds site. The cross to symbolthe cloudless indicates occurrence the matched frequency grid box. for Hefei (b) Occurrence lidar site, and frequencies the red of cumulus, nimbostratus, and deep convection cloud, collectively, is higher than 17%. As for non- oneof eight for Jinhua cloud typeslidar site for. non-precipitationThe cross symbol cloudindicates or precipitation the matched cloud.grid box. (b) Occurrence frequencies precipitationof eight cloud cloud, types the for occurrence non-precipitation frequency cloud ofor precipitation cirrus, altocumulus cloud. , and stratocumulus cloud, collectively,It is well is knownhigher that than the 18 cloud%. That base is height to say of, the non-precipitation contributions cloudto cloud has an base extensive height distribution of cirrus, altocumulus3.1.in the Cloud vertical base, and Height direction. stratocumulus and InCloud contrast, Type cloud precipitations of non-precipitation cloud has acloud lower can heightnot be especially ignored forin the convective cloud base distribution. precipitationOver thecloud. defined Figure region,5 shows the cloud the occurrence base height frequency parameter of of cloudless, the 2B-CLDCLASS non-precipitation-LIDAR cloudproduct or canprecipitation be used to cloud investigate (Figure the5a) relationship and the occurrences between frequency cloud base of eightheight cloud and clou typesd type. of non-precipitation Figure 6 shows thecloud occurrence or precipitation frequencies cloud of cloud (Figure base5b) height over the for defined eight cloud region. types In. For Figure non5-a,precipitation it is obvious cloud that in Figurenon-precipitation 6a, the occurrence cloud dominatesfrequency of the cloud cloud base fraction. height can The be cloudless divided into occurrence three types frequencies, including of ground-based lidars over Hefei site (which is very close to the result in a previous study [21]) and Jinhua site are higher than that of 2B-CLDCLASS-LIDAR over the defined region. However, the grid box containing Hefei site has a better agreement with the ground-based lidar measurement than the equivalent comparison for Jinhua. The difference in Jinhua site is because the grid box area is larger than a single-point and because of the variability in year-average cloud fraction among the different year timeframes. In Figure5b, for precipitation cloud, the occurrence frequency of cumulus, nimbostratus, and deep convection cloud, collectively, is higher than 17%. As for non-precipitation cloud, the occurrence frequency of cirrus, altocumulus, and stratocumulus cloud, collectively, is

Figure 5. (a) Occurrence frequencies of cloudless, non-precipitation cloud, and precipitation cloud. The blue cross sign corresponds to the cloudless occurrence frequency for Hefei lidar site, and the red one for Jinhua lidar site. The cross symbol indicates the matched grid box. (b) Occurrence frequencies of eight cloud types for non-precipitation cloud or precipitation cloud.

3.1. Cloud base Height and Cloud Type Over the defined region, the cloud base height parameter of the 2B-CLDCLASS-LIDAR product can be used to investigate the relationships between cloud base height and cloud type. Figure 6 shows the occurrence frequencies of cloud base height for eight cloud types. For non-precipitation cloud in Figure 6a, the occurrence frequency of cloud base height can be divided into three types, including

Atmosphere 2019, 10, 307 6 of 11 higher than 18%. That is to say, the contributions to cloud base height of cirrus, altocumulus, and stratocumulus clouds of non-precipitation cloud cannot be ignored in the cloud base distribution.

3.1. Cloud Base Height and Cloud Type Over the deﬁned region, the cloud base height parameter of the 2B-CLDCLASS-LIDAR product can be used to investigate the relationships between cloud base height and cloud type. Figure6 shows the occurrence frequencies of cloud base height for eight cloud types. For non-precipitation cloud in Atmosphere 2019, 10, x FOR PEER REVIEW 6 of 11 Figure6a, the occurrence frequency of cloud base height can be divided into three types, including high type,type, middlemiddle type, type and, and low low type. type. The The cirrus cirrus cloud cloud can can be regardedbe regard ased the as high the type,high withtype, thewith range the ofrange cloud of base cloud height base from height 5.5 from to 17 km.5.5 to The 17 middle km. The type middle consists type of altostratus consists of cloud altostratus and altocumulus cloud and cloud,altocumulus with the cloud cloud, with base the height cloud ranging base height from ranging 0.5 to 8.5 from km. 0.5 The to 8.5 other km. cloud The other types cloud with cloudtypes with base heightscloud base below height 3.5s km below are regarded3.5 km are as regarded low cloud. as Aslow for cloud. the precipitation As for the precipitation cloud in Figure cloud6b, in with Figure the peak6b, with in occurrence the peak in frequency occurrence at 1frequency to 1.5 km, at the 1 cloudto 1.5 basekm, the heights cloud for base all ofheights the eight for cloudall of typesthe eight are belowcloud types 3 km. are below 3 km.

