RESEARCH LETTER
10.1029/2020GL090675
Key Points:• A novel, satellite-based method wasdeveloped to retrieve profiles of
convective vertical velocity (wc) andconvective mass flux (Mc)• Comparisons with collocatedground-based radar wind profiler
observations show broad agreements• A k-means cluster analysis showsthat convective vertical velocity is
distinctly different between tropicalland and oceanic convection
Correspondence to:Z. J. Luo,zluo@ccny.cuny.edu
Citation:
Jeyaratnam, J., Luo, Z. J., Giangrande,
S. E., Wang, D., & Masunaga, H.
(2021). A satellite-based estimateof convective vertical velocity andconvective mass flux: Global survey andcomparison with radar wind profiler
observations. Geophysical Research
Letters, 48, e2020GL090675. https://doi.
org/10.1029/2020GL090675
Received 4 SEP 2020
Accepted 24 NOV 2020

A Satellite-Based Estimate of Convective Vertical
Velocity and Convective Mass Flux: Global Survey andComparison With Radar Wind Profiler Observations
Jeyavinoth Jeyaratnam1 , Zhengzhao Johnny Luo2
Dié Wang3 , and Hirohiko Masunaga4, Scott E. Giangrande3,

1

The Graduate Center, Earth and Environmental Sciences Program, City University of New York, New York, NY, USA,
Department of Earth and Atmospheric Sciences, City University of New York, City College, New York, NY, USA,3
Department of Environmental and Climate Sciences, Brookhaven National Laboratory, Upton, NY, USA, 4Institute forSpace-Earth Environmental Research, Nagoya University, Nagoya, Japan
2

Abstract Convective vertical velocity (wc) and convective mass flux (Mc) lie at the heart of global

climate model cumulus parameterizations, but few observations of these critical parameters are available.
This paper develops and evaluates a novel, satellite-based method for estimating profiles of wc and Mc.
Comparisons with collocated ground-based radar wind profiler (RWP) observations show that satelliteestimated median wc is slightly greater than the RWP estimates, but they show solid agreement whencompared at the 95th percentiles (intense updrafts). RWP-derived and satellite-estimated Mc are broadly
comparable in the lower and middle troposphere, with some differences in the upper troposphere due todifferences in convective core sampling. A k-means cluster analysis of multiple years of wc data shows thatconvective characteristics are distinctly different among extratropical convection, tropical land convection,and tropical oceanic convection. Tropical land convection is significantly more intense and more variable
than the oceanic counterpart.

Plain Language Summary Properly representing and parameterizing cumulus convection

has long been a central task of the global climate models (GCMs), as GCM simulations are highly sensitive
to the assumptions made in the cumulus parameterization schemes, resulting in large uncertainties in
predicting the future climate change. At the heart of many of today's GCM convection parameterizations
are convective vertical velocity (wc) and mass flux (Mc). Yet, few global observations of these criticalparameters are available at this time globally. To improve this situation, the authors developed a novel,satellite-based method for estimating profiles of wc and Mc. We first evaluate the satellite estimates against
collocated ground-based radar wind profiler (RWP) retrievals. RWP-derived and satellite-estimated wc andMc are found to be broadly comparable, and their agreement improves when differences in sampling anddefinitions of convective clouds are considered. A k-means cluster analysis of multiple years of satelliteestimated wc data is performed to identify salient patterns related to the structure and global distributionsof convective vertical velocity. Results show that convective characteristics are distinctly different among
extratropical convection, tropical land convection, and tropical oceanic convection. Our ongoing work
seeks to use this newly developed wc and Mc data set to evaluate GCM cumulus parameterizations.

1. Introduction© 2020. American Geophysical Union.
All Rights Reserved.

JEYARATNAM ET AL.

