atmosphere
Article Evaluation of Fog and Low Stratus Cloud Microphysical Properties Derived from In Situ Sensor, Cloud Radar and SYRSOC Algorithm
Jean-Charles Dupont 1,*, Martial Haeffelin 2, Eivind Wærsted 3 ID , Julien Delanoe 4, Jean-Baptiste Renard 5, Jana Preissler 6 ID and Colin O’Dowd 6
1 Institut Pierre-Simon Laplace, École Polytechnique, UVSQ, Université Paris-Saclay, 91128 Palaiseau, France 2 Institut Pierre Simon Laplace, École Polytechnique, CNRS, Université Paris-Saclay, 91128 Palaiseau, France; [email protected] 3 Laboratoire de Météorologie Dynamique, École Polytechnique, Université Paris-Saclay, 91128 Palaiseau, France; [email protected] 4 Laboratoire Atmosphères, Milieux, Observations Spatiales/UVSQ/CNRS/UPMC, 78280 Guyancourt, France; [email protected] 5 LPC2E-CNRS/Université d’Orléans, 3A Avenue de la Recherche Scientifique, 45071 Orléans, France; [email protected] 6 Centre for Climate and Air Pollution Studie, National University of Ireland, Galway H91 CF50, Ireland; [email protected] (J.P.); [email protected] (C.O.) * Correspondence: [email protected]
Received: 15 March 2018; Accepted: 23 April 2018; Published: 2 May 2018
Abstract: The microphysical properties of low stratus and fog are analyzed here based on simultaneous measurement of an in situ sensor installed on board a tethered balloon and active remote-sensing instruments deployed at the Instrumented Site for Atmospheric Remote Sensing Research (SIRTA) observatory (south of Paris, France). The study focuses on the analysis of 3 case studies where the tethered balloon is deployed for several hours in order to derive the relationship between liquid water content (LWC), effective radius (Re) and cloud droplet number concentration (CDNC) measured by a light optical aerosol counter (LOAC) in situ granulometer and Bistatic Radar System for Atmospheric Studies (BASTA) cloud radar reflectivity. The well-known relationship Z = α × (LWC)β has been optimized with α [0.02, 0.097] and β [1.91, 2.51]. Similar analysis is done to optimize the relationship Re = f(Z) and CDNC = f(Z). Two methodologies have been applied to normalize the particle-size distribution measured by the LOAC granulometer with a visible extinction closure (R2 [0.73, 0.93]) and to validate the LWC profile with a liquid water closure using the Humidity and Temperature Profiler (HATPRO) microwave radiometer (R2 [0.83, 0.91]). In a second step, these relationships are used to derive spatial and temporal variability of the vertical profile of LWC, Re and CDNC starting from BASTA measurement. Finally, the synergistic remote sensing of clouds (SYRSOC) algorithm has been tested on three tethered balloon flights. Generally, SYRSOC CDNC and Re profiles agreed well with LOAC in situ and BASTA profiles for the studied fog layers. A systematic overestimation of LWC by SYRSOC in the top half of the fog layer was found due to fog processes that are not accounted for in the cloud algorithm SYRSOC.