Figure 6.6. Occurrence frequenciesfrequencies of cloud basebase heightsheights forfor eighteight cloudcloud types:types: ((a)a) Non-precipitationNon-precipitation cloud; ((b)b) precipitationprecipitation cloud.cloud.

3.2. Comparison of Cloud base Base Height Height Distribution Distribution between between Two Two Ground Ground-Based-Based Observation Sites The retrieval retrieval method method of cloud of cloud base heights base height froms lidar from data lidar is described data isin described Section2. Unfortunately, in Section 2. theUnfortunately, observation the time observation of the lidar time data of is the later lidar than data the is 2B-CLDCLASS-LIDAR later than the 2B-CLDCLASS product-LIDAR data. Therefore, product wedata. attempt Therefore, to counterbalance we attempt to thecounterbalance influence of the timeframeinfluence of problem the timeframe in this study.problem Figure in this7 shows study. theFigure occurrence 7 shows frequency the occurrence of cloud frequency base height of cloud derived base from height ground-based derived from lidars ground in Hefei-based and lidars Jinhua in sitesHefei and and fromJinhua 2B-CLDCLASS-LIDAR sites and from 2B-CLDCLASS in matched-LIDAR grid in boxes.matched The grid cloud boxes. base The height cloud base occurrence height frequencyoccurrence of frequency 2B-CLDCLASS-LIDAR of 2B-CLDCLASS or ground-based-LIDAR or ground lidar over-based Jinhua lidar city over has Jinhua good consistency city has good in theconsistency vertical cloudin the basevertical height cloud distribution. base height For distribution. the ground-based For the lidarground in- Hefeibased city, lidar the in occurrenceHefei city, frequencythe occurrence of cloud frequency base height of cloud is di ffbaseerent height at 11 kmis different and below at 11 3 km. km Thereand below may be3 km. three There reasons may here be forthree this. reasons The firsthere is for year this variation. The first (timeframe is year variation problem). (timeframe Because problem of the di). ffBecauseerent year of the timeframes different ofyear the timeframes three databases, of the the three difference databases, between the the difference lidar measurement between the and lidar the measurement matched grid boxand will the bematched enlarged. grid Figurebox will7c illustratesbe enlarged. the Figure standard 7c illustrate deviationss the of standard cloud base deviations height distributions of cloud base during height thedistribution four years.s during The cloud the fourbase heightyears. distributionThe cloud base near height 1 km has distribution a larger fluctuation near 1 km in has each a season. larger However,fluctuation below in each 1.5 season. km, the However differences, below between 1.5 km, ground-based the difference lidarss between and ground satellites-based are larger lidars than and thesatellite yearsvariations, are larger whichthan the may year be variations caused by, thewhich two may reasons be cau below.sed Theby the second two reasons reason is below from. lidar The measurement.second reason Theis from single-point lidar measurement observation. T hashe asingle representativeness-point observation problem. has a Therepresentativeness third reason is fromproblem. satellite The measurement.third reason is Thefrom Surface satellite clutter measurement or blind-zone. The of Surface the radar clutter on CPR or blind brings-zone about of the misdetectionradar on CPR of brings the lower about cloud the layers.misdetection The first of andthe thirdlower reasons cloud layers. need to The be highlyfirst and considered. third reasons need to be highly considered.