Convection is fundamental to atmospheric energetics and general circulation. While the ultimate energy
source for Earth's atmosphere comes from the Sun, most of the solar insolation is directly deposited at thesurface. Convection redistributes the energy by transporting it from the surface upward into the atmosphere,balancing atmospheric cooling in the infrared, and driving large-scale circulation (e.g., Hartmann, 2016).
In doing so, convection also moves momentum, moisture, and chemically important trace constituentsfrom the planetary boundary layer to the free troposphere. Deep convection can even communicate these
influences all the way to the upper troposphere and lower stratosphere (e.g., Fueglistaler et al., 2009). For
these reasons, properly representing convection and convective transport has always been a central task of
the global climate models (GCMs) (e.g., Arakawa, 2004).

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Because convective core occurs on the scale of O(1–10 km) which is smaller than the grid box of current GCMs
(O[100 km]), cumulus convection cannot be explicitly represented and has to be parameterized as a function ofgrid-scale variables. GCM simulations are highly sensitive to the assumptions made in the parameterizations,
resulting in large uncertainties in our ability to predict the future climate change (e.g., Sanderson et al., 2008).
A large number of current GCM cumulus parameterization schemes are based on the concept of convective
mass flux (defined as the product of air density, convective cloud coverage, and vertical velocity), and tied tothe model's large-scale variables via a closure scheme (see Arakawa, 2004 for a review). Improved assessmentof the fidelity for cumulus parameterizations requires new ways to observe convective mass flux globally.
The most challenging part for obtaining convective mass flux is estimating the vertical air motions inside
deep convective clouds (DCCs). Historically, the most direct observational approach was to fly instrumented aircraft into DCCs to measure the vertical air velocity and other updraft/downdraft core properties insitu. Over the past 70 years, numerous storm penetration flights have enabled important firsthand knowledge of the inner workings of DCCs (e.g., Byers & Braham, 1948; LeMone & Zipser, 1980a, 1980b; Lucaset al., 1994). However, operational costs and safety concerns place severe constraints on such flights, limiting the available data sets while significantly under-sampling the strongest cloud drafts. As an alternative,
high-altitude aircraft may fly above convective storms and use a nadir-viewing Doppler radar to retrieve
convective air motions. For example, a 10-year summary of overflight measurements from numerous field
campaigns have been compiled and analyzed by Heymsfield et al. (2010) to characterize DCCs from various
environments in the tropics and subtropics. In a similar fashion, ground-based (multi-) Doppler radars andwind profilers have been exploited using various methodologies to estimate convective vertical velocities
remotely (Giangrande et al., 2013, 2016; Kumar et al., 2015; May & Rajopadhyaya, 1999; North et al., 2017;
D. Wang et al., 2019, 2020; Williams, 2012). These ground-based retrievals, in particular those from vertically pointing radar wind profilers (RWPs), afford continuous and long-term measurements in deeper andmidlatitude convective clouds where prior in situ or overpass aircraft estimates are limited.
Although these above-mentioned observational studies provide valuable insights into convective verticalvelocity and mass flux, available observations of the convective cloud kinematic properties are limited inspace and time, especially over the tropics and open oceans, making it difficult to generalize such findings
globally for developing or evaluating GCM cumulus parameterizations (Donner et al., 2001, 2016). Observations from satellites offers a potential solution to the problem. However, no current satellite is designed
for estimating the vertical air motions inside convective clouds. Although plans for deploying space-borne
Doppler cloud radars have been undertaken for the near future (e.g., EarthCARE mission; Illingworth
et al., 2015), severe attenuation of W-band (94-GHz) or similar cloud radar through heavy precipitation
makes it challenging to retrieve vertical velocity profiles inside convective cores (Z. J. Luo et al., 2014). To
fill the vacuum, a novel, satellite-based method has been proposed in a recent study by Masunaga and Luo(2016; hereafter ML16). Unlike previous radar-based methods that focus on measurements of each individual cloud, ML16 brings together multiple information including the observed state of convective cloud, as
well as constraints such as the ambient sounding and underlying dynamic and thermodynamic principles
by which convective cloud is bound to abide (i.e., a plume model), and then combines them in a Bayesianmanner to estimate convective vertical velocity and mass flux.
This current study builds upon ML16 with the following two motivations: 1) Evaluate satellite estimates of
convective vertical velocity and mass flux with collocated ground-based RWP observations made by Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program (Section 3). Focus is placedon the updrafts in convective clouds. 2) Extend ML16 study (that emphasized equatorial oceanic region)over the entire globe to identify major global patterns of convective cloud kinematics (Section 4). Section 5summarizes the study and discusses its significance and future applications.