Keywords: fog; cloud radar; microphysical properties
1. Introduction Accurate forecasts of atmospheric conditions resulting in low visibility, particularly on short timescales, have become an important issue [1–24]: Fog and low clouds often cause a strong reduction
Atmosphere 2018, 9, 169; doi:10.3390/atmos9050169 www.mdpi.com/journal/atmosphere Atmosphere 2018, 9, 169 2 of 19 of visibility, thus affecting various types of transport systems such as car, air and ship traffic, and the financial and human costs are comparable to those of tornadoes and storms [9]. Low visibility during fog events is a result of complex radiative, turbulent and microphysical processes as well as interactions between the atmospheric boundary layer (ABL) and the underlying surface [5–27]. The mechanisms of fog formation, development and dissipation are very complex and have been extensively studied with a series of numerical simulations and comprehensive observational programs including in situ measurements [9]. From the numerical modelling point of view, meteorological models usually lack accuracy due to poor horizontal [18] and vertical [25] resolutions, and inadequacies in microphysical parametrizations [9]. To explore the parameterization of microphysical processes in fog, it is essential to observe the spatial and temporal variations of key microphysical parameters related to fog, cloud droplet number concentration (CDNC), liquid water content (LWC), and effective radius (Re). Fog drop size distribution (DSD) is undoubtedly an important parameter for describing various fog types and the lifecycle stages of fog. All the key microphysical parameters are affected by DSD when fog develops [16]. Fog drop-size distributions pertain to various types with spatial and temporal variations [26], and even at different heights of fog layers, fog drop size distributions vary due to inhomogeneous microphysical structures in fog [28]. The variability of fog drop-size distributions is incurred by the combined effects of microphysical processes, such as nucleation, activation, growth, gravitational setting, turbulent mixing, and macro conditions such as wind and radiative fluxes [17]. Unfortunately, few data are available concerning LWC in fog events, as well as for single fog lifecycle stages. Balloon-borne systems are also unsuitable for continuous spatial and temporal measurements of the vertical fog structure. One possible solution comprises frequency-modulated continuous wave technique (FMCW) cloud radars that could provide continuous but indirect LWC measurements in a high temporal resolution [2–16]. Reliable Z-LWC relationships for fog events are paramount to retrieving LWC profiles from radar reflectivity: existing procedures use empirically-derived static relationships [7–22], and some are more advanced accounting for additional instrumentation such as a ceilometer [3] or microwave radiometer [14]. However, assumptions about the shape of the droplet-size distribution lead to inaccuracies in retrieved Z-LWC because Z is proportional to the sixth moment of the DSD (in Rayleigh approximation, valid for small drops i.e., for fog) while LWC is proportional to the third moment of the DSD. The objective of this study is (1) to derive microphysical property profiles from radar reflectivity, (2) to quantify the accuracy of retrievals with an in situ granulometer, and (3) to evaluate an algorithm to derive cloud-layer microphysical properties, the synergistic remote sensing of clouds (SYRSOC) algorithm [15]. In Section2, we describe the instrumental setup of the intensive observational periods (IOP) carried out at the Instrumented Site for Atmospheric Remote Sensing Research (SIRTA) observatory, the dataset available for the four case studies, and the SYRSOC algorithm used in this study. Section3 shows the parametric relationship used to derive the microphysical properties starting from radar reflectivity. In Section4, the vertical profiles of microphysical properties such as LWC, Re and CDNC derived in Section3 are compared to in situ sensor and SYRSOC output data.
2. Observational Dataset and Synergistic Remote Sensing of Clouds (SYRSOC) Algorithm
2.1. Intensive Observational Periods (IOP) and Three Tethered Balloon Flights SIRTA is a French national observatory dedicated to studying clouds, aerosols, dynamics and thermodynamics in the boundary layer and the free troposphere. The SIRTA observatory is a mid-latitude site (48.7◦ N, 2.2◦ E) located in a semi-urban area, on the Saclay plateau 25 km south of Paris [10]. Table1 describes the active and passive remote-sensing instruments and the main in situ sensors deployed at the SIRTA site for the IOP and used in this study. These IOP correspond to 6 January 2015, 19 December 2016, 3 January 2017 and 17 February 2017. On the three first dates, a tethered balloon carrying an optical particle counter was deployed to document fog and low-stratus microphysical properties, while Atmosphere 2018, 9, 169 3 of 19
duringAtmosphere the fog 2018 on, 9 17, x FOR February PEER REVIEW 2017 the balloon was not used and measurements were only3 of taken 18 at 4 m altitude. All instruments were set up in an area covering less than 1 km2. All times are universal 2 time (UT)than and1 km all. All altitudes times are are universal above ground time (UT) level and (agl). all altitudes Figure 1are shows above a ground time series level of (agl). the Figure cloud radar1 reflectivityshows (coloreda time series panel) of and the tetheredcloud radar balloon reflectivi (blackty markers)(colored panel) path for and 3 Januarytethered 2017balloon between (black 10:00 markers) path for 3 January 2017 between 10:00 and 17:00. Fog thickness reaches 300 m at 10:30 and and 17:00. Fog thickness reaches 300 m at 10:30 and maximum altitude reached by the tethered balloon maximum altitude reached by the tethered balloon is 250 m around 11:00. During this event we is 250performed m around 2 11:00.different During flights, thisone eventduring we the performedmorning (between2 different 10:15 flights and 13:00),, one and during one thebetween morning (between15:00 10:15 and 16:45. and 13:00), and one between 15:00 and 16:45.