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Figure 7. Occurrence frequency of cloud base height measured by CALIPSO and CloudSat or by Figure 7. OccurrenceOccurrence frequency frequency of of cloud cloud base base height height measured measured by by CALIPSO and and CloudSat or by ground-based lidar: (a) Hefei site; (b) Jinhua site; (c) the standard deviations of cloud base height groundground-based-based lidar lidar:: (a) (a) Hefei Hefei site; site; (b) (b) Jinhua Jinhua site; site; (c) (c) the standard deviation deviationss of cloud base height distribution during the four years and the grid boxes area (smaller rectangle) are shown in Figure 1. distribution during the four years and thethe grid boxes area (smaller rectangle) areare shownshown inin FigureFigure1 1.. The detailed differences in cloud base height distribution between the two ground-based The detaildetaileded difference diﬀerencess in cloud base height distribution between the two ground-basedground-based observation sites are shown in Figure 8. In Figure 8b, Hefei site has a decreasing frequency of the observation sites sites are are shown shown in in Figure Figure 88.. In In Figure Figure 88b,b, Hefei Hefei site site has has a adecreasing decreasing frequency frequency of ofthe cumulus and stratocumulus cloud fractions, which leads to the decreasing frequency of cloud base cumulusthe cumulus and and stratocumulus stratocumulus cloud cloud fractio fractions,ns, which which leads leads to to the the decreasing decreasing frequency frequency of of cloud cloud base base height at 1–2 km (see Figure 8a) in both ground-based lidar and satellite measurement, because the height at 1 1–2–2 km (see Figure 88a)a) inin bothboth ground-basedground-based lidarlidar andand satellitesatellite measurement,measurement, becausebecause thethe cloud base heights of cumulus and stratocumulus contribute to the cloud base height distribution at cloud base height heightss of cumulus and stratocumulus contribute to the cloud base height distribution at about 0–3 km (see Figure 6). Hefei site has an increasing frequency of cirrus cloud fraction, however about 0 0–3–3 km (see Figure 66).). HefeiHefei sitesite hashas anan increasingincreasing frequencyfrequency ofof cirruscirrus cloudcloud fraction,fraction, howeverhowever the ground-based lidar measurement has an inverse change at about 11 km. This is because the laser the ground ground-based-based lidar measurement has an inverse change at about 11 km. This is because the laser energy of lidar in Hefei city is weaker than that in Jinhua city, which results in the missing energy of of lidar lidar in inHefei Hefei city cityis weaker is weaker than that than in Jinhuathat in city, Jinhua which city, results which in the results missing in measurement the missing measurement of the cirrus cloud over Hefei area. It is obvious that satellite and ground-based lidar measurementof the cirrus cloud of the over cirrus Hefei cloud area. over It is Hefei obvious area. that It is satellite obvious and that ground-based satellite and lidarground measurements-based lidar measurements have a good agreement for the change in cloud base height distribution between the measurementhave a good agreements have a good for the agreement change infor cloud the change base height in cloud distribution base height between distribution the two between sites, which the two sites, which indicates that the 2B-CLDCLASS-LIDAR can appropriately distinguish the cloud twoindicates sites, that which the indicates 2B-CLDCLASS-LIDAR that the 2B-CLDCLASS can appropriately-LIDAR distinguishcan appropriately the cloud distinguish type fraction the change cloud type fraction change between the two sites. The large divergence between the two measurements at typebetween fraction the twochange sites. between The large the divergencetwo sites. The between large divergence the two measurements between the two at about measurements 1–2 km may at about 1–2 km may result from yearly variation. aboutresult 1 from–2 km yearly may variation.result from yearly variation.

Figure 8. (a) Occurrence frequencies of cloud base height between the two observation sites; Figure 8. (a) Occurrence frequencies of cloud base height between the two observation sites; (b) Figure(b) Occurrence 8. (a) Occurrence frequencies frequenc of cloudies types of cloud between base the height two observation between the sites. two The observation columns above sites; the(b) Occurrence frequencies of cloud types between the two observation sites. The columns above the Occurrencehorizontal axis frequenc indicateies anof increasecloud types in the between Hefei site. the two observation sites. The columns above the horizontal axis indicate an increase in the Hefei site. horizontal axis indicate an increase in the Hefei site. 3.3. Seasonal Variation in Cloud Base Height Based on Satellite and Ground-Base Observations 3.3. Seasonal variation in Cloud base Height based on Satellite and Ground-Base Observations 3.3. SeasonalTo respectively variation investigate in Cloud base the Height seasonal based variation on Satellite in cloud and Ground base height-Base distributionObservations for the satellite To respectively investigate the seasonal variation in cloud base height distribution for the and ground-basedTo respectively observations investigate in the the seasonal two sites, variation Figure9 showsin cloud the base seasonal height occurrence distribution frequencies for the satellite and ground-based observations in the two sites, Figure 9 shows the seasonal occurrence satelliteof cloud and base ground height- inbased four observation seasons. Overs in Hefeithe two site, sites the, cloudFigure with 9 shows cloud the base seasonal height at occurrence 0–0.5 km frequencies of cloud base height in four seasons. Over Hefei site, the cloud with cloud base height at frequencies of cloud base height in four seasons. Over Hefei site, the cloud with cloud base height at