2. Data and Methodology
2.1. Data Description
Satellite data used for this study come from the A-Train constellation (Stephens et al., 2002), mainly CloudSat Cloud Profiling Radar (CPR; Stephens et al., 2008), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite

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Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP; Winker et al., 2010),
and Moderate Resolution Imaging Spectroradiometer (MODIS). The variables relevant to the ML16 method
include 1) CPR and CALIOP cloud-top height, and 2) CPR reflectivity profiles, both obtained from CloudSat 2B-GEOPROF-LIDAR product (Mace & Zhang, 2014), and 3) MODIS infrared brightness temperature,obtained from standard MODIS radiance data (Platnick et al., 2003). The CloudSat CPR footprint size is
2.3 km along track and 1.4 km cross track, and the effective vertical resolution is 480 m, oversampled at
240 m. CALIOP data have higher horizontal and vertical resolutions, but they are anchored on CPR profiles
in the 2B-GEOPROF-LIDAR data such that the effective resolutions are reduced to match those of CPR.
MODIS infrared brightness temperatures have horizontal resolution of 1 km. ML16 also used sounding
data from satellite sounders. In this study, we use collocated European Centre for Medium-Range Weather
Forecast (ECMWF) operational analyses that were obtained from the ECMWF-AUX product of CloudSat
and have the horizontal resolution of 0.5°.
Ground-based data are obtained from measurements collected by the DOE ARM (Mather & Voyles, 2013)
Mobile Facility (AMF) at Manacapuru, Brazil (MAO) during the “Observations and Modeling of the Green
Ocean Amazon 2014–2015” (GoAmazon 2014/5, e.g., Martin et al., 2017) field campaign. The primary observations used are convective cloud vertical velocity and mass flux derived from vertically pointing RWP measurements operated at ultrahigh frequencies (1,290 MHz at MAO). The RWPs are not attenuated in rain owing
to their long wavelength, and calibrated using collocated disdrometer observations (e.g., D. Wang et al., 2018).
These measurements were made in time-height space at ∼10-s temporal and 200-m gate resolutions using
operational precipitation modes employed by ARM that are not susceptible to Bragg echo contaminationin deep convective contexts (e.g., Tridon et al., 2013). Estimates of vertical velocity at DCCs and periphery
regions follow the retrieval methods described in Giangrande et al. (2013; 2016) and D. Wang et al. (2020).