TableTable 1. Active 1. Active and and passive passive remote remote sensing sensing instrumentsinstruments and and the the in-situ in-situ sensors sensors deployed deployed at the at the InstrumentedInstrumented Site Site for Atmosphericfor Atmospheric Remote Remote Sensing Sensing ResearchResearch (SIRTA) (SIRTA) site site and and used used for for this this study. study.
Type of Type of Name Parameters Sampling Uncertainty Instrument Name Parameters Sampling Uncertainty Instrument Bistatic Radar System Bistatic Radar System for Reflectivity, Doppler for Atmospheric Reflectivity, Doppler Atmospheric Studies velocity, cloud top 12 s 0.5 dBZ, 0.2 m/s Studies (BASTA) cloud velocity, cloud top 12 s 0.5 dBZ, 0.2 m/s (BASTA) cloud radar height radar (95 GHz) height Remote- (95 GHz) Humidity and Remote-sensingsensing Humidity and Temperature Temperature Profiler LiquidLiquid water water path path instrumentsinstruments Profiler (HATPRO) 5 min5 min LWPLWP ± 20± g/m²20 g/m 2 (HATPRO) microwave (LWP)(LWP) microwave radiometer radiometer CL31 Ceilometer Cloud Cloud base base heigh heightt 1 min 1 min 7.5 m 7.5 m CHM15K Ceilometer Cloud Cloud base base height height 1 min 1 min 7.5 m 7.5 m Degreane DF320 HorizontalHorizontal visibility visibility 1 min1 min ±10–25%±10–25% diffusometer (km)(km) at 4 at m 4 magl agl Particle-size In situ Particle-size In situ sensors Light optical aerosol distributiondistribution for for 1 min1 min sensors Light optical aerosol counter counter (LOAC) particlesparticles ranging ranging from from SamplingSampling (LOAC) granulometer granulometer 0.2 to0.2 50 to µm 50 (inµm 2.5 2.5L/min L/min diameter)(in diameter)
FigureFigure 1. Cloud 1. Cloud radar radar reflectivity reflectivity and and tetheredtethered balloon balloon path path on on 3 January 3 January 2017. 2017.
2.2. Remote-Sensing Instruments and In Situ Sensor 2.2. Remote-Sensing Instruments and In Situ Sensor A Degreane DF320 diffusometer (Degreane, La Garde, France) was operated near the ground (4 Am Degreaneagl). The instrument DF320 diffusometer provides horizontal (Degreane, visibility La Garde,range and France) particle was extinction operated at 550 near nm, the with ground (4 m agl10–25%). The uncertainty instrument [6]. provides horizontal visibility range and particle extinction at 550 nm, with 10–25% uncertaintyThe cloud base [6]. height and the cloud top height were derived from the CL31 (Vaisala, Vantaa, TheFinland) cloud or baseCHM15K height ceilometer and the (Lufft, cloud Berlin, top height Germany) were backscatter derived signal from theand CL31the BASTA (Vaisala, 95 GHz Vantaa, Finland)Doppler or CHM15K cloud radar ceilometer developed (Lufft, by the Berlin, LATMOS Germany) laboratory, backscatter respectively signal [2]. and The the ceilometer BASTA and 95 GHz radar blind zones are 15 m and 40 m, with vertical resolutions of 15 m and 12.5 m, respectively. This Doppler cloud radar developed by the LATMOS laboratory, respectively [2]. The ceilometer and radar zone corresponds to the altitude without information on lidar backscatter or radar reflectivity and blind zones are 15 m and 40 m, with vertical resolutions of 15 m and 12.5 m, respectively. This zone Doppler velocity. The cloud radar is operated in a vertically pointing mode and uses the frequency corresponds to the altitude without information on lidar backscatter or radar reflectivity and Doppler velocity. The cloud radar is operated in a vertically pointing mode and uses the frequency modulated Atmosphere 2018, 9, 169 4 of 19 Atmosphere 2018, 9, x FOR PEER REVIEW 4 of 18 continuousmodulated wave continuous approach wave at 95 approach GHz. Its at bi-static 95 GHz. configuration Its bi-static configuration allows us to allows reduce us theto reduce blind zonethe comparedblind zone to mono-static compared to radar. mono-static The 3.2 radar. mm wavelengthThe 3.2 mm wavelength gives a clear gives advantage a clear advantage compared compared to classical meteorologicalto classical meteorological radar in term radar of sensitivity in term of for sensitivity equivalent for equivalent antenna size antenna and emitted size and power, emitted the power, cloud radarthe is cloud perfectly radar suited is perfectly for the suited characterization for the charac of microphysicalterization of microphysica properties ofl properties fog and stratus of fog above and 40 mstratus height, above while 40 them height, CL31 while or CHM15K the CL31 ceilometer or CHM15K is totallyceilometer attenuated is totally when attenuated the fog when thickness the fog is morethickness than some is more tens than ofmeters. some tens