Atmosphere 2019, 10, x FOR PEER REVIEW 8 of 11 Atmosphere 2019, 10, 307 8 of 11 Atmosphere 2019, 10, x FOR PEER REVIEW 8 of 11 0–0.5 km (cumulus [7]) has a higher frequency in 2B-CLDCLASS-LIDAR, whose difference is higher than the yearly variation at the corresponding height (see Figure 7c). This is probably because the 0(cumulus–0.5 km (cumulus [7]) has a [7] higher) has frequencya higher frequency in 2B-CLDCLASS-LIDAR, in 2B-CLDCLASS- whoseLIDAR, di whosefference difference is higher is than higher the persistent high humidity environment [31] brings more rainy days. This phenomenon is more thanyearly the variation yearly variation at the corresponding at the corresponding height (see height Figure (see7c). Figure This is 7c). probably This is because probably the because persistent the obvious in the monsoon period [32] (see Figure 9b and Figure 9d). However, this phenomenon does persistenthigh humidity high environment humidity environment [31] brings more[31] bring rainys days.more This rainy phenomenon days. This isphenomenon more obvious is in more the not appear in the satellite measurement. In summer over Jinhua area, the East Asian monsoon does obviousmonsoon in period the monsoon [32] (see period Figure [32]9b,d). (see However, Figure 9bthis and phenomenon Figure 9d). However, does not th appearis phenomenon in the satellite does not bring more low clouds to the contrary, it brings less low clouds (see Figure 9f) due to the control notmeasurement. appear in the In summersatellite measurement. over Jinhua area, In summer the East Asianover Jinhua monsoon area does, the notEast bring Asian more monsoon low clouds does of the western North Pacific subtropical high after the plum rain season. The difference correlations notto the bring contrary, more itlow brings clouds less to low the cloudscontrary, (see it Figurebrings9 lessf) due low to clouds the control (see ofFigure the western 9f) due Northto the control Pacific for seasonal variation in cloud base height distribution between the satellite and ground-based ofsubtropical the western high North after Pacific the plum subtropical rain season. high The after diff theerence plum correlations rain season. for The seasonal difference variation correlations in cloud observations in the two sites are shown in Figure 10. The difference for seasonal variation in cloud forbase seasonal height distribution variation in between cloud the base satellite height and distribution ground-based between observations the satellite in the twoand sites ground are- shownbased base height distribution is derived from the frequencies of the cloud base height minus the averaged observationin Figure 10.s Thein the di fftwoerence sites for are seasonal shown variation in Figure in 10 cloud. The base difference height for distribution seasonal isvariation derived in from cloud the seasonal fraction during the four seasons. Over Hefei site, the slope of linear fitting is 0.55, which is basefrequencies height distribution of the cloud is base derived height from minus the frequencies the averaged of seasonalthe cloud fraction base height during minus the the four averaged seasons. less than that in Jinhua site. The grid box containing Jinhua site has a clearer description of the seasonalOver Hefei fraction site,the during slope the of four linear seasons. fitting isOver 0.55, Hefei which site, is lessthe slope than thatof linear in Jinhua fitting site. is 0.55, The which grid box is seasonal change in cloud base height distribution in Jinhua site, although the year variation effect lesscontaining than that Jinhua in Jinhua site has site. a clearer The description grid box containing of the seasonal Jinhua change site has in cloud a clearer base descriptionheight distribution of the exists here. More comparisons between them should be presented. seasonalin Jinhua change site, although in cloud the base year height variation distribution effect exists in Jinhua here. More site, comparisonsalthough the between year variation them should effect existsbe presented. here. More comparisons between them should be presented.