2.2. A Brief Description of ML16 Method
ML16 developed a hybrid method to estimate convective vertical velocity (wc) and convective mass flux(Mc). Input information comes from three main sources: ambient sounding, plume model, and satellite
cloud observations. These inputs are combined in a Bayesian manner to produce estimates of wc and Mc.
In Figure 1, we use a flowchart to schematically illustrate this procedure. First, CloudSat CPR reflectivity
profiles are used to identify convective cores following early studies by Z. Luo et al. (2008, 2009) and Z.
J. Luo et al. (2010), as illustrated in Figures 1a and 1c. A convective core is defined as consecutive radar
profiles that contain continuous radar echoes extending from the planetary boundary layer to cloud top,with 10 dBZ echo-top height being within 2 km of cloud top. The cloud-top buoyancy factor, represented
by Tc  Ta  / Ta is estimated for each convective core following Z. J. Luo et al. (2010), where Tc is cloud-top
temperature and Ta is the ambient temperature of the same level. Tc is estimated from MODIS IR brightness
temperature after accounting for parallax shift of MODIS relative to CloudSat and CALIPSO (C. Wanget al., 2011) and nonblackbody effect in the infrared (C. Wang et al., 2014). Ta is obtained from the collocated
ECMWF analyses.
CloudSat observations along track are divided into segments of O(100 km); each is comparable to a GCM
grid box. Then, ambient temperature and moisture sounding at each segment drives the convective plumemodel, which generates a range of in-cloud buoyancy and vertical velocity profiles. The main unknown
parameter (to be constrained by observations) in this process is the turbulent entrainment rate, which isassumed to vary by a wide range from 0 to 0.4 km−1, following ML16. This step is schematically shownin Figures 1b and 1d. Next, the observed cloud-top buoyancy of each convective core is used to weigh theplume model simulations via a Bayesian approach to arrive at a final estimate of buoyancy and wc profiles,
as illustrated in Figures 1e and 1f.
Finally, convective mass flux Mc is calculated as M c   wc , where σ is the coverage of convective clouds
and ρ is air density. Convective coverage σ is estimated from CloudSat CPR profiles and is determined bycounting the fractional coverage of convective cores within each segment as illustrated in Figure 1g. Air
density ρ is obtained from the collocated ECMWF analyses. For a complete description of the ML16 method, including the plume model and Bayesian formula, see Section 3 of ML16.

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Figure 2. (a) Convective vertical velocity profiles from ground-based RWP observations (black) and ML16 estimates (red). Solid lines represent the medianprofiles, and crosses represent the 95th percentiles (i.e., intense updrafts). (b) ML16 retrieved convective vertical velocity profiles sorted by convective depth.
Only the 95th percentiles are shown. (c) Same as (b), except for RWP estimates.

The ML16 method only considers convective updrafts. Downdrafts are not easily identified from CloudSat radar reflectivity profiles. The contribution of downdraft mass flux can be indirectly inferred from thedifference between total mass flux and convective updraft mass flux, as demonstrated in ML16. However,determination of total mass flux requires additional measurements that are not available globally (e.g., windfields from QuickSCAT, available only over ocean; see ML16). Therefore, we confine this study to updraft
properties and leave the consideration of downdrafts to a future study. For a fair comparison, only updraft
properties (and their fractional coverage) estimated by the RWP observations are used.

3. Comparison Between Satellite Estimates and Radar Wind Profiler
Observations
RWP and satellite estimates use different sampling strategies and retrieval methods. Therefore, a meaningful comparison is only possible in a statistical sense. Similar to previous observational studies that attempt
to extract convective mass flux profiles over GCM-scale grids (e.g., Kumar et al., 2015), we use 3-hourly averages, aggregated over long-term RWP observations (translates to a spatial domain of about 60-km, assuming
an average propagation speed of 5 m s−1). This procedure is intended to align these column observations
with the proposed satellite estimates collected in a 0.5° × 0.5° box around the ARM MAO site. Comparisonsare performed for the entire 2-year GoAmazon2014/5 field campaign.
Figure 2a shows the summary comparisons of wc profiles in terms of the updraft median and 95th percentile properties. Both RWP observations and satellite estimates show that wc generally increases with height,
a feature that is commonly observed in DCCs (Giangrande et al., 2016; Heymsfield et al., 2010; Kumar
et al., 2015; D. Wang et al., 2019, 2020). This is in line with the expectation that buoyancy acceleration withinconvective updraft is usually positive until air parcel overshoots which usually occurs at high altitudes nearcloud top (Z. J. Luo et al., 2010; Takahashi & Luo, 2012; Takahashi et al., 2017). Note, median wc values areslightly larger for satellite estimates than RWP values. This difference may be attributed to different sampling strategies as to defining “core” regions: the RWP vertical velocity profiles include contributions from
intense convective updraft cores and the periphery DCC regions, provided that the measured vertical velocity exceeds 1.5 ms−1 (Giangrande et al., 2016; D. Wang et al., 2020). However, the ML16 method is designed
to characterize the vertical velocity of only the well-defined and active updrafts captured by CloudSat CPR.
To account for some of these differences, we also estimate the 95th percentile velocities from both platforms

Figure 1. Schematic flowchart illustrating the procedure of estimating convective vertical velocity (wc) and convective mass flux (Mc), as developed in Masunaga
and Luo (2016) or ML16.