The of sensitivitymeters. The ofsensitivity the radar of isthe estimated radar is estimated to be at aboutto be at− about40 dBZ −40 at 1 kmdBZ for at a temporal1 km for a resolutiontemporal resolution of 12 s and of 12.512 s mand range 12.5 m resolution range resolution [2]. [2]. TheThe Humidity Humidity and Temperatureand Temperature Profiler (RPG-HATPRO,Profiler (RPG-HATPRO, Radiometer Radiometer Physics Gmbh, Physics Meckenheim, Gmbh, Germany)Meckenheim, water vaporGermany) and water oxygen vapor multi-channel and oxygen microwave multi-channel profiler, microwave installed profiler, at SIRTA installed site since at FebruarySIRTA 2010, site since provides February time series2010, provides of the liquid time water series path of the when liquid fog water or clouds path occur,when andfog or integrated clouds occur, and integrated water vapor on the total column of the atmosphere. water vapor on the total column of the atmosphere. The Light Optical Aerosol Counter (LOAC) is a compact optical counter/sizer to perform The Light Optical Aerosol Counter (LOAC) is a compact optical counter/sizer to perform measurements of liquid and solid particles at ground level and under all kinds of balloons in the measurements of liquid and solid particles at ground level and under all kinds of balloons in the troposphere and in the stratosphere [20]. Particles are collected and are injected through a laser beam troposphere and in the stratosphere [20]. Particles are collected and are injected through a laser beam by a pumping system. LOAC is characterized by a sampling rate around 2.5 L/min through a tube of by a8 pumpingmm in diameter system. and LOAC 20 cm is characterizedin lengths. Measuremen by a samplingts of the rate light around scattered 2.5 L/min by the through particles a tubeare of 8performed mm in diameter at two scattering and 20 cm angles. in lengths. The first Measurements one is around of 12°, the and light is scatteredalmost insensitive by the particles to the ◦ arerefractive performed index attwo of the scattering particles; the angles. second The one first is around one is 60° around and is 12strongly, and sensitive is almost to the insensitive refractive to ◦ theindex refractive of the index particles. of the By particles; combining the measurements second one isat aroundthe two 60angles,and it is is strongly possible sensitiveto retrieve to the the refractiveconcentrations index of thefor 19 particles. size classes By combining between 0.2 measurements µm and 50 µm at thein diameter two angles, and it to is estimate possible tothe retrieve main thetypology concentrations of the forparticles 19 size (droplets, classes betweencarbonaceous, 0.2 µ mmineral and 50 particles,µm in diameter salts) when and the to estimatemedium theis mainrelatively typology homogeneous. of the particles This (droplets, typology carbonaceous,is based on calibration mineral charts particles, obtained salts) in when the laboratory the medium [21]. is relativelyThe homogeneous. LOAC sensor Thiscalibration typology procedure is based follows on calibration the standard charts calibration obtained inof an the optical laboratory counter, [21]. i.e.,The the LOAC particle sensor size calibrationis controlled procedure but not the follows number. the Latex standard beads, calibration which are of perfect an optical transparent counter, i.e.,spheres, the particle have sizebeen is used controlled for diamet buter not calibration the number. below Latex2 µm. beads,For the whichcalibration are in perfect the 5–45 transparent µm size spheres,range, have different been usednatures for diameterof irregular calibration grains belowhave 2beenµm. Forused: the glass calibration beads in(quite the 5–45 irregular),µm size range,carbonaceous different natures particles, of irregulardust sand grains of several have beentypes, used: ashes glass and beadssalts. Figure (quite irregular),2 shows an carbonaceous example of particles,particle-size dust sanddistribution of several measured types, with ashes the and LOAC salts. sensor Figure on2 3 shows January an 2017 example at 10:00 of and particle-size 11:00. At distribution10:00, LOAC measured is at the with ground the LOAC during sensor the fog on event 3 January with 2017more atthan 10:00 50 andparticles 11:00. bigger At 10:00, than LOAC 10 µm is at thewhereas ground at during11:00, LOAC the fog sensor event reaches with morethe top than of the 50 fog particles layer with bigger only than one 10tenthµm of whereas particles at larger 11:00, than 10 µm. LOAC sensor reaches the top of the fog layer with only one tenth of particles larger than 10 µm.