Figure 9. Seasonal occurrence frequency of cloud base height measured by CALIPSO and CloudSat Figure 9. Seasonal occurrence frequency of cloud base height measured by CALIPSO and CloudSat or orFigure ground 9. Seasonal-based lidar. occurrence (a)-(d) frepresentrequency offour cloud-season base results height over measured Hefei bysite. CALIPSO (e)-(h) represent and CloudSat four- ground-based lidar. (a–d) represent four-season results over Hefei site. (e–h) represent four-season seasonor ground results-based over lidar. Jinhua (a) site.-(d) represent four-season results over Hefei site. (e)-(h) represent four- results over Jinhua site. season results over Jinhua site.

Figure 10. Seasonal occurrence frequency of cloud base height measured by CALIPSO and CloudSat or Figure 10. Seasonal occurrence frequency of cloud base height measured by CALIPSO and CloudSat ground-based lidar. orFigure ground 10. -Seasonalbased lidar. occurrence frequency of cloud base height measured by CALIPSO and CloudSat or ground-based lidar.

Atmosphere 2019, 10, 307 9 of 11

4. Conclusions The vertical distribution of cloud base height is an essential element for the radiation process and the climate model. We used the 2B-CLDCLASS-LIDAR product, which combines CloudSat and CALIPSO, and observational data from ground-based lidars to investigate the vertical distribution of cloud base height over the defined region (Eastern China). The method described in Section2 is used to retrieve the cloud base height from lidar measurements. The clouds are classified into precipitation and non-precipitation clouds to investigate the occurrence frequencies of eight cloud types and their cloud base heights. The occurrence frequency of non-precipitation cloud is dominated by altocumulus, stratocumulus, and cirrus clouds. The precipitation clouds have higher occurrence frequencies in nimbostratus, cumulus, and deep convective clouds. The occurrence frequencies of vertical cloud base height distribution measured by the two lidar sites are compared with the 2B-CLDCLASS-LIDAR product, as well as the seasonal results. The difference in occurrence frequencies of cloud types between the two sites is in good agreement with the difference in cloud base height distribution between the two sites, based on either satellite or ground-based measurements. The seasonal results for vertical cloud base height distribution over Jinhua area and Hefei area indicate the influences of local climate on cloud base height distribution. The difference in seasonal occurrence frequencies of cloud base height distributions between lidar and satellite measurements in Jinhua site indicates the seasonal resolution of cloud base height distribution by the 2B-CLDCLASS-LIDAR product. Therefore, the 2B-CLDCLASS-LIDAR product has the ability to differentiate the variations in cloud type fraction associated with the cloud base height distribution between the two sites, however the ability to differentiate the seasonal changes in cloud base height distribution is only potential in Jinhua site and is insufficient in Hefei site. Of course, the different timeframes is a negligible factor, which needs to be improved in future studies. The main reasons for the insufficiency aspect may be the limitations of CPR in the lower cloud layer. Cloud is the product of a dynamic and thermodynamic coupling process under local and large-scale climate influences. Lidar is a powerful tool to continuously monitor the cloud process. The gradually improving lidar network brings the possibility of complementation between satellite and ground-based observations of cloud macro-physical properties. The comparison of vertical cloud base height distribution is beneficial to this complementation. In addition, the understanding of the cloud base height distribution is important for the study of local weather models. The combined satellite and ground-based observations can better uncover the geographical and seasonal distribution of cloud base height. Therefore, in future studies we will compare more areas and look for the differences between these regions.

Author Contributions: The first author conducted the data analysis and manuscript writing. The second author provided direction to this paper. All the authors analyzed the results and revised the manuscript. Acknowledgments: This research was supported by the International Partnership Program of Chinese Academy of Sciences (Grant No. 116134KYSB20180114), and by the National Key R&D Program of China (Grant No. 2018YFB0504500) and funded by the Science and Technology Service Network Initiative (STS) under No. KFJ-STS-QYZD-022, and by the Youth Innovation Promotion Association CAS (2017482). The authors thank Prof. Zhien Wang of University of Colorado Boulder for the improvement advice to this paper. The authors would like to thank the Cooperative Institute for Research in the Atmosphere (CIRA) CloudSat Processing Data Center (DPC) for the CloudSat data (http://www.cloudsat.cira.colostate.edu), and the Atmospheric Science Data Center (ASDC) for CALIPSO data (https://www-calipso.larc.nasa.gov/). Conflicts of Interest: The authors declare no conflict of interest.

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