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Geophysical Research Letters10.1029/2020GL090675(crosses in Figure 2a) that focus only on the more intense updrafts/core properties. Now, the systematic bias
seen in the median wc is no longer evident. A better agreement is seen in comparison of the 95th percentiles,
especially in the upper troposphere. A local spike is seen in the RWP 95th percentile vertical velocity profileat the altitudes of 6–8 km. One physical interpretation is that this spike could be attributed to additional
buoyancy added through latent heating release from fusion and freezing above the melting level (Zipser,
2003) that is not precisely reproduced by the simplified microphysics built in the plume model. However,this spike may also reflect a practical consequence of conditional sampling bias, wherein the RWP retrievals/
extremes for DCC “hits” are less frequently captured (and therefore prone to variability) at these altitudes.
To gain further insight into convective cloud structure, we sort vertical velocity profiles by radar echo-top
height, which serves as a proxy for convective depth (Figures 2b and 2c), following D. Wang et al. (2019).
This sorting better differentiates the properties for the different modes of convection (e.g., deep convection,
cumulus congestus, and shallow convection, Johnson et al., 1999). Since RWP and satellite estimates are initially comparable in their capability to gauge intense updrafts (see Figure 2a), our focus is on the 95th percentile properties. Not surprisingly, Figures 2b and 2c suggest that DCCs have larger vertical velocity thanshallower convection, while vertical velocity estimates tend to peak at the upper parts of the clouds. Bothof these characteristics are consistent with our understanding of buoyancy distribution within convective
updrafts (ML16). Here, Figures 2b and 2c show that these convective vertical velocity tendencies compare
favorably between the ground-based RWP observations and proposed satellite estimates, although the absolute values are slightly different. The RWP estimates also appear more sensitive to the frequent cumulus
congestus mode found in the Amazon.
In Figure 3a, we plot a comparison for retrievals of domain-mean convective mass flux profile, with theshaded region representing the range of retrieval variation as a result of varying the grid box for collecting
satellite data around the ARM site from 0.25° to 0.75° (solid curve is for 0.5° box). Figure 3a suggests that the
RWP and satellite-estimated convective mass fluxes are broadly comparable in the lower and middle troposphere (<6 km), but show some differences in the upper troposphere. Unlike vertical velocity that increases
with height, convective mass flux peaks in the lower to middle troposphere because it is largely modulated
by fractional coverage of convective clouds ( M c   wc ). This change in peak is associated with convective
cloud fraction ( ) that tends to decrease with height due to the more abundant presence (fractional) ofshallow convection and cumulus congestus. To further investigate the roles played by different constituentsof convective mass flux, we examine the variations of wc ,  , and Mc between the traditional Amazonian wet(December to March) and dry (June to September) regimes, following Giangrande et al. (2016) and D. Wanget al. (2019). Figures 3b–3d together suggest that convective vertical velocity properties are similar between
the wet and dry season (slightly larger for the dry season in the upper troposphere), but convective massflux is about twice as large during the wet season, because variation of convective mass flux is primarily
controlled by that of convective cloud coverage, with the mean vertical velocity playing a secondary role.
Similar findings were also reported in previous studies by Kumar et al. (2015), Giangrande et al. (2016),
and Masunaga and Luo (2016). Most of the discrepancy between satellite and ground-based estimate ofconvective (updraft) mass flux in Figure 3a can be attributed to the difference in convective cloud coveragedue to different sampling interpretations. Specifically, the proposed satellite retrievals only consider a stringent (thus narrow) convective core definition, whereas the RWP mass flux profile estimates allow a wider
convective area having vertical velocity > 1.5 ms−1. This difference is especially important in the upper troposphere, where some periphery core to anvil regions are included in RWP area fractions. This explains why
most of the satellite and RWP estimate differences are located in the upper troposphere. These comparison
results point to the importance for properly accounting for the fractional coverage of convective clouds andplatform sensitivity therein, as well as updraft fraction as a function of height when retrieving mass flux
profiles at GCM grid scale for model evaluation.

4. Global Patterns of Convective Vertical Velocity
By applying the proposed satellite retrievals to the larger global data set, one could investigate general patterns related to the structures and distributions of convective vertical velocity and mass flux. Here, we perform the standard k-means cluster analysis (e.g., Anderberg, 1973) on 2 years of wc profile data globally. As

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Figure 3. (a) Mean convective mass flux (M c   wc) profiles estimated from the RWP observations (black) and satellite estimates (red). The shaded region
represents the range of retrieval variation as a result of varying the grid box for collecting satellite data around the ARM site. See texts for details. (b) Satellitederived convective vertical velocity (wc) for the Amazonian wet (blue) and dry (red) regimes. Solid lines represent the mean profiles, and crosses represent the95th percentiles. (c) Satellite-derived convective area fraction ( ) for the Amazonian wet (blue) and dry (red) regimes. The shaded region represents the same
variation associated with grid box size as in (a). (d) Same as (c), but for convective mass flux (Mc).

described in Section 3, satellite data are collected and analyzed for 0.5° × 0.5° grid boxes around the ARM
MAO site providing measurements at a spatial scale that could be evaluated against RWP concepts, butalso aligning with basic GCM-driven evaluation considerations. Following this, we divide the whole globe
within the latitude range of 45°S-45°N into 0.5° × 0.5° grids (higher latitudes are excluded in this study because convection is less frequent there), and in each grid, the retrieved convective vertical velocity profiles
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Figure 4. Centroids of the three clusters in terms of height-wc histograms. The contour value refers to the frequency ofoccurrence for each height level and the white line shows the mean wc values (left). Geographical distribution of theseclusters. Black color at each 0.5° × 0.5° grid means the grid is associated with the corresponding cluster (right).

are compiled in a height-wc histogram to summarize the characteristics of their vertical structures, similar
to the contoured frequency by altitude diagram by Yuter and Houze (1995). The k-mean cluster analysis isperformed on these height-wc histograms.
Three clusters emerge from the k-means analysis (sensitivity tests show that k = 3 is the optimal choice).
Figure 4 shows the centroids of the clusters in terms of height-wc histograms and the corresponding geographical distributions (note that each 0.5° × 0.5° grid is associated with only one of the three clusters). The
three clusters are distinctly separated geographically: the first cluster corresponds to convection over tropical land and the second to convection over tropical ocean. The third cluster is mostly associated with convection in the extra-tropics, some of which may be embedded in extratropical cyclones (Jeyaratnam et al.,2020). The height-wc histograms clearly show the associated differences in vertical structures: 1) Convectionin the tropics is generally deeper than the extratropical convection, due to higher tropopause in the tropics.
2) Tropical land convection tends to be more intense and more variable than the oceanic counterpart. For
example, the height-wc histograms in Figure 4 shows that in the upper troposphere, the mean value for wc isabout 50% larger over land (Cluster 1) than over ocean (Cluster 2), and the standard deviation is more than
4 times as large over land (Cluster 1) as over ocean (Cluster 2).
These regional differences in convective characters are not entirely unexpected, as similar characteristics
have been documented in a number of previous studies. For example, tropical land convection containing stronger updrafts than the oceanic counterpart has been observed by both cloud-penetrating aircraft(Lucas et al., 1994) and airborne Doppler radars (Heymsfield et al., 2010). Similarly, TRMM and CloudSat radar echo-top heights also indirectly point to this potential land-ocean contrast (e.g., Liu et al., 2007;
Takahashi & Luo, 2012; Takahashi et al., 2017). Nevertheless, it is remarkable to see these bulk differences show up unambiguously and globally in extended satellite estimates of convective vertical velocity. Itshould be noted that no artificial or external rule is imposed on our analysis to trigger the land-ocean andtropics-midlatitude separations. They are entirely the result of an untrained, statistical analysis. Moreover,running a similar k-means cluster analysis on the ambient sounding data does not produce such a clear
land-ocean separation (not shown). These results clearly point to the fundamental nature of these regional
differences in convective cloud characters. Furthermore, our analyses suggest that a Bayesian combinationof satellite observations, the ambient sounding, and a plume model—a key feature of our satellite retrieval
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method—add values to the input data sources toward a more complete characterization of the structure anddistribution of convective vertical velocity.