Figure 2. Particle-size distribution derived from LOAC sensor on 3 January 2017 at 10:00 and 11:00 Figure 2. Particle-size distribution derived from LOAC sensor on 3 January 2017 at 10:00 and 11:00 UT. UT.
Atmosphere 2018, 9, 169 5 of 19
The uncertainty for total concentrations measurements is ±20% when concentrations are higher than 5 particles cm−3 (for a 10-min integration time). The uncertainties in size calibration are ±0.025 µm for particles smaller than 1 µm, and 10% for greater particles. The measurement accuracy of submicron particles is reduced when the concentration of particles >3 µm exceeds a few particles cm−3. In the case of measurements inside a thick fog, several particles are present in the laser beam at the same time and the concentrations can be underestimated. Corrective coefficients have been established to tentatively correct from this effect and to retrieve the relative distribution of the particles. Nevertheless, this procedure becomes inaccurate for the highest concentrations; it is then necessary to normalize also the results with the total number of particles estimated from independent measurements. The following part presents a possible method to normalize the particle-size distribution by comparing it to the optical extinction measured with the diffusometer.
2.3. Visible Extinction Normalisation for Light Optical Aerosol Counter (LOAC) Validation The particle-size distribution in ambient conditions is measured by the LOAC sensor and used to derive the extinction coefficient σloac. The measured size distribution, according to Mie applicable to spherical droplet particles allows us to derive σloac as the following equation:
Dmax πD2 σ = N Q (1) loac ∑ 4 D ext(m,D) Dmin where Qext is the Mie extinction efficiency factor depending on the particle size D and the refractive index m. As for [6], we assume m = 1.33 for particles larger than 2 µm and m = 1.48 − 0.01i for particles smaller than 2 µm (derived from an AERONET sun photometerDmin equals to 0.1 µm and Dmax = 50 µm. D corresponds to mean diameter for the LOAC sensor and ND is the particles concentration for each size D. The extinction coefficient σDF is here derived from the measured horizontal visibility (DF320 measurement) according to the Koschmieder equation [12]:
− ln(VC) σ = (2) DF Visi where VC is the visual contrast defined here as 5%, and Visi is the horizontal visibility measured by the DF320 diffusometer [6]. Some visible extinction comparisons have been undertaken to compare extinction coefficient derived from LOAC sensor and measured by DF320 diffusometer (Figure3 for collocated measurement at 4 m agl. Figure3 on the left shows a time series of extinction coefficient derived from Equations (1) and (2). The black line corresponds to diffusometer data and the red line to LOAC processing. Fog occurs between 05:30 and 07:00. The following table shows slope (normalization factor), and correlation coefficient for four experimental closures in visible extinction according to the relationship as in Figure3 on the right. We note a significant difference for the normalization factor that can be explained by some physical phenomena. First, during the foggy situation considered on 6 January 2015 the temperature was about −1 ◦C with perhaps some ice crystal that can induce very important scattering inside the LOAC optical chamber leading to an overestimation of the optical extinction compared to true visibility measured by DF320 diffusometer. This explains why the slope between the LOAC extinction coefficient and the diffusometer extinction coefficient was 8.2 (Table2). For 17 February 2017, the slope is equal to 4.2 according to very high number of particles larger than 10 µm about 110 #/cm3 compared to 35 or 55 #/cm3 for 3 January 2017 and 19 December 2016, respectively. We had some problems with the electronic card for this IOP because of a water leak in the electronic box several days before. So, for this IOP the relative size distribution is biased toward the largest droplets leading to an overestimation of the calculated visible extinction (Equation (1)). For 19 December 2016 and 3 January 2017, the slope Atmosphere 2018, 9, 169 6 of 19 is 0.59 and 0.92, respectively, for an average visibility of 500 m and 180 m. Additional explanation is providedAtmosphere in Section 2018, 9, x3.5 FOR. PEER REVIEW 6 of 18
