remote sensing

Article A Novel Method for Estimating the Vertical Velocity of Air with a Descending Radiosonde System

Jinqiang Zhang 1,2,3, Hongbin Chen 1,2,3, Yanliang Zhu 1, Hongrong Shi 1,*, Youtong Zheng 4, Xiangao Xia 1,2,3, Yupeng Teng 1, Fei Wang 5, Xinlei Han 1, Jun Li 1 and Yuejian Xuan 1

1 Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China 3 University of Chinese Academy of Sciences, Beijing 100049, China 4 Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA 5 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences (CAMS), Beijing 100081, China * Correspondence: [email protected]



Received: 27 May 2019; Accepted: 21 June 2019; Published: 28 June 2019


Abstract: Knowledge of vertical air motion in the atmosphere is important for both meteorological and climate studies due to its impact on clouds, precipitation and the vertical transport of air masses, heat, momentum, and composition. The vertical velocity (VV) of air is among the most difficult and uncertain quantities to measure due to its generally small magnitude and high temporal and spatial variability. In this study, a descending radiosonde system is developed to derive VV at the low and middle troposphere in north China during the summer months. The VV is estimated from the difference between the observed radiosonde descent speed and the calculated radiosonde descent speed in still air based on the fluid dynamic principle. The results showed that the estimated VV generally ranged from 1 m/s to 1 m/s, accounting for 80.2% of data points. In convective − conditions, a wider distribution of the VV was observed, which was skewed to large values relative to those in nonconvective conditions. The average VV throughout the entire profile was close to 0 m/s under nonconvective conditions. In contrast, distinctive vertical air motions below 5 km above the ground were recorded under convective activities. Vigorous air motions with an absolute VV >2 m/s were occasionally observed and were often associated with the occurrence of cloud layers. Moreover, the detailed structure of the instant air motion near the cloud boundaries (i.e., top and base), with an absolute VV >10 m/s in convective weather systems, was clearly revealed by this technique. The uncertainty estimation indicated that this method has the potential to capture and describe events with vertical air motions >0.69 m/s, which is useful for a convective weather study. Further studies are required to carefully assess the accuracy and precision of this novel VV estimation technique.

Keywords: vertical air motion; descending radiosonde; weather conditions; cloud

1. Introduction Vertical air motion reflects dynamic processes in the atmosphere and is crucial for cloud diagnostics, numerical simulation validations [1], and investigations of the indirect effects of aerosols [2,3]. Moreover, vertical air velocity can influence the locations of activated cloud droplets that originate at the cloud top and base; therefore, it can affect cloud formation [4,5]. Previous studies have suggested that vertical air motion associated with anvil clouds played crucial roles in the mass and heat budgets of intense tropical convection systems [6]. The vertical velocity (VV) of air spanned a wide range of

Remote Sens. 2019, 11, 1538; doi:10.3390/rs11131538 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 1538 2 of 17

1 1 scales, from a very small turbulent motion (an order of 0.1 cm s− ) to strong updrafts of ~15 m s− in hurricane rainbands [7]. By conducting an overview of the parameterization of the gravity wave drag in numerical weather predictions and climate simulation models, Kim [8] demonstrated a close relationship between VV and the propagation, evolution and dissipation of gravity waves. Despite its significance, VV is unfortunately among the most difficult and uncertain quantities to measure [9]. Large-scale velocity is typically small in magnitude but uniform and long-lasting. In contrast, a turbulent updraft is sometimes large but is randomly distributed with a short lifetime. Even in the conditions of moderate and strong convective systems, with a VV as large as several or 1 even tens of m s− , this parameter is not easy to measure because of its random occurrence. The VV can be derived from the in situ measurements by towers and aircraft [10–12] and remotely measured using a ground-based Doppler radar/lidar [13–18]. Significant progress has recently been made on the satellite-based estimation of the vertical air motion either by quantifying the turbulent energy [19–21] or by tracking the vertical development of convective cloud tops [22]. Although those detecting approaches can provide valuable information pertaining to air vertical motion, more in situ VV measurements are still required. Considering that the balloon sounding lifting speed, which is calculated from its altitude information, is closely related to the air vertical motion, radiosonde and dropsonde may provide a potential method to estimate the air vertical motion [23–26]. Previous studies investigated atmospheric waves by analyzing the radiosonde ascent speed [27,28]. Radiosonde measurements could be used to determine the height of the boundary layer, since a dramatic decrease in the ascending speed would be expected at the top of this layer [29]; the balloon ascent speed was potentially a good indication of the atmospheric turbulence. Wang [30] indicated 1 that strong vertical motions (larger than 1 m s− ) could be captured via balloon sounding. The dropsonde, which can provide in situ atmospheric profiles of the temperature, pressure, relative humidity (RH), wind speed and direction [31], was initially developed to study the vertical profiles of the kinematic and thermodynamic structure over vast seas and in severe weather conditions, such as hurricanes and typhoons [24,32]. Radiosondes, in contrast, are more available, as they have long been operationally released several times every day at radiosonde stations across the world. It is suggested that a dropsonde’s fall rate is much smoother than a radiosonde’s ascent rate [30] due to the radiosonde’s pendulum effect and the self-induced balloon motion [33], implying that the dropsonde-based VV estimations are more reliable than the radiosonde-based ones. This motivated us to add a dropsonde function based on the operational radiosondes. This is achieved by adding a cutter and a hard ball to the conventional radiosonde package. A field campaign was performed to test the system in north China during the summer months, and an estimation of the VV from this system was investigated in this study. This paper is organized as follows. The main concept underlying the descending radiosonde system is described in Section2. Section3 introduces the site, data, and methodology. The error analysis and credibility assessment are presented in Section4, followed by a discussion and summary in Section5.

2. The Concept of the Descending Radiosonde System The descending radiosonde system is developed based on a conventional radiosonde operated at upper air observation stations. Figure1 shows a schematic diagram of the descending radiosonde system. It is mainly composed of a radiosonde and a 50 cm diameter hard ball that acts as the parachute. A cutter triggered by a timer is placed on a 5 m string, which links the balloon and the hard ball. The radiosonde hangs under the hard ball using a string 40 m in length. The string between the balloon and hard ball is cut when the instrument package is elevated to the upper troposphere by the balloon. Then, the radiosonde and hard ball begin to fall, while the balloon and cutter continue rising until the balloon bursts. Remote Sens. 2019, 11, 1538 3 of 17

Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 17

FigureFigure 1. Schematic 1. Schematic (not (not to scale)to scale) of of the the radiosonde radiosonde payloadpayload for for launch. launch. A Ahard hard plastic plastic foam foam ball ball with with a a diameter of 50 cm acted as the parachute. T and C denote the timer and cutter. diameter of 50 cm acted as the parachute. T and C denote the timer and cutter. The hard ball is made of plastic foam with a weight of ~320 g. The conventional parachute used The hard ball is made of plastic foam with a weight of ~320 g. The conventional parachute used for the radiosonde is generally made of soft materials. The area of the thrust surface will vary during for the radiosonde is generally made of soft materials. The area of the thrust surface will vary during the descent period due to the deformation and swing of this soft parachute. The light hard ball used the descentin our system period never due todeforms the deformation during the entirety and swing of the of ascent this soft and parachute. descent periods. The light The hardsymmetrical ball used in our systemshape of never the ball deforms guarantees during a constant the entirety cross-sectio of then. ascentThis favors and more descent accu periods.rate estimations The symmetrical of the shapevertical of the air ball motion guarantees compared a constantwith that from cross-section. a dropsonde This that favors uses a moresoft parachute. accurate estimations of the vertical airThe motion radiosonde compared deployed with in that the from study a dropsondewas provided that by uses the aChangfeng soft parachute. Company, which Theparticipated radiosonde in the Eighth deployed World Meteorological in the study Organization was provided International by theRadiosonde Changfeng Comparison Company, whichat Yangjiang, participated China in in the 2010 Eighth [34]. The World temperature Meteorological sensor and Organization the RH sensor Internationalof the radiosonde Radiosonde tend Comparisonto display at a more Yangjiang, stable performance China in 2010 at the [34 low]. and The middle temperature troposphere sensor [35]. and The radiosonde the RH sensor sensor of the was oriented and positioned the same during its ascent and descent periods, i.e., the temperature and radiosonde tend to display a more stable performance at the low and middle troposphere [35]. RH sensors reached out from the upper part of one lateral side of the radiosonde box. The hard ball The radiosonde sensor was oriented and positioned the same during its ascent and descent periods, i.e., can decrease the radiosonde descent speed, which allows for more accurate atmospheric the temperaturemeasurements and to RHbe obtained sensors as reached a result out of fromthe reduction the upper in the part time of onelag error. lateral Due side to of the the multiple radiosonde box.data The receiving hard ball channels can decrease designed the in a radiosonde ground-based descent receiver, speed, up to which8 different allows radiosondes for more can accurate be atmosphericreleased measurementssimultaneously, toand be data obtained can be asreceived a result and of processed the reduction by one in ground the time system lag error. at the Duesame to the multiple data receiving channels designed in a ground-based receiver, up to 8 different radiosondes can be released simultaneously, and data can be received and processed by one ground system at the same time. The mean radiosonde ascent velocity is ~5 m/s. The time for the cutter trigger is set to 45 min when the radiosonde generally travels through the troposphere. Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 17 time. The mean radiosonde ascent velocity is ~5 m/s. The time for the cutter trigger is set to 45 min Remote Sens. 2019, 11, 1538 4 of 17 when the radiosonde generally travels through the troposphere.

3. Site, Site, Data Data and and Methodology Methodology

3.1. Site Site

The radiosonde campaign was performedperformed atat TaibusTaibus BannerBanner (TB;(TB; 41.6941.69°N,◦ N, 115.23115.23°E,◦ E, 1380 1380 m m ASL; ASL; as shown in Figure 22),), InnerInner Mongolia,Mongolia, China.China. TBTB isis aa typicaltypical grasslandgrassland areaarea locatedlocated inin thethe southwestsouthwest of the the Xilin Xilin Gol Gol Prairie. Prairie. It has It has a temperate a temperate continental continental arid aridclimate climate with withlow annual low annual precipitation precipitation (<400 mm)(<400 that mm) intensively that intensively occurs occurs in the in summer the summer months. months. The The station station is islocated located approximately approximately 300 300 km northwest of Beijing.

Figure 2. Left Left panel panel:: The The campaign campaign site site at at Taibus Taibus Banner Banner (TB; (TB; yellow yellow triangle) triangle) which which is is located approximately 300 300 km km northwest of of Beijing (BJ; (BJ; red st star),ar), and the deployment of a C-band radar at Zhangjiakou (ZJK;(ZJK; pinkpink dots). dots).Right Right panel panel: drift: drift distance distance (blue (blue dots) dots) of 20 of radiosondes 20 radiosondes from the from launch the launchsite when site the when detecting the detecting signal was signal lost was during lost their duri descentng their periods; descent the periods; red triangle the red denotes triangle the denotes launch thesite launch at TB; radiosondessite at TB; radiosondes released on released the same on day the are same encircled day are by encircled a dashed by line. a dashed line.

3.2. Data Data The descending radiosonderadiosonde datadata used used by by this this study study were were collected collected at TBat fromTB from 3 to 3 8 Augustto 8 August 2012. 2012.The radiosondes The radiosondes were launchedwere launched two to two five to times five pertimes day, per resulting day, resulting in a total in of a 20total profiles. of 20 profiles. Figure2 Figureshows 2 the shows drift the distances drift distances from the from launching the launching spot to spot when to when the radiosonde the radiosonde lost lost a signal a signal during during its itsdescent descent period. period. They They were were mostly mostly less less than than 50 50 km km and and were were very very close close to theto the radiosondes radiosondes released released on onthe the same same day. day. The maximum maximum heights heights of of the the radiosonde radiosonde exceed exceed 10 10km, km, after after which which it begins it begins to descend. to descend. The minimumThe minimum descent descent altitude altitude with with a radiosonde a radiosonde signal signal is isgenerally generally ~0.5 ~0.5 km km above above the the ground ground level (AGL). The radiosonde descentdescent speedspeed rangesranges fromfrom ~25~25 mm/s/s atat 1010 km to ~15 m/s m/s at 0.5 km AGL. AGL. It takes takes some extraextra timetime toto reach reach the the quasi-equilibrium quasi-equilibrium state state when when the the radiosonde radiosonde begins begins to descend. to descend. The highThe highdescent descent speed speed at the at high the altitude high altitude of the troposphereof the troposphere may induce may ainduce large uncertainty a large uncertainty in the VV in retrievals the VV retrievalssince their since magnitudes their magnitudes are usually are dozens usually of dozens centimeters of centimeters per second per or second less. The or less. radiosonde The radiosonde we used wetends used to providetends to aprovide more stable a more performance stable performance at the low at and the middlelow and troposphere middle troposphere [35]. Hence, [35]. only Hence, VVs onlyat low VVs and at middle low and tropospheres middle tropospheres (i.e., from (i.e., 0.5 to from 7 km 0.5 AGL) to 7 arekm usedAGL) in are this used study. in this study. The radiosonderadiosonde measures measures the the atmospheric atmospheric temperature, temperature, RH, horizontalRH, horizontal wind speed wind and speed direction and directionat a temporal at a temporal resolution resolution of 1 s during of 1 s during its ascent its ascent and descent and descent periods. periods. The responseThe response time time of the of thetemperature temperature and and humidity humidity sensors sensors is 0.8 is and0.8 and 1.5 s1. respectively.5 s respectively. As anAs example,an example, Figure Figure3 shows 3 shows the themeasurement measurement profiles profiles from from our lastourradiosonde last radiosonde launch launch at 12:57 at local12:57 standard local standard time (LST), time 8(LST), August 8 August2012 when 2012 largest when drift largest occurred. drift occurred. The temperature The temperature profiles during profiles the ascentduring and the descent ascent periodsand descent were periodsin a reasonable were in agreement.a reasonable The agreement. RH profile The observed RH profile during observed the descent during wasthe descent wetter thanwas wetter that during than the ascent, which is likely due to a larger spatial variation in the water vapor during the drift of the Remote Sens. 2019, 11, x 1538 FOR PEER REVIEW 5 of 17 that during the ascent, which is likely due to a larger spatial variation in the water vapor during the driftradiosonde. of the radiosonde. Compared toCompared the ascent, to thethe horizontalascent, the wind horizontal speed wind and direction speed and tended direction to be tended smoother to beduring smoother the descent during period, the descent which period, was also which likely was associated also likely with associated the spatial with variation the spatial and due variation in part andto the due coarse in part vertical to the resolution coarse vertical (a larger resoluti descenton (a speed larger of descent the radiosonde). speed of the radiosonde).

Figure 3. ProfilesProfiles of of (a) (a) temperature, temperature, (b) (b) RH, RH, (c) (c) horizontal horizontal wind wind speed speed and and (d) (d) horizontal wind direction provided by the radiosonde launched at 12:5712:57 LST August 8, 2012, over the TB site. The blue and redred lines lines represent represent the measurementsthe measurements collected collected during during the ascent the and ascent descent and periods, descent respectively. periods, respectively. To aid in the investigation of the VV retrieval, the ERA5 data were used to provide information pertainingTo aid toin the the large-scale investigation synoptic of the circulation,VV retrieval, surface the ERA5 atmospheric data were instability, used to provide and vertical information velocity. pertainingERA5 is the to latest the large-scale global atmospheric synoptic reanalysiscirculation, dataset surface available atmospheric from theinstability, European and Centre vertical for velocity.Medium-Range ERA5 is Weather the latest Forecasts. global atmospheric The gridded rean dataalysis provides dataset hourly available estimates from the of aEuropean large number Centre of foratmospheric, Medium-Range land and Weather oceanic Forecasts. climate variables.The gridded The da datata provides cover the hourly Earth estimates on a 30 km of grid a large and number resolve ofthe atmospheric, atmosphere usingland and 137 levelsoceanic from climate the surface variables. up toThe a height data cover of 80 km.the Earth ERA5 on includes a 30 km information grid and resolveabout uncertainties the atmosphere for using all variables 137 levels at reducedfrom the spatialsurface and up temporalto a height resolutions. of 80 km. ERA5 The pressure,includes informationtemperature, about zonal uncertainties and meridional for components all variables of at the reduced wind, the spatial convective and temporal available resolutions. potential energy The pressure,(CAPE), andtemperature, the vertical zonal velocity and datameridional over Inner components Mongolia, of werethe wind, used. the Additionally, convective aavailable Weather potentialSurveillance energy Radar-1998 (CAPE), Doppler and the (WSR-98D)vertical velocity radar da deployedta over Inner at Zhangjiakou Mongolia, were (ZJK; used. 41.16 ◦Additionally,N, 114.69◦ E, a1425 Weather m ASL; Surveillance as shown inRadar-1998 Figure2). TheDoppler radar (WSR-98D) base reflectively radar product deployed was at used Zhangjiakou to provide (ZJK; the 41.16°N,temporal 114.69°E, evolution 1425 of am convective ASL; as shown cloud in process Figure over2). The the radar TB site. base The reflectively WSR-98D product radar at was ZJK used is a toC-band provide radar the withtemporal a maximum evolution range of a convective of 400 km cloud [36]. Theprocess distance over fromthe TB this site. radar The WSR-98D to the TB siteradar is atapproximately ZJK is a C-band 96 km. radar with a maximum range of 400 km [36]. The distance from this radar to the TB site is approximately 96 km. 3.3. Methodology 3.3. Methodology 3.3.1. VV Estimation Algorithm 3.3.1.Following VV Estimation [30], weAlgorithm obtained the VV from the difference between the calculated radiosonde descent speed in still air (W in m/s) and the observed radiosonde descent speed (W in m/s): VV = W W . Following [30],C we obtained the VV from the difference between theO calculated radiosondeC − O A positive/negative value of the VV implies upward/downward air motion, respectively. descent speed in still air (WC in m/s) and the observed radiosonde descent speed (WO in m/s): VV = W can be calculated by using the following formula, based on the balance between gravity and WC－WCO. A positive/negative value of the VV implies upward/downward air motion, respectively. the drag forces [29]: WC can be calculated by using the following formula, based on the balance between gravity and M g = A ρ C W 2/2 (1) the drag forces [29]: × × × × C 𝑀×𝑔=𝐴 ×𝜌×𝐶×𝑊 /2 (1)

Remote Sens. 2019, 11, 1538 6 of 17 where M is the total mass of the radiosonde (~0.24 kg), hard ball (~0.32 kg) and string (~0.13 kg), which is weighed and recorded for each package set. g is the acceleration due to gravity at the surface 2 3 of Earth (in m s− ). ρ is the vertical dry air density profile (in kg/m ), which can be calculated from the individual vertical profiles of the temperature and pressure. A is the cross-sectional area of the hard ball (in m2), which is the same for every hard ball due to its constant diameter (i.e., 50 cm). This favors more accurate estimations of the vertical air motion compared to the soft parachute used by [30] because of the deformation and swing of the soft parachute. C is the drag coefficient which varies with the altitude in this study; it was set as a constant parameter for the dropsonde in [30].

3.3.2. Radiosonde-Based Cloud Layer Identification To aid the comprehension of the VV characteristics, a radiosonde-based cloud layer identification method developed by [37] was used. A brief description of the algorithm is presented here, while detailed procedures can be found in the above literature. The algorithm employs three height-resolving RH thresholds to determine the cloud layers, i.e., the minimum and maximum RH thresholds in cloud layers, and the minimum RH threshold within the distance of the two neighboring cloud layers. The RH is transformed with respect to ice for levels with a temperature below 0 °C. This method was developed based on the Vaisala RS92 radiosonde, which had a higher RH detecting accuracy compared to other types of radiosondes [38]. The RH thresholds used to determine the cloud location by various radiosondes should be modified according to their different RH detecting accuracies [39]. The average dry bias of our radiosonde was about on the order of 10% from the surface to the middle troposphere [40]. Therefore, the above three RH thresholds for our radiosonde are 10% RH less than the values used for the Vaisala RS92 in [37].

4. Uncertainty Analysis and Credibility Assessment of VV Retrievals Validating the VV has long been a daunting task, not only because of the lack of independent “truth” in VV measurements but also because of the inherently large variability of VV [26,30]. Both apply to this study. Here, we assess the credibility of the estimated VV by (1) an uncertainty analysis, and (2) examining the consistency of the retrieved VV with the radar- and radiosonde-observed cloud field and reanalysis data. The latter is achieved by conducting a composite analysis and a set of case studies. Before proceeding, we first revisit some background knowledge of VV in order to aid in the interpretation of the credibility assessment. The VV of any air parcel has two components: VVmean and VV’, in which VVmean is the vertical velocity of the large-scale mean flow that is typically small in 1 magnitude (several tens of cm s− in fair weather conditions), and VV’ is the turbulent component that is highly variable and can have large values in highly convective weather systems. VVmean, as a background VV, is much less variable than the small-scale VV’.

4.1. Uncertainty Estimation According to the aforementioned VV estimation formula, its accuracy is determined by the uncertainties of WO and WC. The WO is inferred from the Global Position System (GPS) data that is provided by the descending radiosonde. Although the absolute uncertainty of the GPS data can reach up to several or tens of meters, WO is estimated from the relative position of the radiosonde. The uncertainty of WO is less than 0.3 m/s [41]. To reduce the occasional noise and uncertainty, the vertical resolution of WO was projected at a vertical resolution of 100 m for the radiosonde profile. Hence, the total uncertainty of WO may be expected to be 0.3 m/s at most. The calculation of WC is a function of M, A, ρ, and C:

WC = WC(M, A, ρ, C ) (2)

The uncertainty in WC is a composite contribution from the individual uncertainties of these four different parameters. Their uncertainties are assumed to be random and follow Gaussian distributions; Remote Sens. 2019, 11, x FOR PEER REVIEW 7 of 18

RemoteThe Sens. uncertainty2019, 11, 1538 in WC is a composite contribution from the individual uncertainties of these four7 of 17 different parameters. Their uncertainties are assumed to be random and follow Gaussian distributions; the Gaussian law of uncertainty propagation is thus applied to Equation (2). The ∆ theoverall Gaussian relative law uncertainty of uncertainty is defined propagation as and is thus is expressed applied as to follows, Equation with (2). the The results overall provided relative uncertainty is defined as ∆WC and is expressed as follows, with the results provided in Table1: in Table 1: WC s 2 2 2 2 ∆WC 1 ∆M ∆A ∆ρ ∆C ∆=푊 1( ∆M) + ( ∆A) +∆ (ρ ) +∆C ( ) (3) WC 2= M( ) + (A )+( )ρ + ( )C (3) W 2 M A ρ C ∆M ∆M is assumed to be a constant value of 1 g, leading to a 0.14% relative uncertainty (∆ ) after being ∆M is assumed to be a constant value of 1 g, leading to a 0.14% relative uncertainty ( M) after being divided by the typical total mass of the descending system at 690 g. The surface of the plastic foam divided by the typical total mass of the descending system at 690 g. The surface of the plastic foam ball is designed smoothly, expecting to remove or reduce the water accumulation on the ball during ball is designed smoothly, expecting to remove or reduce the water accumulation on the ball during the flight. However, potential total mass increase might occur due to the effect of the rain or/and the flight. However, potential total mass increase might occur due to the effect of the rain or/and ice. This effect is ignored in the current study because its quantity is still unclear at present given the ice. This effect is ignored in the current study because its quantity is still unclear at present given the complexitycomplexity of theof the moisture moisture accumulation accumulation under under diff differenterent weather weather conditions. conditions. As theAs the next next step, step, it might it bemight expected be to expected address this to address issue through this issue the combination through the of in-situcombination experimental of in-situ measurements experimental and modelmeasurements simulations. and The model diameter simulations. of the hardThe diameter plastic foam of the ball hard is 50plastic cm, withfoam anball absolute is 50 cm, uncertainty with an ∆A ∆ setabsolute at a constant uncertainty value ofset 0.1 at cm.a constant This results value inof the0.1 relativecm. This uncertainty results in the of relativeA being uncertainty 0.40%. of being 0.40%. Table 1. Summary of the uncertainty existing in the descending radiosonde system.

Table 1. SummaryMagnitude of the uncertainty Absolute existing Uncertaintyin the descending radiosonde Relative Uncertainty system M Magnitude690 g Absolute uncertainty 1 g Relative 0.14% uncertainty 푀 A 690 1964g cm2 7.8618 1 g cm2 0.40% 0.14% 3 3 퐴 ρ 19640.4684–0.9665 cm2 kg/m 7.86180.0016 kgcm2/m 0.17–0.34% 0.40% C 0.3261–0.3319 0.0037–0.0078 2.29–4.74% 휌 0.4684- 0.9665 kg/m3 0.0016 kg/m3 0.17-0.34% W 14.68–21.29 m/s 0.2393–0.3877 m/s 1.18–2.38% 퐶 C 0.3261- 0.3319 0.0037-0.0078 2.29-4.74%

푊 14.68-21.29 m/s 0.2393-0.3877 m/s 1.18-2.38% InIn contrast contrast with withM and푀 andA, the퐴, uncertainty the uncertainty contributions contributions from fromρ and 휌 C anddepend 퐶 depend on the on air the pressure air (orpressure altitude) (or and altitude) temperature. and temperature. Figure4a Figure presents 4a presents the vertical the vertical distributions distributions of the of calculated the calculatedρ for 20 radiosondes휌 for 20 radiosondes released over released the TB over site. the Overall, TB site. small Overall, variabilities smallwere variabilities found among were di foundfferent among profiles; theirdifferent mean valuesprofiles; increased their mean from values ~0.47 increased kg/m3 at 7from km to~0.47 0.97 kg/m kg/m3 3atat 7 0.5 km km. to Vertical0.97 kg/m uncertainties3 at 0.5 km. of ρ isVertical mathematically uncertainties derived of 휌 from is mathematically uncertainties derived of temperature from uncertainties (0.3 °C) and of pressuretemperature (1 hPa) (0.3 ℃ data) and with thepressure law of uncertainty (1 hPa) data propagation. with the law As of shownuncertainty in Figure propagation.5, the relative As shown uncertainty in Figure is less 5, thanthe relative 1% for ρ at alluncertainty height levels. is less than 1% for 휌 at all height levels. Height AGL (km) Height AGL (km)

Figure 4. The vertical profiles of (a) the air density and (b) the average drag coefficient (red line) over the TB site. The shaded green areas in panel (b) represent the standard deviation of the drag coefficient.

C depends on the Reynolds number (Re, being generally in the range of 3 105). A transition often × exists in Re from the boundary layer or turbulent layer to the free troposphere [33,42]. Additionally, other factors, such as free stream turbulence and unsteadiness of the ball during flight can also influence Remote Sens. 2019, 11, x FOR PEER REVIEW 8 of 18

Figure 4. The vertical profiles of (a) the air density and (b) the average drag coefficient (red line) over the TB site. The shaded green areas in panel (b) represent the standard deviation of the drag coefficient. Remote Sens. 2019, 11, 1538 8 of 17

퐶 depends on the Reynolds number (Re, being generally in the range of 3×105). A transition the valueoften of existsC [27 in]. Re Therefore, from the it boundary is very di layerfficult or to turbulent estimate layerC actually to the andfree troposphereit is challenging [33,42]. to use Additionally, other factors, such as free stream turbulence and unsteadiness of the ball during flight a uniform C below a fixed altitude for the soundings. C is set varied to find optimal values for the can also influence the value of 퐶 [27]. Therefore, it is very difficult to estimate 퐶 actually and it is radiosondechallenging whereas to use a constant a uniformC is퐶 used below for a fixed the dropsonde altitude for in the [30 soundings.]. Given the 퐶 di isffi setculties varied in to estimating find C basedoptimal on Re values and thefor largethe radiosonde uncertainty whereas associated a constant with 퐶 other is used complex for the influencedropsonde factors in [30].mentioned Given above,theC difficultiesused in this in study estimating is derived 퐶 based as follows. on Re and Vertical the large descent uncertainty speed profiles, associated which with are other almost smoothcomplex associated influence with factors the stable mentioned weather above, conditions 퐶 used according in this study to the is observation derived as records,follows. Vertical are selected. This leadsdescent to speed 6 almost profiles, smooth which profiles are almost selected smooth from associated the 14 profiles with the collected stable weather under nonconvective conditions conditions.according A low-pass to the observation filter was thenrecords, applied are selected. to these This 6 profiles leads toto remove6 almost the smooth noise profiles from the selected pendulum effect,from self-induced the 14 profiles ball motions,collected under and other nonconvective potential conditions. causes (such A low-pass as turbulence). filter was The then filtered applied profiles to these 6 profiles to remove the noise from the pendulum effect, self-induced ball motions, and other data are deemed the radiosonde descent rate in still air, which are used to calculate vertical average potential causes (such as turbulence). The filtered profiles data are deemed the radiosonde descent C and its standard deviation using the formula (1). The vertical C varied little (between 0.32 and rate in still air, which are used to calculate vertical average 퐶 and its standard deviation using the 0.33) atformula different (1). height The vertical levels; 퐶 its varied standard little deviation (between tended 0.32 and to 0.33) be larger at different below 4 height km than levels; that its in the middlestandard troposphere deviation (Figure tended4b). to The be larger mathematical below 4 km estimation than that ofin uncertaintythe middle troposphere sources pertaining (Figure 4b). to C is also veryThe mathematical difficult due estimation to the complex of uncertainty influence sources factors pertaining aforementioned. to 퐶 is also Therefore, very difficult the dueC uncertainty to the fromcomplex its vertical influence variability factors (i.e., aforementioned. standard deviation Therefore, in Figurethe 퐶 uncertainty4b) is analyzed from inits thisvertical study. variability As shown in Figure(i.e., 5standard, a greater deviation relative in uncertainty, Figure 4b) is at analyzed most up in to this 4.74% study. at As ~2.5 shown km AGL, in Figure is revealed 5, a greater for ∆C . ∆ C relative uncertainty, at∆C most up to 4.74% at ~2.5 km AGL, is revealed for . The large magnitude of The large magnitude of C below 4 km is most likely a result of the turbulence that occurs at the low ∆ troposphere below [29 4 km,30]. is most likely a result of the turbulence that occurs at the low troposphere [29,30]. The resultantThe resultant relative relative and and absolute absolute uncertainties uncertainties ofof WC causedcaused by by the the above above four four parameters parameters are are ∆W also shownalso shown in Figure in Figure5. The 5. The maximum maximum relative relative uncertainty uncertainty (( C) is) is2.38%, 2.38%, which which is mainly is mainly attributed attributed WC to theto large the large uncertainty uncertainty of C of. The퐶. The absolute absolute uncertainty uncertainty ofof W CC isis calculated calculated by bythe the relative relative uncertainty uncertainty multipliedmultiplied by the by mean the mean WC (as WC presented (as presented in the in following the following section), section), leading leading to the to uncertainties the uncertainties varying betweenvarying 0.24 between and 0.39 0.24 m/ s.and 0.39 m/s.

Relative uncertainty (%) 0 1 2 3 4 5 7 7

6 C 6 W (M, A, , C) C 5 VV 5

4 4

3 3

2 2

1 1

0 0 0 0.1 0.2 0.3 0.4 0.5 Uncertainty of VV (m/s)

FigureFigure 5. Relative 5. Relative uncertainty uncertainty of ofρ 휌(short (short dashed dashed redred line), line), 퐶C (long(long dashed dashed red red line), line), the theresultant resultant relativerelative uncertainties uncertainties of Wof CW(solidC (solid red red line) line) caused by by the the four four parameters parameters (i.e., (i.e.,M,A,ρ,M, andA, ρC), and theC) and the inducedinduced resultant resultant uncertainty of of the the VV VV (solid (solid blue blue line). line).

The resultant absolute uncertainty of the VV retrievals, obtained by the sum of both WO and WC, is estimated on the order of 0.54–0.69 m/s. Based on this, the descending radiosonde estimated VV may have the potential to capture and describe events with vertical air motions >0.69 m/s.

4.2. Composite Analysis The mass and vertical air density derived from the individual radiosonde and the vertical mean drag coefficients are input into Equation (1) to calculate WC in still air for each sounding. Figure6 shows the vertical distributions of the observed and calculated radiosonde descent speed and the VV Remote Sens. 2019, 11, x FOR PEER REVIEW 9 of 17

The mass and vertical air density derived from the individual radiosonde and the vertical mean

Remotedrag coefficients Sens. 2019, 11, 1538are input into Equation (1) to calculate WC in still air for each sounding. Figure9 of 176 shows the vertical distributions of the observed and calculated radiosonde descent speed and the VV retrievals at a vertical resolution of 100 m from 20 radiosondes. In general, the observed radiosonde retrievalsdescent speed at a verticalvaried from resolution ~20 m/s of at 100 7 mkm from to ~15 20 m/s radiosondes. in the boundary In general, layer the (Figure observed 6a). However, radiosonde a descentcloser view speed of the varied individual from ~20 desc ment/s at profiles 7 km to revealed ~15 m/s discrepancies in the boundary in the layer magnitude (Figure6 ofa). the However, descent aspeed closer among view of different the individual profiles. descent Compared profiles to revealed the measurements, discrepancies inthe the calculated magnitude descents of the descent were speedsmoother among and di presentedfferent profiles. better consistency Compared to among the measurements, the different profiles the calculated (Figure descents 6b). The were vast smoother majority andof VVs presented ranged from better -1 consistency m/s to 1 m/s, among accounting the di ffforerent 80.2% profiles of the (Figure data points6b). The(Figure vast 6c). majority It should of VVs also rangedbe noted from that strong1 m/s to vertical 1 m/s, accountingair motions forwere 80.2% occasionally of the data detected points (Figureby a few6c). of It shouldthe radiosonde also be − notedprofiles. that For strong example, vertical updrafts air motions >2 m/s were were occasionally detected by detected two radiosondes by a few of theat the radiosonde low and profiles.middle Fortroposphere. example, In updrafts addition,>2 violent m/s were updrafts detected with by a two VV radiosondes>6 m/s and downdrafts at the low andwith middle a VV <-10 troposphere. m/s were Inobserved addition, by violentone radiosonde updrafts withreleased a VV at> 15:586 m/s LST and August downdrafts 7, 2012 with (h aereafter VV < 10this m extreme/s were observedradiosonde by − onecase radiosondeis referred releasedto as RC at7th 15:58). Further LST 7 investigations August 2012 (hereafter on these this particular extreme cases radiosonde are presented case is referred in the tofollowing as RC7th section.). Further investigations on these particular cases are presented in the following section.

Figure 6. Vertical distributions of (a) (a) the observed descent speed, (b) (b) the calculated descent speed, speed, and ((c)c) the vertical velocity of air from 2020 radiosondesradiosondes (represented(represented byby didifferentfferent colors).colors).

The average vertical profiles profiles of the radiosonderadiosonde descent speed and VV from 20 radiosondes are displayed in Figure 77.. TheThe descentdescent speedsspeeds fromfrom thethe measurementsmeasurements andand calculationscalculations werewere generallygenerally close at most most altitudes altitudes (Figure (Figure 7a).7a). A A relatively relatively larg largerer difference di fference could could be befound found at ~3 at km ~3 kmbetween between the thetwo twodescent descent datasets datasets at a more at a more detailed detailed level, level,which which was manifested was manifested in the extreme in the extreme outliers outliers in Figure in Figure7b. The7 b.medians The medians and means and meansof the VV of the were VV generally were generally distributed distributed at approximately at approximately 0 m/s at 0 mall/ heights at all heightlevelsRemote (Figure levels Sens. 2019 (Figure 7b)., 11 , x 7FORb). PEER REVIEW 10 of 18

FigureFigure 7. 7.(a )(a) Vertical Vertical average average of of 20 20 radiosonde radiosonde profilesprofiles of the observed observed descent descent speed speed (red (red line) line) and and Figurethethe calculated calculated 7. (a) Vertical descent descent average speed speed (blue of(blue 20 lines); radiosondelines); ((b)b) Box-and-whiskerBox-and-whisker profiles of the observedplots plots of of the the descent vertical vertical speed velocity velocity (red spaced spacedline) atand at 1the km1 calculated km intervals intervals descen from from 20t 20 speed radiosonde radiosonde (blue lines); profiles, profiles, (b) including includingBox-and-whisker the median plots (red (red of line linethe invertical in the the box), box), velocity 25th 25th and spaced and 75th 75th at percentilespercentiles (ends (ends of of the the box), box), 5th 5th and and 95th 95th percentiles percentiles (ends(ends of the whiskers), whiskers), and and means means (black (black dots). dots).

The VV retrievals from the 20 radiosonde profiles were further divided into two categories: nonconvective (14 profiles) and convective (6 profiles) conditions, which are classified mainly according to the observation records, CAPE, and radar reflectivity. The nonconvective conditions are related to a clear sky or cloudy sky without precipitation and with a small CAPE (<150 J/kg) and reflectivity (<20 dBZ). The convective conditions are associated with a cloudy sky and precipitation (or thunderbolt) occurring [43,44]. Figure 8a shows the histogram distribution of the VV retrievals. A close occurrence frequency was exhibited by the two weather conditions for a VV between 0 and 0.5 m/s. However, compared to the nonconvective condition, the VV distribution was wider and skewed to the large values under the convective condition. In view of a certain bin for a VV >1 m/s, the occurrence frequency was always larger under convective conditions rather than that under nonconvective conditions, implying that there was a stronger updraft in the former weather condition. The occurrence frequency from the convective condition with the exception of the particular radiosonde case (RC7th) is also shown in Figure 8a. A similar variation pattern was exhibited between all convective conditions and those with RC7th discarded, indicating that the extreme RC7th had little impact on the statistics of the VV distribution under convective conditions. The vertical averages of the VV and their standard deviations are presented in Figure 8b. The vertical VV was almost close to 0 m/s at all altitude levels under nonconvective conditions. In contrast, greater VV variations were exhibited under convective conditions. The incompleteness of the data below 1 km hindered us from analyzing the VV characters near the surface. An apparent difference was found by the two weather categories for a VV from 1 to 5 km, with stronger vertical air motion exhibited under convective activities. Dramatic deep convective systems, which could extend to the upper troposphere, did not occur during our experimental period, resulting in a small VV magnitude above 5 km under both convective and nonconvective conditions. The VV under all convective conditions and those with RC7th discarded mostly agree well, except for at approximately 3 km.

Remote Sens. 2019, 11, 1538 10 of 17

The VV retrievals from the 20 radiosonde profiles were further divided into two categories: nonconvective (14 profiles) and convective (6 profiles) conditions, which are classified mainly according to the observation records, CAPE, and radar reflectivity. The nonconvective conditions are related to a clear sky or cloudy sky without precipitation and with a small CAPE (<150 J/kg) and reflectivity (<20 dBZ). The convective conditions are associated with a cloudy sky and precipitation (or thunderbolt) occurring [43,44]. Figure8a shows the histogram distribution of the VV retrievals. A close occurrence frequency was exhibited by the two weather conditions for a VV between 0 and 0.5 m/s. However, compared to the nonconvective condition, the VV distribution was wider and skewed to the large values under the convective condition. In view of a certain bin for a VV >1 m/s, the occurrence frequency was always larger under convective conditions rather than that under nonconvective conditions, implying that there was a stronger updraft in the former weather condition. The occurrence frequency from the convective condition with the exception of the particular radiosonde case (RC7th) is also shown in Figure8a. A similar variation pattern was exhibited between all convective conditions and those with RC7th discarded, indicating that the extreme RC7th had little impact on the statistics of the VV distribution under convective conditions. The vertical averages of the VV and their standard deviations are presented in Figure8b. The vertical VV was almost close to 0 m /s at all altitude levels under nonconvective conditions. In contrast, greater VV variations were exhibited under convective conditions. The incompleteness of the data below 1 km hindered us from analyzing the VV characters near the surface. An apparent difference was found by the two weather categories for a VV from 1 to 5 km, with stronger vertical air motion exhibited under convective activities. Dramatic deep convective systems, which could extend to the upper troposphere, did not occur during our experimental period, resulting in a small VV magnitude above 5 km under both convective and nonconvective conditions. The VV under all convective conditions and those with RC7th discarded mostly agree well, except for atRemote approximately Sens. 2019, 11, 3x FOR km. PEER REVIEW 11 of 18

Figure 8. (a) Histogram distribution of the radiosonde-based vertical velocity under nonconvective Figure 8. (a) Histogram distribution of the radiosonde-based vertical velocity under nonconvective conditions (NC; blue bar), all convective conditions (C; red bar), and convective conditions with conditions (NC; blue bar), all convective conditions (C; red bar), and convective conditions with the the exception of the extreme radiosonde case –RC7th (C1; green bar). (b) Vertical averages of the exception of the extreme radiosonde case –RC7th (C1; green bar). (b) Vertical averages of the vertical vertical velocity under nonconvective conditions (Avg_NC; blue line), all convective conditions (Avg_C; velocity under nonconvective conditions (Avg_NC; blue line), all convective conditions (Avg_C; red red line), and convective conditions with the exception of RC7th (Avg_C1; green line). Shaded regions line), and convective conditions with the exception of RC7th (Avg_C1; green line). Shaded regions represent the standard deviation under nonconvective (Std_NC) and all convective conditions (Std_C). represent the standard deviation under nonconvective (Std_NC) and all convective conditions 4.3. Case(Std_C). Study

4.3. CaseThe Study VV measurements with absolute values of >10 m/s were detected by the RC7th release. This section focuses on the details of this case and briefly introduces the other two cases with relatively largeThe VV values.VV measurements with absolute values of >10 m/s were detected by the RC7th release. This section focuses on the details of this case and briefly introduces the other two cases with relatively large VV values. According to the observation records, the cloud fraction increased from 90% at 09:00 LST to 100% at 15:00 LST August 7 over the TB site. The cumulus congestus transformed into cumulonimbus, which was accompanied by rain and lightning at 16:05 LST August 7. As shown in Figure 9a (the synoptic system from ERA5 at 14:00 LST August 7), the TB site was located at the trough of low pressure at 500 hPa, indicating upward motions of air according to omega equations [9]. In addition, the ERA5-based CAPE was 45.7 J kg−1 at 14:00 LST, which jumped to 609.1 J kg−1 when the rain started. Figure 9b shows the vertical air motions from the ERA5 data at 14:00 LST August 7. A radiosonde was launched at 14:14 LST, which temporally matched the ERA5 data. The VV obtained by this radiosonde profile is also presented in Figure 9b for comparison. The ERA5 data indicated upward air motion during all altitude levels. The radiosonde showed downward motion near 3 km, straddling between two upward-motion layers with one in the lower atmosphere and one in the middle atmosphere. Compared to the radiosonde derived column-integrated VV (0.44 m/s), a smaller magnitude (0.07 m/s) and a less-structured VV were revealed by the ERA5. However, this difference is understandable by taking into account the followed factors. The ERA5 is a gridded data product which represents the average state of the vertical air motion within a certain spatial region. Several cm s−1 is a typical value for large-scale vertical air motion. However, local deep convection may cause much larger VV values, which can be well expressed by the real-time and in situ measurement from the radiosonde along its descending path. Note that ERA5 dataset is only used to provide a qualitative comparison with the radiosonde measurements in this study; caution should be taken to the ERA5 VV value presented here since ERA5 do not explicitly resolve convection.

Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 17

The VV measurements with absolute values of >10 m/s were detected by the RC7th release. This Remotesection Sens. focuses2019, 11 on, 1538 the details of this case and briefly introduces the other two cases with relatively11 of 17 large VV values. AccordingAccording toto thethe observationobservation records,records, thethe cloudcloud fractionfraction increasedincreased fromfrom 90%90% atat 09:0009:00 LSTLST toto 100% atat 15:0015:00 LSTLST 7August August 7 overover thethe TBTB site.site. TheThe cumuluscumulus congestuscongestus transformedtransformed intointo cumulonimbus,cumulonimbus, whichwhich waswas accompanied by by rain rain and and lightning lightning at at 16:05 16:05 LST LST August 7 August. 7. As Asshown shown in Figure in Figure 9a (the9a (thesynoptic synoptic system system from from ERA5 ERA5 at 14 at:00 14:00 LST LST August 7 August), 7), the the TB TB site site was was located located at at the the trough of lowlow pressurepressure atat 500500 hPa,hPa, indicatingindicating upwardupward motionsmotions ofof airair accordingaccording toto omegaomega equationsequations [[9].9]. InIn addition,addition, the ERA5-based CAPE was 45.7 J kg−11 at 14:00 LST, which jumped to 609.1 J kg−1 when the rain started. the ERA5-based CAPE was 45.7 J kg− at 14:00 LST, which jumped to 609.1 J kg− when the rain started. FigureFigure9 b9b shows shows the the vertical vertical air air motions motions from from the the ERA5 ERA5 data data at 14:00 at 14:00 LST 7LST August. August A radiosonde7. A radiosonde was launchedwas launched at 14:14 at LST,14:14 which LST, temporallywhich temporally matched matched the ERA5 the data. ERA5 The VVdata. obtained The VV by obtained this radiosonde by this profileradiosonde is also profile presented is also in presented Figure9b forin Figure comparison. 9b for comparison. The ERA5 data The indicated ERA5 data upward indicated air motionupward duringair motion all altitude during levels. all altitude The radiosonde levels. The showed radiosonde downward showed motion downward near 3 km, motion straddling near between 3 km, twostraddling upward-motion between two layers upward-motion with one in the layers lower with atmosphere one in the and lower one atmosphere in the middle and atmosphere. one in the Comparedmiddle atmosphere. to the radiosonde Compared derived to the column-integrated radiosonde derived VV column-integrated (0.44 m/s), a smaller VV magnitude (0.44 m/s), (0.07 a smaller m/s) andmagnitude a less-structured (0.07 m/s) VVand were a less-structured revealed by theVV ERA5.were revealed However, by this the diERA5.fference However, is understandable this difference by takingis understandable into account by the taking followed into factors.account Thethe ERA5follow ised a factors. gridded The data ERA5 product is a gridded which represents data product the which represents the average state of the vertical air motion within a certain spatial1 region. Several average state of the vertical air motion within a certain spatial region. Several cm s− is a typical value −1 forcm large-scales is a typical vertical value air for motion. large-scale However, vertical local air deepmotion. convection However, may local cause deep much convection larger VVmay values, cause whichmuch canlarger be VV well values, expressed which by can the be real-time well expresse and ind situ by measurementthe real-time and from in the situ radiosonde measurement along from its descendingthe radiosonde path. along Note thatits descending ERA5 dataset path. is only Note used that to ERA5 provide dataset a qualitative is only comparison used to provide with the a radiosondequalitative measurementscomparison with in thisthe study;radiosonde caution measurements should be taken in this to the study; ERA5 caution VV value should presented be taken here to sincethe ERA5 ERA5 VV do value not explicitly presented resolve here since convection. ERA5 do not explicitly resolve convection.

FigureFigure 9.9. ((a)a) SynopticSynoptic systemsystem analysisanalysis overover thethe TBTB sitesite fromfrom thethe ERA5ERA5 datadata atat 500500 hPahPa atat 14:0014:00 LSTLST onon 7August August 7, 2012. 2012. TheThe whitewhite arrow is the wind speed speed and and direction; direction; the the maximu maximumm value value of of the the winds winds is 1 is26 26 m m s− s1−; the; the black black lines lines are are the the geopot geopotentialential height height (units: (units: gpm); gpm); the the color shading representsrepresents thethe temperaturetemperature (units: (units:◦ °C);C);and and the the red red diamond diamond is is the the location location of of TB TB site. site. (b )(b) Vertical Vertical velocity velocity (VV) (VV) of air of from radiosonde released at 14:14 LST (dashed blue lines) and the ERA5 analysis data at 14:00 LST air from radiosonde released at 14:14 LST (dashed blue lines) and the ERA5 analysis data at 14:00 LST (dashed red lines) on 7 August 2012. The VV values of 0 m/s are denoted by the dashed black line. (dashed red lines) on August 7, 2012. The VV values of 0 m/s are denoted by the dashed black line. The solid blue line is a boundary layer height (BLH) retrieved from the radiosonde data (1210 m) and The solid blue line is a boundary layer height (BLH) retrieved from the radiosonde data (1210 m) and the solid red line is the BLH from the ERA5 data (1010 m). the solid red line is the BLH from the ERA5 data (1010 m). The reanalysis and the radiosonde-measured VV diverged dramatically in the boundary layers The reanalysis and the radiosonde-measured VV diverged dramatically in the boundary layers marked by the horizontal lines (Figure9b). The reanalysis showed upward motions that were coherent marked by the horizontal lines (Figure 9b). The reanalysis showed upward motions that were with the free-tropospheric measurements, whereas the radiosonde showed a sudden shift to downward coherent with the free-tropospheric measurements, whereas the radiosonde showed a sudden shift motions below the boundary layer. This is because the reanalysis VV data represent VVmean and cannot resolve the turbulent component—VV’. In a planetary boundary layer that is highly turbulent, Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 17 Remote Sens. 2019, 11, 1538 12 of 17 to downward motions below the boundary layer. This is because the reanalysis VV data represent VVmean and cannot resolve the turbulent component – VV’. In a planetary boundary layer that is thehighly rapidly turbulent, varying the VV’ rapidly should varying dominate VV’ theshould VV. dominate This is well the capturedVV. This byis thewell radiosonde captured by data, the whichradiosonde show data, negative which VV show values negati evenve though VV values the cloud even observations though the cloud and synoptic observations conditions and synoptic indicate upwardconditions air indicate motions. upward air motions. FigureFigure 10 shows the the temporal temporal evolution evolution of of base base reflectivity reflectivity from from the the nearest nearest C-band C-band radar radar at ZJK at ZJKfrom from TB site TB (96 site km) (96 during km) during the entire the entire measurem measurementent period period of the ofextreme the extreme radiosonde radiosonde case –RC case7th. –RCThe7th color. The bar color represents bar represents the radar the reflectivity, radar reflectivity, with the with red the curve red curve denoting denoting the radiosonde the radiosonde drift driftpath pathduring during the interval the interval of each of eachradar radar panel. panel. The ma Theximum maximum reflectivity reflectivity was was~60 dBZ ~60 dBZfor this for case. this case. The Theradiosonde radiosonde gradually gradually drifted drifted from from the thecore core region region to the to the borderland borderland of this of this cloud cloud system. system. As Asseen seen by byboth both the the experimental experimental log log and and the the radar radar echo, echo, it was it was a significant a significant convective convective cloud cloud process. process.

FigureFigure 10.10.The The temporal temporal evolution evolution of reflectivity of reflectivity from the fr C-bandom the radarC-band at ZJK radar during at theZJK measurement during the periodmeasurement of the radiosondeperiod of the launched radiosonde at 15:58 launched LST 7 at August 15:58 LST 2012, August over the 7, 2012, TB site. over The the latitude TB site. andThe

longitudelatitude and integers longitude are 41integers◦ N and are 115 41°N◦ E, and respectively; 115°E, respectively; only their minutesonly their are minutes labeled. are The labeled. color barThe representscolor bar represents the radar reflectivitythe radar reflectivity (dBZ); the red(dBZ); curve the denotesred curve the denotes radiosonde the radiosonde drift path during drift path the intervalduring the of each interval radar of panel;each radar and thepanel; black and line the illustrates black line the illu geographicstrates the boundaries.geographic boundaries.

TheThe ground-basedground-based radar radar can can provide provide the the convection convection development development process process for for this this case case (Figure (Figure 10). However,10). However, due todue the to longthe long distance distance from from the radarthe rada to ther to TBthe siteTB site causing causing very very few few data data points points to be to generatedbe generated in the in the vertical vertical direction, direction, it is it not is feasiblenot feasible for thisfor this radar radar to provide to provide the verticalthe vertical profile profile of this of convectivethis convective system system along along the radiosonde the radiosonde path. path. The radiosonde-basedThe radiosonde-based cloud cloud retrieval retrieval algorithm algorithm is thus is usedthus toused determine to determine the vertical the vertical distribution distribution of the of cloud the cloud layer, aslayer, shown as shown in Figure in Figure11. One 11. low One cloud low andcloud one and middle one middle cloudwere cloud detected, were detected, with thicknesses with thicknesses of 2234 of m 2234 and 2087m and m, 2087 respectively. m, respectively. The tops The of bothtops cloudof both layers cloud were layers capped were bycapped inversion by inversion layers. Figure layers. 12 Figurea shows 12a the shows observed the observed vertical descentvertical speeddescent (blue speed line) (blue and line) the and radiosonde the radiosonde descent descent height asheight a function as a function of the descendingof the descending time (red time line). (red Theline). variation The variation tendency tendency of thetemporal of the temporal descent desc heightent washeight generally was generally smooth duringsmooth the during whole the altitude whole range,altitude except range, for except a sudden for a jump sudden occurring jump occurring at approximately at approximately 3 km that 3 was km reflectedthat was byreflected a dramatic by a

RemoteRemote Sens. Sens.2019 2019, 11, 11, 1538, x FOR PEER REVIEW 13 of13 17 of 17

dramaticRemote Sens.fluctuation 2019, 11, x FORin the PEER observed REVIEW descent speed profile. The VV profile superimposed13 on of 17the cloud layers is shown in Figure 12b. Strong positive and negative VV occurred near the lower-level fluctuationdramatic in thefluctuation observed in descentthe observed speed descent profile. sp Theeed VVprofile. profile The superimposedVV profile superimposed on the cloud on layersthe is cloud top and even <-10 meters above the cloud top, respectively. The switch between the updraft showncloud in Figure layers is12 shownb. Strong in Figure positive 12b.and Strong negative positive VV and occurred negative nearVV occurred the lower-level near the lower-level cloud top and and the downdraft should be associated with the turbulence dissipation [14]. This was likely a typical even 2 m/sof this was convection ever presente cloud.d atIn 1.4 addition km, which to the was strong close VV to atthe the bottom cloud oftop the of this convection cloud. In addition to the strong VV at the cloud top height, an evident updraft cloud.height, an evident updraft >2 m/s was ever presented at 1.4 km, which was close to the bottom of the >2 m/cloud.s was ever presented at 1.4 km, which was close to the bottom of the cloud.

FigureFigureFigure 11. 11.Cloud 11.Cloud Cloud layers layers layersretrieved retrieved retrieved fromfrom from the the radiosonde radiosonde launched launched atat at 15:58 15:58 LSTLST LST AugustAugust 7 August 7, 7, 2012 2012 2012 (RC (RC (RC7th7th) )7th ) overover theover the TB theTB site. TBsite. site. Cyan Cyan Cyan shading shading shading marksmarks marks the the extent extent of ofof the thethe cloud cloud layer.layer. layer. The The cloud cloud basebase base heights heights heights (CBH (CBH (CBH1 for1 for 1 for the lower layer and CBH2 for the upper layer) and their corresponding cloud top heights (CTH1 and thethe lower lower layer layer and and CBH CBH22for for thethe upperupper layer) and and their their co correspondingrresponding cloud cloud top top heights heights (CTH (CTH1 and1 and CTH2) of the two cloud layers are noted. The radiosonde vertical profiles of RH with respect to water, CTHCTH2)2 of) of the the two two cloud cloud layerslayers areare noted. The The radiosonde radiosonde vertical vertical profiles profiles of ofRH RH with with respect respect to water, to water, RH with respect to ice when the temperatures are less than 0°C, and the temperature are shown by RHRH with with respect respect to to ice ice when when the the temperatures temperatures are are less less than than 0 ◦0°C,C, and and the the temperature temperatureare areshown shown bybythe the solid blue line, the dashed blue line, and the solid red line, respectively. solidthe bluesolid line,blue theline, dashed the dashed blue blue line, lin ande, and the solidthe solid red red line, line, respectively. respectively.

Figure 12. (a) Observed descent speed (dashed blue line) and the descending height as a function of FigureFiguretime 12. 12. (solid(a (a)) Observed Observedred line) for descent descent the radiosonde speedspeed (dashed launched blue blue at 15:58line) line) anLST andd theAugust the descending descending 7, 2012 (RC height height7th). (b) as asThea function a vertical function of of velocity of air (red line) superimposed on the cloud layers (cyan shadings) with the base heights timetime (solid (solid red red line) line) for for the the radiosonde radiosonde launchedlaunched at at 15:58 15:58 LST LST August 7 August 7, 2012 2012 (RC (RC7th7th). (b)). ( bThe) The vertical vertical velocity velocity of of air air (red (red line) line) superimposed superimposed on on the the cloud cloud layers layers (cyan (cyan shadings) shadings) with with the basethe base heights heights (CBH 1 and CBH ) and top heights (CTH and CTH ) noted. The vertical velocity values of 2 m/s, 0 m/s and 2 1 2 − 2 m/s are denoted by the dashed lines colored black, blue and black, respectively. Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 17

Remote(CBH Sens. 12019 and, 11CBH, 15382) and top heights (CTH1 and CTH2) noted. The vertical velocity values of -2 m/s, 140 of 17 m/s and 2 m/s are denoted by the dashed lines colored black, blue and black, respectively.

Figure 1313 shows shows the the other other two two radiosonde radiosonde cases cases that th detectedat detected relatively relatively larger larger absolute absolute VV ( >VV2 m (>2/s) comparedm/s) compared to the to general the general VV value. VV value. The twoThe radiosondestwo radiosondes were were launched launched at 12:46 at 12:46 LST 7LST August August and 7 10:53and 10:53 LST LST 8 August August 2012, 8, 2012, respectively. respectively. Their Their synoptic synoptic systems systems were were similar similar to the to previousthe previous case, case, i.e., thei.e., TBthe siteTB site was was located located in the in the low low pressure pressure at a at 500 a 500 hPa hPa level. level. Great Great VV VV variations variations were were presented presented by bothby both cases. cases. The The first first case case was was a two-layered a two-layered cloud cloud configuration. configuration. A large A large VV VV was was observed observed within within the low-levelthe low-level cloud, cloud, with with the strongestthe strongest downdraft downdraft being being2.1 -2.1 m/s m/s at 1.5 at km,1.5 km, located located at the at middlethe middle of this of − cloudthis cloud layer. layer. The The VV thenVV then fluctuated fluctuated between between 4 and 4 and 5 km. 5 km. The The relatively relatively larger larger absolute absolute VV VV value value of 1.3of 1.3 m/ sm/s was was retrieved retrieved at 6.2 at km6.2 km within within the high-levelthe high-level cloud. cloud. Regarding Regarding the second the second case, case, one cloud one cloud layer withlayer awith thickness a thickness of 2.9 kmof 2.9 was km determined was determined by the by radiosonde. the radiosonde. The VV The was VV relatively was relatively stable belowstable 3.5below km, 3.5 followed km, followed by a noticeable by a noticeable variation variation of an updraft of an> updraft3 m/s and >3 am/s downdraft and a downdraft< 1 m/s that <-1 occurredm/s that − withinoccurred 1 km within below 1 km the cloudbelow base the height.cloud base The height VV measurements. The VV measurements with the greatest with values the greatest close to values 2 m/s wereclose alsoto 2 m/s noted were within also the noted cloud within layer. the cloud layer.

Figure 13. Panels (a,b) show the synoptic system analysis, which is the same as Figure9b but at 12:00 LSTFigure on 13. 7 August Panelsand (a) and 10:00 (b) LST show on 8the August synoptic 2012, system respectively. analysis, Panels which (c is,d )the denote same the as Figure vertical 9b velocity, but at which12:00 LST are theon August same as 7 in and Figure 10:00 12 LSTb but on for Au thegust other 8 of two2012, radiosondes respectively. launched Panels at(c)12:46 and (d) LST denote 7 August the andvertical 10:53 velocity, 8 August which 2012, are respectively. the same as in Figure 12b but for the other two radiosondes launched at 12:46 LST August 7 and 10:53 August 8 of 2012, respectively. 5. Discussion and Conclusions 5. Discussion and Conclusion Vertical air motion has long been among the most difficult quantities to measure. Some progress has beenVertical made air in motion air velocity has long retrieval been among in recent the years most by difficult using ground-based quantities to measure. and space-borne Some progress remote sensinghas been instruments.made in air velocity However, retrieval such in information recent years is by still using insu ground-basedfficient for most and space-borne parts of the remote world, including a large territory of China. Thus, more in situ observations are desperately needed. In this

Remote Sens. 2019, 11, 1538 15 of 17 study, a descending radiosonde system is developed based on the conventional radiosonde operated at upper air observation stations. By using descending radiosonde data collected at a local site in north China during the summer of 2012 (from 3 to 8 August), the vertical air velocity was derived at the low and middle troposphere. The VV generally varied from 1 m/s to 1 m/s, accounting for 80.2% of all analyzed data points. − Compared to the nonconvective condition, the VV distribution was wider and skewed to the large value under the convective condition. Apparent vertical air motion was exhibited below 5 km under the convective activities. The average VV throughout the entire profiles was close to 0 m/s under noconvective conditions. Strong vertical air motion (>2 m/s) was observed by a few radiosonde profiles. Radiosonde-based VV profiles showed similar gross patterns with retrievals from the reanalysis data for a case analysis. Moreover, the detailed structure of the instant air motion in the vicinity of the cloud boundaries (i.e., top and base), with an absolute VV >10 m/s occurring in the convection weather system, was well captured by the in situ radiosonde observational technique. The radiosonde has been in routine operation at more than 120 stations in China for over a decade. In view of the extra low-cost attached to the routine radiosonde operation, it is hoped that this system could be applied to this radiosonde network, and thus, provide additional VV information over a large space of China. This would be beneficial for monitoring climate change and may be used to validate and potentially improve the dynamic parameterization in model simulations. The preliminary results in this study showed that the technique presented here could be used to derive vertical air motion, which is likely to be deployed for conventional operations and scientific applications. However, taking the following factors into account, only the VV at the low and middle troposphere are discussed in this study: (1) it will take some time for the radiosonde from dropping at a high troposphere to achieve posture balance, and thereby, the quasi-equilibrium state of descent, (2) the great radiosonde descent speed at the upper troposphere could induce large uncertainty in VV retrievals due to their small conventional magnitudes (usually less than dozens of centimeters), and (3) the radiosonde we used tends to provide a more stable performance at the low and middle troposphere. Future studies are needed to reduce the course of the posture balance and to decrease the radiosonde descent speed, thereby favoring a reasonable VV estimation at high altitude levels as well as a better VV retrieval at low/middle altitude levels. Radiosonde descent rate profiles under stable weather conditions are selected and processed to calculate C in this study. More attention should be paid to finding a better estimation method of C and its uncertainty, to improve the VV retrievals. Due in part to the generally small magnitude and high temporal and spatial variability of the vertical air, it is currently difficult to collocate objects detected by a drifting radiosonde and other measurements (e.g., fixed ground-based) to make a point-to-point data comparison. The uncertainty estimation of our system was deduced from the numerical calculation in this study. The results indicated that this method had the potential to capture and describe events with vertical air motions >0.69 m/s, which is useful for convective weather study, as shown by the case analysis with VV > 2m/s and even up to 10 m/s. Further studies will focus on the accurate validation of the method. Satellite measurements and/or a network composed of combined Doppler lidar/radar that can cover the predicted drifting path of the radiosonde should help achieve a careful assessment of the VV method, especially in case of large balloon drift and in cloudy conditions.

Author Contributions: Conceptualization, J.Z., H.S. and H.C.; methodology, J.Z., H.S., Y.T., and Y.Z.(Yanliang Zhu); validation, Y.Z. (Youtong Zheng), F.W., X.H., Y.X and J.L.; data analysis, J.Z. and H.S.; writing—review and editing, J.Z., H.S., X.X. and Y.Z. (Youtong Zheng). Funding: This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA17010101), National Key R&D Program of China (Grant No. 2017YFA0603504), and National Natural Science Foundation of China (Grant No. 41875183, 41675034, 41775005 and 41805021). Acknowledgments: The authors would like to thank Chao Ling for preparing and launching the radiosonde. Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2019, 11, 1538 16 of 17

References

1. Heymsfield, A.J. Precipitation Development in Stratiform Ice Clouds: A Microphysical and Dynamical Study. J. Atmos. Sci. 1977, 34, 367–381. [CrossRef] 2. Li, Z.; Lau, W.K.M.; Ramanathan, V.; Wu, G.; Ding, Y.; Manoj, M.G.; Liu, J.; Zhou, T.; Fan, J.; Rosenfeld, D.; et al. Aerosol and monsoon climate interactions over Asia. Rev. Geophys. 2016, 54, 866–929. [CrossRef] 3. Zhao, B.; Jiang, J.H.; Diner, D.J.; Su, H.; Gu, Y.; Liou, K.N.; Jiang, Z.; Huang, L.; Takano, Y.; Fan, X.; et al. Intra-annual variations of regional aerosol optical depth, vertical distribution, and particle types from multiple satellite and ground-based observational datasets. Atmos. Chem. Phys. 2018, 18, 11247–11260. [CrossRef][PubMed] 4. Paluch, I.R.; Lenschow, D.H. Stratiformcloud formation in the marine boundary layer. J. Atmos. Sci. 1991, 48, 2141–2158. [CrossRef] 5. Sun, J.; Leighton, H.; Yau, M.K.; Ariya, P. Numerical evidence for cloud droplet nucleation at the cloud-environment interface. Atmos. Chem. Phys. 2012, 12, 12155–12164. [CrossRef] 6. Leary, C.A.; Houze, R.A. The contribution of mesoscale motions to the mass and heat fluxes of an intense tropical convective system. J. Atmos. Sci. 1980, 37, 784–796. [CrossRef] 7. Stern, D.P.; Aberson, S.D. Extreme vertical winds measured by dropwindsondes in hurricanes. In Proceedings of the 27th Conference on Hurricanes and Tropical Meteorology, Monterey, CA, USA, 28 April 2006; Available online: http://ams.confex.com/ams/pdfpapers/108766.pdf (accessed on 8 May 2019). 8. Kim, Y.J.; Eckermann, S.D.; Chun, H.Y. An overview of the past, present and future of gravity-wave drag parameterization for numerical climate and weather prediction models. Atmos. Ocean 2003, 41, 65–98. 9. Holton, J.R. An Introduction to Dynamic Meteorology; Academic Press: San Diego, CA, USA, 1922; p. 507. 10. Lenschow, D.H.; Stephens, P.L. The role of thermals in the convective boundary layer. Bound. Layer Meteor. 1980, 19, 509–532. [CrossRef] 11. Greenhut, G.K.; Singh Khalsa, S.J. Updraft and downdraft events in the atmospheric boundary layer over the equatorial Pacific Ocean. J. Atmos. Sci. 1982, 39, 1803–1818. [CrossRef] 12. Saïd, F.; Canut, G.; Durand, P.; Lohou, F.; Lothon, M. Seasonal evolution of boundary-layer turbulence measured by aircraft during the AMMA 2006 Special Observation Period. Quart. J. R. Meteor. Soc. 2010, 136, 47–65. [CrossRef] 13. Balsley, B.B.; Ecklund, W.L.; Carter, D.A.; Riddle, A.C.; Gage, K.S. Average vertical motions in the tropical atmosphere observed by a radar wind profiler on Pohnpei (7oN latitude, 157oE longitude). J. Atmos. Sci. 1988, 45, 396–405. [CrossRef] 14. Shupe, M.D.; Kollias, P.; Poellot, M.; Eloranta, E. On deriving vertical air motions from cloud radar Doppler spectra. J. Atmos. Ocean. Technol. 2008, 25, 547–557. [CrossRef] 15. Kollias, P.; Albrecht, B.A.; Lhermitte, R.; Savtchenko, A. Radar observations of updrafts, downdrafts, and turbulence in fair-weather cumuli. J. Atmos. Sci. 2001, 58, 1750–1766. [CrossRef] 16. Ansmann, A.; Fruntke, J.; Engelmann, R. Updraft and downdraft characterization with Doppler lidar: Cloud-free versus cumuli-topped mixed layer. Atmos. Chem. Phys. 2010, 10, 7845–7858. [CrossRef] 17. Contini, D.; Mastrantonio, G.; Viola, A.; Argentini, S. Mean vertical motions in the PBL measured by Doppler sodar: Accuracy, ambiguities, and possible improvements. J. Atmos. Oceanic Technol. 2004, 21, 1532–1544. [CrossRef] 18. Zhao, K.; Wang, M.; Xue, M.; Fu, P.; Yang, Z.; Chen, X.; Zhang, Y.; Lee, W.; Zhang, F.; Lin, Q.; et al. Doppler radar analysis of a tornadic miniature supercell during the Landfall of Typhoon Mujigae (2015) in South China. Bull. Amer. Meteor. Soc. 2017, 98, 1821–1831. [CrossRef] 19. Zheng, Y.; Rosenfeld, D. Linear relation between convective cloud base height and updrafts and application to satellite retrievals. Geophys. Res. Lett. 2015, 42, 6485–6491. [CrossRef] 20. Zheng, Y.; Rosenfeld, D.; Li, Z. Satellite inference of thermals and cloud base updraft speeds based on retrieved surface and cloud base temperatures. J. Atmos. Sci. 2015, 72, 2411–2428. [CrossRef] 21. Zheng, Y.; Rosenfeld, D.; Li, Z. Quantifying cloud base updraft speeds of marine stratocumulus from cloud top radiative cooling. Geophys. Res. Lett. 2016, 43, 11407–11413. [CrossRef] 22. Luo, Z.J.; Jeyaratnam, J.; Iwasaki, S.; Takahashi, H.; Anderson, R. Convective vertical velocity and cloud internal vertical structure: An A-Train perspective. Geophys. Res. Lett. 2014, 41, 723–729. [CrossRef] Remote Sens. 2019, 11, 1538 17 of 17

23. Treddenick, D.S. A comparison of aircraft and Jimsphere wind measurements. J. Appl. Meteor. 1971, 10, 309–312. [CrossRef] 24. Franklin, J.; Black, M.L.; Valde, K. GPS dropwindsonde wind profiles in hurricanes and their operational implications. Weather Forecast. 2003, 18, 32–44. [CrossRef] 25. Gallice, A.; Wienhold, F.G.; Hoyle, C.R.; Immler, F.; Peter, T. Modeling the ascent of sounding balloons: Derivation of the vertical air motion. Atmos. Meas. Tech. 2011, 4, 2235–2253. [CrossRef] 26. Bony, S.; Stevens, B. Measuring area-averaged vertical motions with dropsondes. J. Atmos. Sci. 2019, 76, 767–783. [CrossRef] 27. Lalas, D.P.; Einaudi, F. Tropospheric gravity waves: Their diction by and influence on rawinsonde balloon data. Quart. J. R. Meteor. Soc. 1980, 106, 855–864. [CrossRef] 28. McHugh, J.P.; Dors, I.; Jumper, G.Y.; Roadcap, J.R.; Murphy, E.A.; Hahn, D.C. Large variations in balloon ascent rate over Hawaii. J. Geophys. Res. 2008, 113, D15123. [CrossRef] 29. Johansson, C.; Bergström, H. An auxiliary tool to determine the height of the boundary layer. Bound. Layer Meteor. 2005, 115, 423–432. [CrossRef] 30. Wang, J.; Bian, J.; Brown, W.O.; Cole, H.; Grubiši´c,V.; Young, K. Vertical Air Motion from T-REX Radiosonde and Dropsonde Data. J. Atmos. Ocean. Technol. 2009, 26, 928–942. [CrossRef] 31. Wick, G.A.; Hock, T.F.; Neiman, P.J.; Vomel, H.; Black, M.L.; Spackman, J.R. The NCAR–NOAA Global Hawk Dropsonde System. J. Atmos. Ocean. Technol. 2018, 35, 1585–1604. [CrossRef] 32. Wu, C.C.; Chou, K.H.; Lin, P.H.; Aberson, S.D.; Peng, M.S.; Nakazawa, T. The impact of dropwindsonde data on typhoon track forecasts in DOTSTAR. Weather Forecast. 2007, 22, 1157–1176. [CrossRef] 33. MacCready, P.B. Comparison of some balloon techniques. J. Appl. Meteor. 1965, 4, 504–508. [CrossRef] 34. Nash, J.; Oakley, T.; Vömel, H.; Li, W. WMO Intercomparison of High Quality Radiosonde Systems, Yangjiang, China, 12 July–3 August 2010. Tech. Rep., WMO, 2011, WMO/TD-No. 1580, Instruments and Observing Methods Report No. 107. Available online: http://library.wmo.int/pmb_ged/wmo-td_1580.pdf (accessed on 10 December 2018). 35. Xie, Q.; Huang, K.; Wang, D.; Yang, L.; Chen, J.; Wu, Z.; Li, D.; Liang, Z. Intercomparison of GPS radiosonde soundings during the eastern tropical Indian Ocean experiment. Acta Oceanol. Sin. 2014, 33, 127–134. [CrossRef] 36. Hou, T.; Lei, H.; Yang, J.; Hu, Z.; Feng, Q. Investigation of riming within mixed-phase stratiform clouds using Weather Research and Forecasting (WRF) model. Atmos. Res. 2016, 178–179, 291–303. [CrossRef] 37. Zhang, J.; Li, Z.; Chen, H.; Cribb, M. Validation of a radiosonde-based cloud layer detection method against a ground-based remote sensing method at multiple ARM sites. J. Geophys. Res. 2013, 118, 846–858. [CrossRef] 38. Miloshevich, L.M.; Vömel, H.; Whiteman, D.N.; Lesht, B.M.; Schmidlin, F.J.; Russo, F. Absolute accuracy of water vapor measurements from six operational radiosonde types launched during AWEX-G and implications for AIRS validation. J. Geophys. Res. 2006, 111, D09S10. [CrossRef] 39. Zhang, J.; Chen, H.; Bian, J.; Xuan, Y.; Duan, Y.; Cribb, M. Development of cloud detection methods using CFH, GTS1, and RS80 radiosondes. Adv. Atmos. Sci. 2012, 29, 236–248. [CrossRef] 40. Bian, J.; Chen, H.; Vömel, H.; Duan, Y.; Xuan, Y.; Lü, D. Intercomparison of humidity and temperature sensors: GTS1, Vaisala RS80, and CFH. Adv. Atmos. Sci. 2011, 28, 139–146. [CrossRef] 41. Guo, Q.Y.; Li, F.; Guo, K.; Yang, R.K. Comparative analysis of new GPS and GTS1-2 radiosondes. Meteor. Sci. Technol. 2005, 43, 59–64, (In Chinese with English Abstract). 42. Vennard, J.K. Elementary Fluid Mechanics, 7th ed.; John Wiley & Sons: Hoboken, NJ, USA, 1955; p. 401. 43. Shi, H.; Chen, H.; Xia, X.; Fan, X.; Zhang, J.; Li, J.; Ling, C. Intensive radiosonde measurements of summertime convection over the Inner Mongolia grassland in 2014: Difference between shallow cumulus and other conditions. Adv. Atmos. Sci. 2017, 34, 783–790. [CrossRef] 44. Zhuang, Y.; Fu, R.; Marengo, J.A.; Wang, H. Seasonal variation of shallow-to-deep convection transition and its link to the environmental conditions over the Central Amazon. J. Geophys. Res. 2017, 122, 2649–2666. [CrossRef] 45. Yang, J.; Wang, Z.; Heymsfield, A.J.; French, J.R. Characteristics of vertical air motion in isolated convective clouds. Atmos. Chem. Phys. 2016, 16, 10159–10173. [CrossRef]

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