5. Summary and Discussions
Convective vertical velocity (wc) and mass flux (Mc) lie at the heart of GCM cumulus parameterizations, yet
few observations of these critical parameters are available at this time. To improve this situation in a manner
amenable to global scales, ML16 developed a novel method using space-borne measurements, aided by aplume model driven by the ambient sounding, to estimate profiles of wc and Mc. This current study is a follow on to ML16, with the primary purposes being: 1) to compare ML16 satellite estimates of convective wc
and Mc with ground-based RWP observations made by the DOE ARM program and 2) to apply ML16 method to global scales to identify salient patterns related to the structure and global distributions of convective
vertical velocity. The main findings are as follows:
1. Satellite and RWP estimates show the general trend that convective vertical velocity increases withheight. Satellite-estimated vertical velocity is slightly stronger than that retrieved by RWPs when comparing median profile behaviors, with this difference attributed to differences in the definitions for allowable convective regions. When compared by vertical velocity properties at the 95th percentile levels,
such systematic difference is no longer as evident between satellite and RWP estimates, and there is
generally solid agreement between these estimates.
2. RWP-derived and satellite-estimated convective mass fluxes are broadly comparable in the lower andmiddle troposphere, but show some differences in the upper troposphere. Analysis of different constituents of convective mass flux shows that Mc is primarily controlled by fractional coverage of convective
clouds, with the mean vertical velocity playing a secondary role, confirming findings from several recent
studies.
3. K-means cluster analysis of multiple years of global (45°S-45°N) convective vertical velocity data showsthat convective characteristics are distinctly different among extratropical convection, tropical land convection, and tropical oceanic convection. Generally, tropical convection is deeper, while tropical convection over land is more intense and variable than the oceanic counterpart.
Developing global observations of convective vertical velocity and mass flux is critical to understanding thecomplicated nature of convective dynamics and to evaluating their representations in GCMs. ML16 andthis current study represent a first step toward this goal. Comparisons with ground-based observations aremostly positive, although there are discrepancies due to differences in observational methods and samplingstrategy. A simple clustering analysis leads to valuable insights into fundamental differences in convective
characters in different environments. Our ongoing work seeks to use the newly developed data to evaluate
GCM cumulus parameterizations.

Data Availability Statement
Acknowledgments
This work was mainly supported by
the following NASA grants awarded to
CUNY under 80NSSC17K0197, 80NSSC18K1600, and 80NSSC20K0089. S. E.
Giangrande and D. Wang are supported
by U.S. DOE contract DE-SC0012704.
The publisher by accepting the paper
for publication acknowledges that the
U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide
license to publish or reproduce thepublished form of this paper or allow
others to do so, for U.S. Government
purposes. The authors would like toacknowledge fruitful discussions with
Drs. W. B. Rossow, A. D. Del Genio, M.
P. Jensen, and G. Elsaesser.

JEYARATNAM ET AL.

CloudSat and DOE ARM data are publicly available for all researchers and can be obtained at http://www.
cloudsat.cira.colostate.edu/ and https://arm.gov/, respectively.

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