Figure 3. Time series of extinction coefficient derived from LOAC granulometer and DF320 Figurediffusometer 3. Time ( seriesleft) and of scatter extinction plot of coefficientLOAC extinction derived coefficient from versus LOAC DF320 granulometer extinction coefficient and DF320 diffusometerfor 19 December (left) and 2016 scatter (right plot). of LOAC extinction coefficient versus DF320 extinction coefficient for 19 December 2016 (right). Table 2. Coefficients used to normalize the LOAC cloud droplet number concentration (CDNC) compared to visible extinction derived from DF320 diffusometer for the four case studies at 4 m agl. Table 2. Coefficients used to normalize the LOAC cloud droplet number concentration (CDNC) Average Average Average CDNC Average Normalization compared to visible extinctionSlope derived# fromρ DF320Temp. diffusometer Wind Speed for the four(#/cm case3) studies atVisibility 4 m agl. Procedure (°C) (m/s) Total, and >10 µm (m) Average Average Average CDNC Average Normalization6 January 2015 8.2 86 0.93 −1.2 2.2 840/40 670 Slope # ρ Temp. Wind Speed (#/cm3) Visibility Procedure19 December 2016 0.59 76 0.88 3.4 0.5 465/55 500 (◦C) (m/s) Total, and >10 µm (m) 3 January 2017 0.92 157 0.73 0.9 1.8 930/35 180 6 January17 February 2015 2017 * 8.2 4.2 86 450.93 0.83 −1.2 7.6 2.2 1.1 840/40 270/110 320670 19 December 2016 0.59 76 0.88 3.4 0.5 465/55 500 * This case study is used in this part solely because the LOAC was fixed at the ground next to the 3 January 2017 0.92 157 0.73 0.9 1.8 930/35 180 17 Februarydiffusometer, 2017 * and4.2 we did 45 not deploy 0.83 the tethered 7.6 balloon; 1.1# corresponds to 270/110the sample for each day. 320 * This case study is used in this part solely because the LOAC was fixed at the ground next to the diffusometer, and we2.4. did SYRSOC not deploy Algorithm the tethered balloon; # corresponds to the sample for each day. SYRSOC is an algorithm for the calculation of microphysical cloud-property profiles from 2.4. SYRSOCcollocated Algorithm cloud radar, ceilometer or lidar, and microwave radiometer data. In this study, SYRSOC used backscatter signal from the CHM15k (Lufft) and CL31 (Vaisala) ceilometers to detect the cloud SYRSOC is an algorithm for the calculation of microphysical cloud-property profiles from base. Reflectivity from BASTA cloud radar was used to detect the cloud top and to calculate cloud collocateddroplet cloud number radar, concentration ceilometer or(CDNC) lidar, andcombined microwave with information radiometer from data. the In other this instruments. study, SYRSOC From used backscatterthe HATPRO signalfrom microwave the CHM15k radiometer (Lufft) (RPG, and Sectio CL31n (Vaisala) 2.2), liquid ceilometers water path to detect(LWP) thewas cloud used. base. ReflectivityTemperature from BASTA profiles cloud were radarderived was from used in to situ detect mast the measurements, cloud top and assuming to calculate an cloudadiabatic droplet numbertemperature concentration decrease (CDNC) with height. combined Under with the information assumption fromof a mono-modal the other instruments. cloud droplet From size the HATPROdistribution microwave of single-layer radiometer purely (RPG, liquid Section water 2.2 clouds,), liquid profiles water of pathRe and (LWP) LWC waswere used.calculated Temperature from profilesCDNC, were which derived is the from primary in situ output mast of measurements, SYRSOC. Further assuming details on an SYRSOC adiabatic were temperature given by [15] decrease and with height.[19]. Under the assumption of a mono-modal cloud droplet size distribution of single-layer For this study, an entrainment factor has been introduced to take into account entrainment of purely liquid water clouds, profiles of Re and LWC were calculated from CDNC, which is the primary dry air at the cloud top. The entrainment factor is calculated for the top 30% of the cloud, replicating output of SYRSOC. Further details on SYRSOC were given by [15] and [19]. the shape of the measured reflectivity profile. The ratio of radar reflectivity at each height bin to radar Forreflectivity this study, at 30% an entrainmentfrom the cloud factor top is has calculated. been introduced This height to dependent take into accountfactor is entrainmentthen multiplied of dry air at theto the cloud CDNC top. profile The entrainmentto produce more factor realistic is calculated shapes offor the theCDNC top profile, 30% of and the subsequently cloud, replicating of Re the shapeand of the LWC measured profiles. reflectivity profile. The ratio of radar reflectivity at each height bin to radar reflectivityThis at 30% is the from first thetime cloud SYRSOC top ishas calculated. been applied This to heightfog layers. dependent The cloud factor base in is any then fog multiplied layer is to the CDNCat ground profile level. to produce However, more BASTA realistic has a shapes blind ofzone the in CDNC the lowest profile, 40 andm and subsequently a height range of Re of and LWC profiles.incomplete overlap of outgoing radiation and receiver field of view from 40 m to 240 m [2]. ThisReflectivity is the first below time 240 SYRSOC m will be has reduced been applieddue to th tois fogeffect. layers. However, The cloud LWP of base the in whole any fogcolumn layer is is at measured by HATPRO. In case of fog, much of the liquid water will be located in the lowest 240 m. ground level. However, BASTA has a blind zone in the lowest 40 m and a height range of incomplete overlap of outgoing radiation and receiver field of view from 40 m to 240 m [2]. Reflectivity below Atmosphere 2018, 9, 169 7 of 19
240 m will be reduced due to this effect. However, LWP of the whole column is measured by HATPRO. In case of fog, much of the liquid water will be located in the lowest 240 m. The weak reflectivity would be causing SYRSOC to produce unrealistically high CDNC values and, consequently, very small Re. Therefore, BASTA reflectivity was corrected in the height range of 120–240 m using a near-field correction proposed by [23]. Below 120 m, reflectivity was assumed constant.
3. Liquid WaterAtmosphere Content 2018, 9, x FOR (LWC), PEER REVIEW Effective Radius (Re) and Cloud Droplet Number Concentration7 of 18 (CDNC) RetrievalsThe weak reflectivity would be causing SYRSOC to produce unrealistically high CDNC values and, consequently, very small Re. Therefore, BASTA reflectivity was corrected in the height range of 120– 3.1. Pairing of240 Radar m using and a near-field In Situ Observationscorrection proposed Along by the[23]. Flight Below Path120 m, reflectivity was assumed constant.
In Section3. Liquid3, we Water derive Content the microphysical (LWC), Effective properties Radius (Re) such and Cloud as liquid Droplet water Number content Concentration (LWC), effective radius (Re) and(CDNC) cloud Retrievals droplet number concentration (CDNC) measured by the LOAC granulometer from BASTA cloud radar reflectivity (Z). We compare LWC, Re, CDNC and Z along the tethered 3.1. Pairing of Radar and In Situ Observations Along the Flight Path balloon path. We consider 2 min average data of LOAC along the balloon path and we determine the identical time andIn Section altitude 3, we bins derive for BASTAthe microphysical measurements. properties We such used as liquid the 12.5 water m content vertical (LWC), resolution of effective radius (Re) and cloud droplet number concentration (CDNC) measured by the LOAC BASTA andgranulometer the closest gatefrom BASTA to the averagecloud radar altitude reflectivity of LOAC(Z). We compare during LWC, the averaging Re, CDNC and period. Z along the tethered balloon path. We consider 2 min average data of LOAC along the balloon path and we 3.2. Dopplerdetermine Cloud Radar the identical Reflectivity time and (Z)–LWC altitude Relationshipbins for BASTA measurements. We used the 12.5 m vertical resolution of BASTA and the closest gate to the average altitude of LOAC during the averaging Theoreticalperiod. relationships between cloud radar reflectivity and LWC have been developed and [7–22] proposed a relationship of the form: 3.2. Doppler Cloud Radar Reflectivity (Z)–LWC Relationship − β Theoretical relationships betweenZ mm 6cloudm 3 radar= αre×flectivityLWC and LWC have been developed and (3a) [7–22] proposed a relationship of the form: