The microphysical contributions to and evolution of latent heating profiles in two MC3E MCSs: Latent heating profiles in two MC3E MCSs

Abstract

The shapes and magnitudes of latent heating profiles have been shown to be different within the convective and stratiform regions of mesoscale convective systems (MCSs). Properly representing these distinctions has significant implications for the atmospheric responses to latent heating on various scales. This study details (1) the microphysical process contributions to latent heating profiles within MCS convective, stratiform, and anvil regions and (2) the time evolution of these profiles throughout the MCS lifetime, using cloud-resolving model simulations. Simulations of two MCS events that occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E) are conducted. Several features of the simulated MCSs are compared to a suite of observations obtained during the MC3E field campaign, and it is concluded that the simulations reasonably reproduce the MCS events. The simulations show that condensation and deposition are the primary contributors to MCS latent warming, as compared to riming and nucleation processes. In terms of MCS latent cooling, sublimation, melting, and evaporation all play significant roles. It is evident that throughout the MCS lifecycle, convective regions demonstrate an approximately linear decrease in the magnitudes of latent heating rates, while latent heating within stratiform regions is associated with transitions between MCS flow regimes. Such information regarding the temporal evolution of latent heating within convective and stratiform MCS regions could be useful in developing parameterizations representing convective organization.

Full text

The microphysical contributions to and evolution
of latent heating profiles in two MC3E MCSs
P. J. Marinescu
1
, S. C. van den Heever
1
, S. M. Saleeby
1
, and S. M. Kreidenweis
1
1
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
Abstract The shapes and magnitudes of latent heating profiles have been shown to be different within
the convective and stratiform regions of mesoscale convective systems (MCSs). Properly representing
these distinctions has significant implications for the atmospheric responses to latent heating on various
scales. This study details (1) the microphysical process contributions to latent heating profiles within MCS
convective, stratiform, and anvil regions and (2) the time evolution of these profiles throughout the MCS
lifetime, using cloud-resolving model simulations. Simulations of two MCS events that occurred during the
Midlatitude Continental Convective Clouds Experiment (MC3E) are conducted. Several features of the
simulated MCSs are compared to a suite of observations obtained during the MC3E field campaign, and it is
concluded that the simulations reasonably reproduce the MCS events. The simulations show that
condensation and deposition are the primary contributors to MCS latent warming, as compared to riming
and nucleation processes. In terms of MCS latent cooling, sublimation, melting, and evaporation all play
significant roles. It is evident that throughout the MCS lifecycle, convective regions demonstrate an
approximately linear decrease in the magnitudes of latent heating rates, while latent heating within
stratiform regions is associated with transitions between MCS flow regimes. Such information regarding the
temporal evolution of latent heating within convective and stratiform MCS regions could be useful in
developing parameterizations representing convective organization.
1. Introduction
The vertical structure of latent heating within midlatitude, continental mesoscale convective systems (MCSs)
has been shown to vary between convective and stratiform regions by both observational [Kuo and Anthes,
1984; Gallus and Johnson, 1991] and modeling [Tao et al., 1993] studies. Convective regions have latent
warming (i.e., positive latent heating) throughout the majority of the vertical profile and more intense latent
warming rates than stratiform regions. Stratiform regions tend to have latent warming above and latent
cooling (i.e., negative latent heating) below a midtropospheric cloud base [Houze, 2004, Figure 4a].
The distinction between the shape and magnitude of midlatitude, continental MCS convective and stratiform
latent heating profiles has important implications over a range of temporal and spatial scales. For example,
on the synoptic scale, the vertical location and magnitude of latent heating can play a significant role in
the enhancement of upper tropospheric jet winds [Wolf and Johnson, 1995; Hamilton et al., 1998], the devel-
opment of mesoscale convective vortices (MCVs) that can persist for days and generate new convection
[Zhang and Fritsch, 1987; Rogers and Fritsch, 2001], and the formation and propagation of synoptic-scale
troughs that can impact downstream weather forecasting [Stensrud and Anderson, 2001; Rodwell et al., 2013].
Idealized numerical experiments have shown that latent heating and its induced buoyancy perturbations cre-
ate gravity waves that propagate outward from their source and force regions of enhanced rising and sinking
motions [Bretherton and Smolarkiewicz, 1989]. Nicholls et al. [1991] further reported that the shape and
magnitude of the vertical profiles of heating in both convective and stratiform regions impact gravity wave
propagation speeds, as well as the environmental response to the gravity waves (i.e., perturbations to hori-
zontal wind, vertical wind, pressure, and buoyancy). These changes to the mesoscale environment can con-
sequently alter MCS behavior. For example, using a numerical simulation, Adams-Selin and Johnson [2013]
demonstrated that latent heating-induced gravity waves resulted in increased pressure ahead of an MCS con-
vective line, which assisted in forcing a bowing region. The vertical structure and magnitude of latent heating
are also critical to a variety of internal MCS processes, including the development of the mesoscale circulation
within the MCS [e.g., Raymond and Jiang, 1990; Pandya and Durran, 1996] and the propagation of the MCS
[e.g., Raymond, 1984; Cram et al., 1992].
MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7913
PUBLICATION
S
Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2016JD024762
Key Points:
•Cloud-resolving model simulations of
two MC3E MCS events are used to
assess latent heating
•Microphysical contributions to latent
heating are calculated for MCS regions
•Vertical profiles of latent heating
change significantly with MCS
lifecycle stage
Correspondence to:
P. J. Marinescu,
peter.marinescu@colostate.edu
Citation:
Marinescu, P. J., S. C. van den Heever,
S. M. Saleeby, and S. M. Kreidenweis
(2016), The microphysical contributions
to and evolution of latent heating
profiles in two MC3E MCSs, J. Geophys.
Res. Atmos.,121, 7913–7935,
doi:10.1002/2016JD024762.
Received 5 JAN 2016
Accepted 17 JUN 2016
Accepted article online 20 JUN 2016
Published online 9 JUL 2016
©2016. American Geophysical Union.
All Rights Reserved.

Many of these latent-heating-dependent features are poorly reproduced in large-scale models that do not
explicitly resolve the cloud processes that impact latent heating [e.g., Hartmann et al., 1984; Davis et al.,
2002; Schumacher et al., 2004]. While several convective parameterizations have been developed to account
for the different processes associated with MCS convective and stratiform regions [Donner, 1993; Alexander
and Cotton, 1998; Donner et al., 2001], few have been incorporated into large-scale models. Further, these
parameterizations do not incorporate the time evolution of MCS processes, which has been argued to be
important for the improvement of parameterization results [Futyan and Del Genio, 2007; Del Genio
et al., 2012].
One obstruction to the understanding and parameterization of MCS latent heating and its time evolution is
the inability of current observing platforms to directly obtain latent heating rates, although observations,
such as those from rawinsondes and radars, have been used in conjunction with simplified assumptions to
diagnose estimated heating rates [Yanai et al., 1973]. Using this diagnostic analysis, early studies disen-
tangled MCS stratiform latent heating from its convective counterpart in tropical MCSs [Leary and Houze,
1979; Johnson and Young, 1983] and midlatitude, continental MCSs [Kuo and Anthes, 1984; Gallus and
Johnson, 1991; Braun and Houze, 1996]. These studies, and others that have focused on the kinematics of
MCSs [e.g., Smull and Houze, 1985], have provided numerous insights into MCS processes and corroborated
the general shapes of the idealized convective and stratiform latent heating vertical profiles, as shown in
Houze [2004]. Collectively, they have shown that convective region latent warming is primarily driven by
condensational growth within updrafts and that it peaks in the middle-to-upper troposphere. Hydrometeors
are advected from the convective regions into the developing stratiform regions, where depositional growth
onto ice hydrometeors dominates latent heating production above the stratiform cloud base. As ice hydrome-
teors precipitate, sublimation, evaporation, and melting all appear to play important roles in creating a latent
cooling peak below the stratiform cloud base.
While a few of these studies were able to diagnose estimated latent heating rates at a few times during a spe-
cific MCS event [Gallus and Johnson, 1991; Braun and Houze, 1996], they were unable to fully resolve the
evolution of MCS latent heating. Furthermore, estimates of convective region latent heating from many
observation-based studies are susceptible to aliasing biases, as the spatial sampling of observations is typi-
cally too coarse for the calculation of the finer scale processes within the convective region, although some
of these issues have been resolved with increased radar observations [Braun and Houze, 1996]. For these rea-
sons, the time evolution of MCS latent heating may currently best be studied using cloud-resolving model
(CRM) simulations, providing that the simulations can reasonably reproduce MCS events. Furthermore,
should the simulations be reasonably accurate, CRMs can then provide details regarding the microphysical
processes related to latent heating. However, relatively few modeling studies [e.g., Tao et al., 1993; Caniaux
et al., 1994] have focused on the evolution of latent heating within MCS convective and stratiform regions.
Satellites have also been used to estimate latent heating rates within MCSs. The Tropical Rainfall Measuring
Mission (TRMM) [Simpson et al., 1988] increased the spatial and temporal extents of latent heating estimation
in tropical regions, and the Global Precipitation Measurement [Hou et al., 2014] is now extending these esti-
mates to the midlatitudes. Many TRMM latent heating retrieval algorithms have been developed, all of which
are rooted in data from CRM simulations [Tao et al., 2006; Shige et al., 2009]. Advancements in computing
power have allowed recent CRM simulations to better reproduce many features of MCSs, due to the use of
more sophisticated microphysics parameterizations [Morrison et al., 2009; Li et al., 2009; Adams-Selin et al.,
2013; Lang et al., 2014]. A renewed and enhanced focus on understanding CRM simulations of MCS latent
heating would thus be useful for algorithm improvements in satellite applications.
The goals of this study are therefore (1) to assess the microphysical process contributions to latent heating
profiles within MCS regions and (2) to evaluate the time evolution of latent heating within MCS regions.
These goals are accomplished through conducting CRM simulations of two MCS events that occurred during
the Midlatitude Continental Convective Clouds Experiment (MC3E) [Jensen et al., 2016]. From 22 April through
6 June 2011, the National Aeronautics and Space Administration and the Department of Energy (DOE) colla-
borated on MC3E, which transpired in the Southern Great Plains of the United States. One of the major goals
of the field project was to provide details of the physical processes that drive convective clouds [Jensen et al.,
2016]. Two of the best-sampled events occurred on 20 May and 23–24 May 2011, both of which involved an
MCS with a leading convective line and trailing stratiform precipitation region (LLTS), the most common MCS
Journal of Geophysical Research: Atmospheres 10.1002/2016JD024762
MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7914

type in the central United States [Parker
and Johnson, 2000]. These two MC3E
MCS events have been the focus of
numerous studies on various aspects of
convection [Tao et al., 2013; Lang et al.,
2014; Fan et al., 2015; Liu et al., 2015].
2. Data
2.1. Simulations
The 20 May and 23–24 May MCS events
were simulated with the three-
dimensional, nonhydrostatic Regional
Atmospheric Modeling System (RAMS)
[Cotton et al., 2003; Saleeby and van den
Heever, 2013]. RAMS has successfully
simulated the microphysical and dyna-
mical features of MCSs in many prior
studies [e.g., Olsson and Cotton, 1997;
Alexander and Cotton, 1998; Cheng and
Cotton, 2004; Seigel and van den Heever, 2013; Seigel et al., 2013]. The RAMS microphysics scheme incorpo-
rates a bin-emulating, two-moment bulk cloud microphysical parameterization that tracks three liquid hydro-
meteor (cloud, drizzle, and rain) and five ice hydrometeor (graupel, hail, pristine ice, snow, and aggregates)
species [Walko et al., 1995; Meyers et al., 1997; Saleeby and Cotton, 2004].
In order to appropriately account for the synoptic conditions while still being able to simulate cloud-scale
processes, the simulations were set up with three nested grids with horizontal grid spacings of 30km,
6.0 km, and 1.2km (Figure 1). All of the simulation analyses were performed over a subset of grid 3 (“analysis
domain”in Figure 1), which was approximately bounded by 33°N, 38°N, 101°W, and 90°W, as the overwhelm-
ing majority of both MCS systems fell within this bounding box (compare Figure 1 with Figures 3 and 5). The
model domain was constructed with 60 vertical levels that were spaced 75 m apart near the surface and were
stretched to 500 m by 4 km above ground level (agl), at which point the vertical spacing remained constant to
the model top at 22 km agl.
Both simulated events were initialized several hours before the observed initiation of the convective cells that
grew upscale into their respective MCSs. The 20 May simulation was initialized with the Global Data
Assimilation System (GDAS-FNL) reanalysis data from 20 May 2011 00Z. Due to weaker synoptic forcing asso-
ciated with the 23–24 May event, as well as the presence of mesoscale features that were essential to the
development of the MCS, the higher-resolution Rapid Update Cycle (RUC) model analysis data from 23
May 2011 16:00 UTC were used to initialize the 23–24 May simulation. The GDAS-FNL and RUC analysis data
also provided the lateral boundary conditions for the 20 May and 23–24 May simulations, respectively. The
simulations were initialized with horizontally homogeneous aerosol profiles that were based on surface mea-
surements from a Droplet Measurement Technologies single-column cloud condensation nucleus (CCN)
counter [Roberts and Nenes, 2005] at the DOE’s Atmospheric Radiation Measurement Program’s Southern
Great Plains site (ARM-SGP; 36.6°N, 97.5°W) at the onset of the 20 May and 23–24 May events. These profiles
were formulated with aerosol particle number concentrations of 2000 cm
3
at the surface, and this concen-
tration was exponentially decreased with a scale height of 7 km to the model top. In the model, aerosol par-
ticles are represented using a lognormal distribution that was specified with mean diameter (d) and
geometric standard deviation (σ
g
). Aerosol particles are also prescribed with a solubility fraction (ε), which
is a measure of their hygroscopicity. For these simulations, these values were d= 120 nm, σ
g
= 1.8, and
ε= 0.2 and were based on surface aerosol measurements taken at the ARM-SGP during MC3E. These aerosol
particles can serve as cloud condensation nuclei (CCN), and the number of particles activated is based on
predicted environmental conditions [Saleeby and van den Heever, 2013]. For the 20 May event, this aerosol
initialization (2000 particles cm
3
at the surface) differs from simulations in Fan et al. [2015], which used much
cleaner surface conditions (320 particles cm
3
), as their initialization data were based on CCN observations
Figure 1. Map of the nested grids used in the RAMS simulations. Grids 1,
2, and 3 have horizontal grid spacing of 30 km, 6km, and 1.2 km,
respectively. A subset of Grid 3 (“Analysis Domain”) is used for all analyses
presented herein.
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MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7915

taken after the influence of convection and preci-
pitation at the ARM-SGP site. Specified and
tracked separately from CCN in these simulations,
ice nucleating particles were prescribed with a
vertical profile (Figure 2) that was also applied in
a horizontally homogenous manner throughout
the model domain and was based on MC3E air-
craft observations of number concentrations of
particles with diameters larger than 500 nm.
RAMS prognoses the mixing ratio and number
concentration of all hydrometeor species and
provides output of the rates of microphysical pro-
cesses (e.g., melting, riming, and nucleation) and
latent heating [Saleeby and van den Heever,
2013]. These microphysical processes are critical
for understanding the evolution of latent heating
throughout the two simulations. An analysis of
the MCSs encompasses a 12 h period (“analysis
period”) with model output every 5 min, beginning with the initial convective cell development, which occurs
at approximately 03:00 UTC on 20 May and 21:00 UTC on 23 May for the 20 May and 23–24 May events,
respectively. A summary of the simulation configurations is provided in Table 1.
2.2. Observations
A combination of satellite and surface-based measurements was used to ensure that the RAMS simulations
produced MCS events similar to those observed during the MC3E field campaign. Four MCS characteristics
were selected for model evaluation and comparison: radar signatures, precipitation, convective updraft
strength, and MCS convective, stratiform, and anvil cloud areas.
2.2.1. Radar Signatures
The National Weather Service Next Generation Radar Network (NEXRAD) reflectivity was used to compare the
evolution and structure of the simulated and observed MCS events. RAMS data were converted to radar
reflectivity using QuickBeam, a radar reflectivity simulator, that was interfaced with the RAMS output
[Haynes et al., 2007].
2.2.2. Precipitation
The National Centers for Environmental Prediction’s National Stage IV QPE Product (ST4) was used for the pre-
cipitation validation. This data set uses both radar and gauge data to produce quantitative precipitation
amounts on an hourly basis across the United States [Lin and Mitchell, 2005]. This data set was provided on
a 4 km, polar stereographic grid.
2.2.3. Convective Updraft Strength
During MC3E, multiple radars were strategically placed in order to retrieve information about vertical
velocities (W) within convective systems [Jensen et al., 2016]. After applying corrections for attenuation,
Figure 2. Profiles of ice nucleating particles (INP). The black
line represents the profile of INP used to initialize the model
simulations. The blue lines represent concentrations of aerosol
particles with diameters greater than 500 μmasmeasuredby
the University of North Dakota’s Citation aircraft during MC3E
on three separate flight days: 27 May, 01 June, and 02 June
2011.
Table 1. Summary of RAMS Configurations and Options
Model Aspect Setting
Grid Arakawa C grid [Mesinger and Arakawa, 1976]
Three nested grids: Grid 1: Δx=Δy= 30 km, Δt= 30 s; Grid 2: Δx=Δy= 6 km, Δt= 7.5 s; Grid 3: Δx=Δy= 1.2 km, Δt= 3.8 s
Δz= variable (details provided in section 2.1); Model top at ~22 km agl
Initialization GDAS-FNL reanalysis data for 20 May event and RUC model analysis data for 23–24 May event
Aerosol initialization described in section 2.1 and based on MC3E data
Microphysics scheme Two-moment bulk microphysics for eight hydrometeor species [Meyers et al., 1997; Saleeby and Cotton, 2004]
Boundary conditions Radiative lateral boundary [Klemp and Wilhelmson, 1978]
Cumulus parameterization Kain-Fritsch scheme [Kain and Fritsch, 1993] only on Grid 1
Radiation scheme Harrington [1997]
Turbulence scheme Horizontal diffusion based on Smagorinsky [1963]; Vertical diffusion based on Mellor and Yamada [1974]
Land-surface model LEAF-3 [Walko et al., 2000]
Journal of Geophysical Research: Atmospheres 10.1002/2016JD024762
MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7916

multi-Doppler techniques were used to determine Wusing the variational method as described in Dolan and
Rutledge [2010]. Depending on availability, this analysis was conducted with data from three or four radars,
including two X-band Scanning ARM Precipitation Radars (SAPR), one C-band SAPR, and a National
Weather Service WSR-88D radar (KVNX). Errors in the calculated Wused in this study are estimated to be
on the order of several m s
1
[Nelson and Brown, 1987; Dolan and Rutledge, 2010; Collis et al., 2013]. Due to
the limited ranges of the radars, these data were confined to a 120 × 120 km area centered at the
ARM-SGP site. The quality-controlled data were mapped onto a 1.0 × 1.0 × 1.0 km grid at output intervals of
~5–15 min. This analysis was available between ~06:00 UTC and ~10:00 UTC on 20 May 2011 and between
~21:00 UTC and ~24:00 UTC on 23 May 2011, which are both within the 12 h analysis periods of this study.
2.2.4. MCS Cloud Regions
The observed MCSs were partitioned into convective (CONV), stratiform (STRA), and anvil (ANVL) cloud
regions. The major distinction between STRA and ANVL cloud regions is that STRA cloud regions likely have
precipitation at the surface. This observation-based MCS partitioning was done in a manner similar to that
used in Feng et al. [2011] (herein F11), which incorporates both the NEXRAD network data and the
Geostationary Operational Environmental Satellite (GOES) data. Within the F11 algorithm, CONV and STRA
regions were separated using a modified version of the methods used in Steiner et al. [1995], which were
based on radar reflectivity. The reflectivity threshold used in this study was 45 dBZ, and the algorithm was
Figure 3. Radar reflectivity and convective, stratiform, and anvil areas for the 20 May event at three times: 06:00 UTC, 10:00
UTC, and 14:00 UTC. (a–c) NEXRAD radar reflectivity at 2.5 km agl and (d–f) QuickBeam simulated reflectivity at 2.5 km agl
for the RAMS model. The partitioning of observations and model data into convective (purple), stratiform (green), and anvil
(brown) regions is presented. (g–i) The reflectivity-based (RB) partitioning for the observations. (j–l) The RB partitioning for
RAMS and (m–o) the physical threshold (PT) partitioning for RAMS. Details on the partitioning methods are provided in
section 2.2.4.
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MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7917

implemented at 2.5 km altitude, which is well
below the 0°C isotherm level (~4 km agl). The
peakedness criterion was not included here to
simplify the analysis, although testing indicated
that including this criterion did not significantly
impact the results of the partitioning. This metho-
dology will be termed as the reflectivity-based
(RB) partitioning in this study.
In order to separate the simulation data into similar
CONV and STRA regions as was done for the obser-
vations, the model data were converted to radar
reflectivity using the QuickBeam radar simulator
and were analyzed with the RB partitioning. Using
the same partitioning method allows for direct
comparison between the simulations and observa-
tions. However, a second convective-stratiform
partitioning algorithm was also used. While the
RB partitioning is solely based on the reflectivity
values at a specific altitude, this alternative parti-
tioning method takes into account precipitation
rates, vertical velocities, and cloud mixing ratios
throughout the cloud system and is similar to
methods used in many other studies [Churchill and Houze, 1984; Tao et al., 1993; Alexander and Cotton,1998;
Lang et al., 2003]. This alternative partitioning will be termed physical threshold (PT) partitioning. In the PT par-
titioning, a convective core was defined as any model column where the instantaneous, surface precipitation
rate exceeds twice the background, surface precipitation rate, which was calculated as the average over a
20 km
2
area surrounding the column. Convective cores and their surrounding grid points were classified as
CONV. Model columns that also met any of the following criteria were also classified as convective: (1) an instan-
taneous precipitation rate greater than 25 mmh
1
, (2) the absolute value of vertical velocity below the melting
level exc eeding 3 m s
1
, (3) the absolute value of vertical velocity above the melting level exceeding 5 ms
1
,or
(4) cloud mixing ratios below the melting level greater than 0.4g kg
1
. Because this fourth criterion can some-
times misidentify thick, low-level stratiform clouds as CONV, it was specified that to be classified as CONV,
model columns must have cloud tops above 6 km. All other columns with instantaneous surface precipitation
rates greater than 0. 01 mm h
1
were classified as STRA.
ANVL regions were also determined in a manner similar to F11. In F11, GOES data were used to capture the full
anvil area by identifying locations with cloud tops greater than 6 km agl and cloud top infrared (IR) brightness
temperature less than 270K. The GOES cloud top, cloud base, and IR data were calculated using the visible-
infrared-solar infrared-split window technique [Minnis et al., 2002]. In the RAMS simulations, the same thresh-
olds were applied, except that cloud top temperature was used as a proxy for cloud top infrared brightness
temperature. Cloud tops were determined as the highest level within a grid column with condensate mixing
ratios greater than 0.1 g kg
1
. The condensate mixing ratio and temperature thresholds were tested among
a range of reasonable values, and the total computed ANVL area was largely insensitive to threshold changes.
ANVL regions were classified as regions that met these criteria and were not classified as CONV or STRA.
3. Simulation-Observation Comparisons
3.1. Radar and Cloud Region Evolution
3.1.1. 20 May Event
Figure 3 demonstrates both radar reflectivity and cloud-region partitioning for both the simulations and
observations at three different times during the 20 May event. Both the RB and PT partitioning methods
for the model simulation are shown. Early on 20 May 2011, a linear MCS traversed eastward across southern
Kansas, Oklahoma, and northern Texas. Convection initiated around 03:00 UTC (not shown), along a dryline in
western Oklahoma and Texas, and by 06:00 UTC (Figure 3, left column) quickly developed into a line in the
Figure 4. Percentage of precipitating area that is convective
(purple) and stratiform (green) for the 20 May event. The
observational data, which uses RB partitioning, are repre-
sented by the solid lines; the simulation data using RB
partitioning are represented by the dashed lines; and the
simulation data using PT partitioning are represented by the
dotted lines.
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MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7918

southwestern region of the analysis domain. The RAMS simulations produced enhanced convection in the
northern half of the analysis domain during the first half of the analysis period, which was due to instability
that was not present in the observations. In the observations, convection in central Oklahoma during and
prior to the model initialization and spin-up stabilized the environment in northern and central Oklahoma
and also created a larger anvil region that extended further eastward in the observations (Figures 3g and
3h). More importantly, RAMS correctly developed a linear convective line in the southwest part of the analysis
domain, and it was this feature that grew upscale into the LLTS MCS. Around 10:00 UTC (Figure 3, middle
column), the leading convective line assumed a bowing structure that was also captured in the RAMS simula-
tions. Also, both the observations and RAMS show a large trailing stratiform region behind the leading
convective line, although this region is less extensive in the RAMS simulation. The MCS continued to move
eastward across OK. Around 14:00 UTC (Figure 3, right column), while the convective line looked less
organized and intense, especially in the RAMS simulation, new convective cells began to develop several
hundred kilometers ahead of the leading convective line. Around 18:00 UTC, a second linear MCS developed
in southern Oklahoma and northern Texas, immediately behind the MCS described above. This second MCS
quickly grew upscale and merged with the original MCS in the hours following 18:00 UTC (not shown). For
this reason, the decaying stage of this MCS event was not easily assessed in both the observational and
simulation data sets.
Figure 5. Radar reflectivity and convective, stratiform, and anvil areas for the 23–24 May event at three times: 00:00 UTC,
04:00 UTC, and 07:00 UTC. (a–c) NEXRAD radar reflectivity at 2.5 km agl and (d–f) represent QuickBeam simulated reflec-
tivity at 2.5 km agl for the RAMS model. The partitioning of observations and model data into convective (purple), stratiform
(green), and anvil (brown) regions is presented. (g–i) The reflectivity-based (RB) partitioning for the observations. (j–l) The
RB partitioning for RAMS and (m–o) the physical threshold (PT) partitioning for RAMS. Details on the partitioning methods
are provided in section 2.2.4.
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MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7919

To quantify the evolution described above,
Figure 4 shows the percentage of the total precipi-
tation area (i.e., convective and stratiform area)
that was either convective or stratiform as a func-
tion of time. Throughout the 12 h analysis period,
RAMS overpredicted the convective area and
underpredicted the stratiform area for the first
9 h of the analysis period by an average of 8%
using the same RB partitioning as the observa-
tions. The PT partitioning of the RAMS data shows
an average difference of 22% in the convective
and stratiform areas as compared to the observa-
tions over the 12 h period, and these larger differ-
ences can be attributed to the disparities in the
partitioning methods [Lang et al., 2003].
3.1.2. 23–24 May Event
Around 21:00 UTC on 23 May 2011, individual
thunderstorms began forming in west-central
Oklahoma along a dryline. At these early stages
of the 23–24 May event, a separate MCS that origi-
nated the prior night was present over southern
Missouri, northern Arkansas, and extreme north-
east Oklahoma and southeast Kansas (Figure 5, left column). RAMS reproduced both regions of convection
in their respective locations. By 01:00 UTC on 24 May 2011, the northern section of the dryline storms merged
with the preexisting MCS in the southern MO region, and the merged convective and stratiform precipitation
began to propagate southeastward through Oklahoma and Arkansas. By 04:00 UTC, a LLTS MCS had devel-
oped, with the stratiform region extending hundreds of kilometers north and northeast of the convective line
(Figure 5, middle column). Although the trailing stratiform region directly behind the convective line was
underpredicted in the model, RAMS captured the asymmetric, northeastward extension of stratiform rainfall.
At this time, the stratiform regions was underestimated in RAMS by ~12% (using the RB partitioning), espe-
cially directly behind the leading convective line (Figure 6). Similar to the 20 May event, this underestimation
of stratiform area and overestimation of convective area were present throughout the analysis period, with
an average difference of ~9% throughout the 12 h analysis period (Figure 6).
The MCS continued its southeastward propagation until ~06:00 UTC. Around this time, the convective line
began to dissipate in central AR, and by 07:00 UTC (Figure 5, right column), mostly light stratiform precipita-
tion was present in central and eastern AR in both the simulation and observation data. The timing of this
decay in convective area (and subsequent increase in percentage of the precipitation area that was strati-
form) occurred between 05:00 and 06:00 UTC in both the observations and simulation (Figure 6). For this
event, the majority of the lifetime of the MCS was simulated within the analysis domain during the analysis
period. Descriptions of the synoptic precursors and additional mesoscale features of both of these MCS
events can be found in a variety of other studies [Tao et al., 2013; Lang et al., 2014; Fan et al., 2015].
3.2. Convective Updraft Strength
For comparative purposes, model vertical velocity (W) wasgridded to the same vertical grid spacing as theradar
data using linear interpolation. It should be noted that the radar data were not available below 1 km agl. Since
the convection in the simulations of both MCS events developedin a fashion (i.e., structure, timing, and location)
similar to observations, model data were confined to the same spatial area and temporal period as the available
radar data. Convective updraft regions were identified as all regions where Wwas greater than 1 m s
1
.Data
from all defined convective updraft regions were binned by altitude, and the 50th, 75th, and 95th percentiles
of Wwere then calculated at each altitude level (Figure 7). These methods are similar to other studies that have
compared simulated MCS vertical velocities to radar-derived values [Varble et al.,2014;Fan et al., 2015].
For the relatively weak convective updrafts (50th percentiles in Figure 7, blue lines), simulated vertical velocities
were mostly within 0.3 m s
1
of the radar-derived values throughout the vertical column for both events. The
differences between the 75th percentiles of the simulated and radar-derived vertically velocities (Figure 7,
Figure 6. Percentage of precipitating area that is convective
(purple) and stratiform (green) for the 23–24 May event. The
observational data, which uses RB partitioning, are represented
by the solid lines; the simulation data using RB partitioning are
represented by the dashed lines; and the simulation data using
PT partitioning are represented by the dotted lines.
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MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7920

red lines) were within ~1 m s
1
at all loca-
tions throughout the vertical column for
both events, except between 1 and
6 km agl for the 23–24 May event, where
ameanoverpredictionbiasof~2ms
1
occurred. It is worth remembering, as sta-
ted in section 2.2.3, that observational
errors in Wmay be on the order of sev-
eral m s
1
[Nelson and Brown,1987;
Dolan and Rutledge,2010;Collis et al.,
2013], and hence, the simulation 75th
percentile Wfalls within the range of
uncertainty of the observation-based W.
For the strongest updraft velocities
(Figure 7, green lines), RAMS overpre-
dicted Wby at most 5 m s
1
for the 20
May event, again a reasonable compari-
son given the potential errors of several
ms
1
for the Doppler calculations.
RAMS does, however, overpredict Win
the 23–24 May event, which had the
more intense convection of the two
events, by at most 16 m s
1
. A similar
overprediction bias in the most intense,
convective vertical velocities has also
been observed in recent simulations of
both tropical and midlatitude MCSs
[Varble et al.,2014;Fan et al.,2015].In
summary, convective Win the RAMS
simulations was within the range of
uncertainty of observed Wfor the 20
May and 23–24 May events, except for
overprediction biases in the most intense
updrafts during the 23–24 May event.
3.3. Precipitation
For the surface precipitation compari-
sons, the RAMS simulation data were
regridded to the ST4 grid using linear
interpolation. In order to compare the
temporal and spatial evolution of preci-
pitation between the simulations and observations, Hovmöller diagrams were created within the analysis
domain and are displayed in Figure 8. For the 20 May event (Figures 8a and 8b), both the simulation and
observation data sets showed initial convective development around 20 May 06:00 UTC at 100°W (Points 1 in
Figure 8), with eastward propagation of the precipitation. Furthermore, both the model and observations
showed that the main MCS feature had its highest precipitation amounts between 10:00 UTC and 14:00
UTC between the longitudes of 98°W and 96°W, followed by a slight decrease in precipitation amounts.
RAMS also reproduced the convection forming ahead of the main convective line after 12:00 UTC (Points
2). For the 23–24 May event (Figures 8c and 8d), beginning around 22:00 UTC, RAMS reproduced the location
of precipitation associated with the convection along the dryline at 98°W (Points 3), as well as precipitation in
the Missouri/Arkansas region between 94°W and 91°W (Points 4) at these earlier times. The main MCS preci-
pitation feature formed around 00:00 UTC (Points 5) in both the model and observations and propagated
with an eastward component. To quantify the precipitation comparison, total accumulated surface precipita-
tion was summed spatially across the analysis domain and temporally over the analysis period for both the
Altitude (km)
0
2
4
6
8
10
12
14 (a) 20 May Event
W (m s-1)
0 6 12 18 24 30
Altitude (km)
0
2
4
6
8
10
12
14 (b) 23-24 May Event
Radar-50th
RAMS-50th
Radar-75th
RAMS-75th
Radar-95th
RAMS-95th
Figure 7. The 50th, 75th, and 95th percentiles of vertical velocities within
convective updrafts at each altitude level for (a) the 20 May event and (b)
the 23–24 May event. Convective updrafts include all grid points that
have updrafts greater than 1 m s
1
.
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simulation and observation data sets and compared. Percentage differences of simulated domain-
accumulated precipitation from observed values were 3.6% and +12.4% for the 20 May and 23–24 May
events, respectively. The enhanced precipitation in the simulated 23–24 May event over observations may
have been related to the overprediction of convective updraft strength in that simulation.
Some discrepancies between the simulations and the observations do appear in Figure 8. At the earliest
times, both simulations formed too much precipitation in some regions (Points 4 and 6), before becoming
more in keeping with observations once the MCSs developed. This precipitation intensity difference may
be partly due to the lack of mesoscale information within the model initialization data and simulation
spin-up, as discussed in section 3.1. Another discrepancy that can be seen in Figure 8 is the simulations’
underprediction of stratiform precipitation. In spite of these shortfalls, the simulations reproduced many of
the observed precipitation features for both events, both spatially and temporally.
Using precipitation, convective updraft strength, radar reflectivity, and MCS cloud-region partitioning, it has
been demonstrated that while there are some inconsistencies between the model output and the observa-
tions, the RAMS model was overall able to successfully reproduce many features of the two MC3E MCS events
accurately, including their general development, evolution, and propagation. Therefore, these simulations
can be used to study the microphysical processes and the resulting latent heating structure associated with
these two LLTS MCS events.
4. Regional Microphysical Process Contributions to Latent Heating
Figure 9 shows vertical profiles of temporally averaged, spatial means of latent heating rates (solid black lines)
over the CONV, STRA, and ANVL regions, as well as the entire MCS for both simulations. The PT partitioning
methods were used to separate the model data into the different MCS subregions for the latent heating ana-
lysis, since this partitioning method takes into account a wide range of physical processes throughout the
MCS, as opposed to the RB partitioning, which is based on radar reflectivity at one altitude. These temporal
averages were computed over the 12 h analysis period, which began with the initial convective cell develop-
ment for both events. The contributions to total latent heating arising from different microphysical processes
are also shown. These microphysical processes are the net deposition-sublimation associated with existing
Figure 8. Time-longitude (Hovmöller) diagrams of meridionally summed (33°–38°N) accumulated surface precipitation for
both the observations and RAMS model for (a and b) the 20 May and (c and d) 23–24 May events. Numbers represent
corresponding key features during the MCS events, which are referenced in the text.
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ice hydrometeors (D-S), net condensation-evaporation associated with existing liquid hydrometeors (C-E), cloud
droplet and ice crystal nucleation (NUC), melting of ice hydrometeors (MELT), and freezing of rain and cloud
water due to a collision with an ice hydrometeor (RIME). In this study, deposition refers to the growth of ice
hydrometeors from water vapor, exclusively. Cloud droplet nucleation refers to the activation of aerosol parti-
cles into cloud droplets based on environmental conditions. Ice crystal nucleation includes deposition freezing,
condensation freezing, and immersion freezing via the DeMott et al. [2010] temperature-dependent parameter-
ization, as well as contact freezing and homogenous freezing [Saleeby and van den Heever, 2013]. The approx-
imate cloud base heights for the convective regions (~1.2km agl for the 20 May event and ~1.8km agl for the
23–24 May event) and the freezing level heights within the MCSs (~3.7 km agl for the 20 May event and ~3.9 km
agl for the 23–24 May event) are also shown in Figure 9.
4.1. Convective (CONV) Regions
The CONV regions of both MCSs had net cooling below cloud base, which was caused by the evaporation of
rain, and net warming above cloud base through to the cloud top. Between ~4 and 8km agl, net latent heating
reached its peak levels, with mean heating rates of 25–35 K h
1
. These altitudes of peak latent heating are simi-
lar to prior observational studies of midlatitude, continental MCSs [Gallus and Johnson,1991;Braun and Houze,
1996]. Condensation was the primary driver of this warming through ~7-8km agl, above which vapor deposi-
tion onto ice played the dominant role. These simulations demonstrate that convective latentheatingresulting
from vapor deposition onto ice hydrometeors was approximately half the magnitude of the latent heating
resulting from condensational growth of liquid hydrometeors and occurred through a shallower layer.
Around 9km agl, peak net deposition-based warming and net evaporation-based cooling represents the
Wegener-Bergeron-Findeisen process, which was primarily occurring on the peripheries of convective cores.
Riming produced mean latent warming rates of only 2–5Kh
1
between 4 and 9 km agl, though locally, the
contribution of riming can be upward of 10 Kh
1
within hail cores (not shown). Nucleation had a negligible role
in CONV latent heating, except near 10km agl, where homogeneous freezing of lofted cloud droplets was the
dominant nucleation process.
Although the vertical locations of peak CONV latent heating were similar to prior observation-based, diagnostic
studies [Gallus and Johnson, 1991; Braun and Houze, 1996], the magnitudes of latent heating (both warming and
Figure 9. Vertical profiles of latent heating rates and the associated microphysical contributions. Key microphysical pro-
cesses are described in the text. Values shown are temporally averaged over the 12h analysis period and spatially aver-
aged over classified MCS regions. (a–d) The CONV, STRA, ANVL, and All MCS regions for the 20 May event. (e–h) The same
regional and temporal averages for the 23–24 May event. Approximate cloud bases for convective regions are shown with
dotted grey lines in Figures 9a and 9e. STRA and ANVL cloud bases are highly variable and are therefore not shown.
Simulation freezing levels within the MCSs are indicated by the dotted grey lines in Figures 9d and 9h.
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cooling) rates from the RAMS simulations were significantly larger than these studies, which typically showed
peak latent heating rates of several degrees per hour. This discrepancy is in part due to aliasing caused by
the inability of observational networks to resolve convective-region processes. A recent study, using higher-
resolution Doppler-based radar to estimate latent heating, obtained rates of up to 80 K h
1
in intense tropical
convective updrafts, which were in keeping with TRMM latent heatingestimates for the same system [Park and
Elsberry, 2013]. Furthermore, other recent 3-D modeling studies of MCSs have also shown convective-region
latent heating rates of over 20K h
1
[Shige et al.,2009;Adams-Selin and Johnson,2013;McGee and van den
Heever, 2014].
4.2. Stratiform (STRA) Regions
The STRA regions had net latent cooling from the surface to around 4 km agl, with evaporation, melting, and
sublimation all playing a significant role, depending on the altitude. The low-level cooling rates in Figures 9b
and 9f were similar to observation-based studies of MCSs in the tropics and midlatitudes [Leary and Houze,
1979; Gallus and Johnson, 1991] and recent detailed modeling studies of stratiform regions [Grim et al.,
2009] that all estimated cooling rates of several K h
1
. Given their larger and more uniform subcloud areas,
stratiform regions are better resolved than convective regions by observational networks.
The vertical profiles in Figures 9b and 9f demonstrate that sublimation had similar cooling rates to evapora-
tion and melting but was more strongly masked by condensational heating at the same altitude. Some recent
observations have suggested the importance of the sublimation of ice particles to latent cooling within MCS
stratiform regions, especially for those processes that occur in drier environments [McFarquhar et al., 2007;
Heymsfield et al., 2015]. Furthermore, the sublimation cooling peak near the freezing level helps to explain
the presence of peak melting ~1 km below the freezing level, as sublimation will continue to cool the hydro-
meteors and delay the onset of melting. The most intense cooling rates were present near or directly below
the freezing level, which was the case in both prior observation-based [Leary and Houze, 1979; Gallus and
Johnson, 1991] and modeling [Tao et al., 1993; Shige et al., 2009; Grim et al., 2009; McGee and van den
Heever, 2014] studies. Above the freezing level, condensation resulted in net warming from 4 to 6 km agl,
and above 6 km, deposition dominated latent heating rates. Unlike the CONV regions, the STRA latent heat-
ing rates from riming processes were negligible.
4.3. Anvil (ANVL) Regions
In the nonprecipitating ANVL regions (Figures 9c and 9g), the mean latent heating rate magnitudes were at
least 1 order of magnitude smaller than in the CONV and STRA regions. The main ANVL latent heating fea-
ture was the cooling from sublimation between ~3 and 9 km agl, which peaked around 6–7 km agl.
Therefore, there was more dissipation than growth of ice hydrometeors collectively within the ANVL
regions. The condensation-based latent heating below 5 km agl in the ANVL regions (Figures 9c and 9g)
was due to low-level cloud that formed below the forward anvil, ahead of the leading convective line
(Figures 3 and 5).
4.4. All MCS Regions
For completion, mean latent heating profiles for the entire MCS (i.e., a combination of the CONV, STRA, and
ANVL regions) are provided in Figures 9d and 9h. The entire MCS profile most closely resembles the CONV
profile due to the intensity of the microphysical processes within the CONV regions. However, it is important
to note that these simulations underpredicted the stratiform regions by ~9%, which may also be contributing
to the similarities between the CONV and All MCS profiles.
4.5. Profile Comparisons
The magnitude and vertical distribution of the microphysical processes and resulting latent heating are very
similar between the two simulations, suggesting that results from this study may be applicable to other
similarly structured midlatitude, continental MCSs. Pearson correlation coefficients (r) between the two
events for CONV, STRA, ANVL, and MCS total latent heating profiles are 0.99, 0.96, 0.71, and 0.97, respec-
tively. The low correlations in the ANVL regions are due to variations in the low-level cloud that forms
ahead of the leading convective lines. As such, rfor the ANVL regions above 5.5 km agl is 0.94.
Another difference in the latent heating profiles between the two events is that the 23–24 May CONV latent
heating rates (Figure 9e) were more intense than the 20 May event (Figure 9a). The more intense latent
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heating in the 23–24 May event was due to stronger W, which was likely driven by differences in the
environmental conditions. Mixed layer convective available potential energy values during the initial
stages of the 23–24 May event were ~3000 J kg
1
, compared to ~2000 J kg
1
for the 20 May event (not
shown). The model overprediction of the strongest Wfor the 23–24 May event may also have been a
contributing factor.
Comparing the latent heating rates within the different MCS subregions in Figure 9, the magnitude of latent
heating can vary by over 2 orders of magnitude. Understanding and quantifying such distinctions may be
useful for parameterizing the pervasive effects of MCS latent heating. For example, in order to assess the
integrated MCS impact of warming in the upper troposphere, total MCS profiles may be more useful (i.e.,
Figure 9d). However, to assess the interactions of gravity waves with the ambient environment, region-
specific profiles (i.e., Figures 9a–9c) may be more beneficial since the structure and magnitude of latent
heating in convective and stratiform regions can individually influence gravity wave properties [e.g.,
Nicholls et al., 1991].
Figure 10. The evolution of latent heating rates and contributing microphysical processes for convective and stratiform
regions for the 23–24 May event. (a) CONV latent heating and (b–e) contributions from key microphysical processes are
shown throughout the 12 h analysis period. The evolution of STRA latent heating and contributions from key microphysical
processes are shown in Figures 10f–10i. In STRA regions, RIME processes are negligible and are therefore not shown.
Approximate transitions between the development, mature, and decay stages are shown with the dashed, black lines.
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5. Time Evolution of Latent Heating
To facilitate the explanation of the temporal evolution of latent heating, the 12 h analysis periods for both
event simulations were separated into three stages, the development, mature, and decay stages of the
MCS lifetime. The transition between the development and mature stages was determined based on when
the trailing stratiform regions began to steadily grow in size, which occurred at approximately 06:00 UTC
(Figure 4) and 00:00 UTC (Figure 6) for the 20 May and 23–24 May events, respectively. The transition between
the mature and decay stages was determined based on when the leading convective line began to break
apart and lose its continuous connectivity, which occurred around 13:00 UTC and 05:45 UTC for the 20
May and 23–24 May events, respectively. Despite the somewhat subjective partitioning into lifecycle stages,
testing demonstrated that the results in this section were largely insensitive to changes in the exact times
used to define these transitions, which tend to be gradual processes. Figure 10 depicts the temporal evolu-
tion of total latent heating and latent heating from the separate microphysical processes for the CONV and
STRA regions, separated into the three stages, for the 23–24 May event. To quantify the results of
Figure 10, temporally averaged vertical profiles of net latent heating and latent heating from specific micro-
physical processes were computed for each MCS lifecycle stage from the spatial means within the CONV and
STRA regions. From these profiles, the arithmetic mean and maximum latent heating rates over the main
regions of net warming and cooling were then calculated for all levels where latent heating rates were
greater than 0.1 K h
1
. Percentage changes in the mean and maximum latent heating rates between lifecycle
stages are provided for reference in Tables 2 and 3.
Table 2. Changes in the CONV Maximum and Mean Latent Heating Rates Between MCS Lifecycle Stages
a
Development Stage LH
Rate (K h
1
)
Mature Stage LH Rate (% Change
From Development Stage)
Decay Stage LH Rate (% Change
From Development Stage)
Maximum Mean Maximum Mean Maximum Mean
Latent warming 44.3 18.5 30 26 52 57
Latent cooling 11.0 8.5 32 33 50 56
Process level
D-S warming 28.7 7.5 42 20 68 45
D-S cooling 3.7 2.5 15 13 65 56
C-E warming 40.8 24.8 29 29 48 65
C-E cooling 11.0 8.5 32 33 50 56
MELT cooling 10.7 4.0 8 12 22 21
RIME warming 3.8 2.1 22 18 54 53
a
Latent heating rate vertical profiles are spatially averaged over the CONV regions and then temporally averaged over
each lifecycle stage. From these profiles, the maximum and mean values are assessed over the main regions of warming
and cooling, as seen in Figure 10. The two left columns represent maximum and mean latent heating values during
the development stage, while the right four columns represent percentage changes in these values to the mature
and decay stages.
Table 3. Changes in the STRA Maximum and Mean Latent Heating Rates Between MCS Lifecycle Stages
a
Development Stage LH
Rate (K h
1
)
Mature Stage LH Rate (% Change
From Development Stage)
Decay Stage LH Rate (% Change
From Development Stage)
Maximum Mean Maximum Mean Maximum Mean
Latent warming 2.9 1.3 99 92 377 183
Latent cooling 5.1 3.6 10 8 124 23
Process level
D-S warming 3.1 1.4 113 58 75 65
D-S cooling 3.9 2.5 31 41 55 51
C-E warming 1.3 0.8 372 349 998 674
C-E cooling 4.0 2.9 511 144 20
MELT cooling 3.8 1.8 412 22 24
a
Latentheating rate vertical profiles are spatially averaged over the STRA regions and then temporally averaged over each
lifecycle stage. From these profiles, the maximum and mean values are assessed over the main regions of warming and
cooling, as seen in Figure 10. The two left columns represent maximum and mean latent heating values during the devel-
opment stage, while the right four columns represent percentage changes in these values to the mature and decay stages.
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For the 20 May event, new convection was initiating several hundred kilometers ahead of the weakening con-
vective line and the decaying MCS was eventually overtaken by a newly developing MCS. For these reasons,
the latent heating analysis of the 20 May MCS event’s decay stage was not completed. Therefore, to simplify
the explanation of the temporal evolution of latent heating, this section will focus on the results from the
23–24 May event, since the majority of the MCS lifetime was captured within the analysis domain for this
event and the latent heating profiles between the two events were highly correlated (Figure 9). Also, all of
the trends and related processes for the 23–24 May event discussed below are consistent with the 20 May
event simulation, except for trends associated with the decay stage.
5.1. Convective (CONV) Regions
The latent warming and cooling rates in the CONV regions generally decreased in magnitude throughout the
MCS lifecycle (Figure 10a and Table 2). Maximum latent cooling rates below the cloud base decreased by
~30% in each progressive stage (see Table 2). This reduction in evaporative cooling (Figure 10c) was due
to increases in the low-level relative humidity, which were driven by increased water vapor concentrations
and decreased temperatures. Schlemmer and Hohenegger [2015] recently demonstrated using a CRM simula-
tion that water vapor advection is the dominant contributor to moistening cold pool edges (i.e., the convec-
tive regions of MCS cold pools) within convective systems over land. This moisture advection into the CONV
regions, along with continued, though weakening, rates of precipitation evaporation and surface latent heat
flux all contribute to CONV subcloud moistening. Decreased temperatures below the cloud base were in part
a result of evaporation, the advection of cooler air, and diurnal cooling.
CONV latent warming from deposition, condensation, and riming all decreased throughout the MCS lifetime
(Figures 10b, 10c, and 10e, respectively) with relatively similar rates of ~20–30% per stage (see Table 2),
Figure 11. Vertical velocities within CONV and STRA regions for the 23–24 May MCS lifecycle stages. Contour frequency by
altitude diagrams (CFADs) [Yuter and Houze, 1995] of vertical velocity are shown for (a–c) the CONV regions and (d–f) the
STRA regions. The left column represents data during the development stage, while the middle and right plots represent
percentage differences in CFAD frequency from the development stage to the mature and decay stages, respectively.
CONV region bin spacing is 1 m s
1
, and stratiform bin spacing is 0.25 m s
1
. At each vertical level, 95% of the data fall
within the black contours.
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suggesting that these heating rates are tied to a similar forcing. To a first order, vertical velocity dictated these
changes in latent warming, since stronger and more frequent updrafts are associated with increased super-
saturation, enhanced hydrometeor collisions, and greater vertical flux of water within the cloud. Throughout
the MCS lifetime, the frequency of the strongest CONV vertical motions decreased (Figures 11a–11c). While
the mature stage had more frequent moderate updrafts (~1–8ms
1
) between ~3 and 10 km agl
(Figure 11b), the MCS had an increased propagation speed during the mature stage, which led to a larger,
storm-relative horizontal component to the CONV updrafts (i.e., ascending, front-to-rear flow), which can
be seen in the cross sections shown in Figure 12. In the development stage cross section (Figures 12a and
12b), the MCS updraft was more upright, with a significant southeastward component to the motion, forming
an extensive forward anvil, which can be seen in both the observations and simulations (Figures 5g, 5j, and
5m). In the mature stage cross section (Figures 12c and 12d), the MCS propagating speed (~17 m s
1
)
increased, assisting the development of a continuous front-to-rear, ascending flow. In the decay stage
(Figures 12e and 12f), the MCS was also composed of a front-to-rear, ascending flow, although weaker than
the mature stage. Propagation speeds were estimated for each cross section based on the locations of the
leading surface precipitation gradient (i.e., leading edge of the intense MCS precipitation) at the model out-
put time before and after the cross section time. The combination of less frequent intense vertical motions
Figure 12. MCS flow patterns shown in a 100 km cross section through the MCS during the different lifecycle stages. The
locations of cross sections are shown by a blue solid line in Figures 12a, 12c, and 12e and occur at 23 May 23:00 UTC, 24
May 03:00 UTC, and 24 May 07:00 UTC, respectively. Shading in Figures 12a, 12c, and 12e represents convective, stratiform,
and anvil regions as shown in Figures 3 and 5. Cross sections of vertical winds and horizontal winds in the direction along
the cross section are shown in Figures 12b, 12d, and 12f for the development, mature, and decay stages, respectively. The
black solid contour represents 0.1 g kg
1
total condensate mixing ratio. The yellow dotted contours represent 0.5 m s
1
vertical motions, while the yellow solid contours represent +1.0 m s
1
vertical motions. The shaded areas represent the
component of storm-relative winds that are parallel to the cross sections, with red colors portraying northwesterly winds
and blue colors portraying southeasterly winds. Storm motion was estimated to be 8.5 m s
1
, 17.0 m s
1
, and 17.0 m s
1
for the development, mature, and decay stage cross sections, respectively.
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and increased front-to-rear flow out of the CONV regions lowered the amount of water mass reaching the
upper levels of the CONV regions, which both weakened the magnitude of latent warming (Table 2) and
assisted in the decrease in altitude of the most intense deposition, condensation, and riming rates by
1–2 km throughout the analysis period (Figures 10b, 10c, and 10e).
5.2. Stratiform (STRA) Regions
The temporal evolution of the STRA latent heating profile includes a distinct transitional period between 24
May 00:00 UTC and 02:00 UTC (Figures 10f–10h). In the development stage, latent cooling was present below
6 km agl, and latent warming was present above this level. However, as the MCS entered the mature stage, a
prevailing storm-relative, front-to-rear flow and a descending rear-inflow jet above and below ~4 km agl,
respectively, developed. The evolution in the storm structure and flow pattern between the development
and mature stages of the MCS can be seen in the cross sections of Figure 12. This changing flow pattern
was not limited to this cross section, as indicated by the increase in the frequency of positive vertical motions
between 4 and 8 km agl and negative vertical motions below 4 km agl in Figure 11e. These processes shifted
the altitude of the transition between net latent warming and cooling to near 4 km agl in the mature stage
(Figure 10f).
The shift in transition altitude was also associated with increases in STRA precipitation processes, which acted
to moisten the midlevels (~3–6 km agl) and resulted in decreasing mean sublimation-based latent cooling
throughout the MCS lifecycle (Figure 10g and Table 3). Following the enhanced mature-stage precipitation,
low-level moistening and the eventual weakening of stratiform precipitation rates quickly reduced
evaporation-based latent cooling in the decay stage (Figure 10h). Similar to the CONV regions, decreases
in the magnitudes of sublimation-based cooling allowed peak melting rates to increase in altitude by
~1 km throughout the analysis period (Figures 10g and 10i).
In-cloud, STRA latent warming, which was most frequently caused by deposition throughout the majority of
the MCS lifecycle (Figures 9b and 9f), approximately doubled between the development and mature stages
(Figure 10g and Table 3), assisted by enhanced water flux from the CONV regions, as well as the generation of
supersaturation from the ascending, front-to-rear flow that developed. During the decay stage, stratiform
vertical motions weakened (Figure 11f), which led to decreased depositional heating rates. However, a spike
in condensation and evaporation rates occurred in the decay stage near 4 km agl, which led to increases in
maximum latent heating and cooling of up to several hundred percent from the development stage (see
Table 3). This spike in latent heating was focused in the large stratiform area in the northern region of the
MCS, where widespread condensational growth occurred between 4 and 6 km agl (Figure 13a). Within this
region, a dry, descending rear-inflow jet met moist, southerly flow that was advecting cloud water from
the convective region into the stratiform region (Figures 13b and 13c). The juxtaposition of these two airflows
provided conditions favorable for strong latent warming (via condensation) and latent cooling (via evapora-
tion), as shown in Figures 13b–13d. This spike in both condensation and evaporation and the resulting gra-
dient in diabatic heating developed a positive potential vorticity anomaly in the midlevels of the stratiform
regions during the late stages of the MCS’s lifetime. This enhancement of potential vorticity via diabatic heat-
ing gradients has been shown to contribute to the development of MCVs [e.g., Fritsch et al., 1994].
5.3. Anvil (ANVL) Regions
ANVL latent heating had minimal changes throughout the MCS lifetime, as compared to the STRA and CONV
regions. Peak cooling from sublimation occurred at approximately the same altitude (6–7 km agl) with
approximately the same magnitude (0.4 K h
1
) throughout the analysis period.
6. Conclusions and Implications
In this study, the vertical structure and evolution of MCS latent heating rates and the contributing microphy-
sical processes within CONV, STRA, and ANVL regions are assessed using CRM simulations. Such simulations
prove useful in this regard since observation-based methods of estimating latent heating rates have not been
able to resolve such details. This study focuses on two LLTS, midlatitude, continental MCS events (20 May and
23–24 May 2011) that occurred during MC3E. Using the cloud-resolving RAMS, 3-D simulations of these two
events were conducted and compared to a suite of observations. The simulated accumulated precipitation
totals for both events are within ~10% of the radar-gauge estimates. Weak-to-moderate convective updraft
Journal of Geophysical Research: Atmospheres 10.1002/2016JD024762
MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7929

strengths were well represented; however, the
strongest updrafts (95th percentile) were over-
predicted, particularly in the 23–24 May event
simulation. Overestimation of MCS updrafts by
CRMs has also recently been observed for both
tropical and midlatitude MCSs [Varble et al.,
2014; Fan et al., 2015]. The simulations produced
less stratiform area than was observed, although
the difference in percentage of the precipitating
area that was convective or stratiform was on
average within 10% of observations. In spite of
the shortfalls in simulating the strongest updrafts
and underpredicting stratiform area, the RAMS
model was able to reproduce many of the key
features of these two MCS events, including the
storm evolution, spatial development, and propa-
gation. Therefore, these simulations were used to
assess the key microphysical processes that con-
tributed to latent heating and the evolution of
these processes.
Temporally and spatially averaged latent heating
rates over a 12 h period beginning with the initial
convection associated with the MCSs were used
to quantify the latent heating rates due to various
microphysical processes within the different
MCS subregions. CONV regions had locally
more intense evaporation-based latent cooling
rates below cloud base, as compared to strati-
form regions. CONV regions also had peak
condensation-driven latent warming rates of
25–35 K h
1
between 4 and 8 km agl. While
CONV latent heating was dominated by conden-
sational growth in the midlevels, vapor deposi-
tion produced latent warming rates in the upper
levels of deep convection of ~10–20 K h
1
.In
the STRA regions, peak latent warming of
~5–8Kh
1
near 5 and 7 km agl was present.
Latent cooling rates from sublimation, melting,
and evaporation were ~2–4Kh
1
, with the domi-
nant process depending on the altitude. In both
CONV and STRA regions, peak melting rates
occurred up to 1 km below the freezing level due
to sublimation. Riming and nucleation processes
had a limited impact on latent heating, regardless
of the MCS region. The key process driving latent
cooling in the nonprecipitating anvil (ANVL)
regions was net sublimation, and these cooling
rates were at least 1 order of magnitude smaller
than STRA and CONV latent heating.
The shapes of the vertical profiles and the magni-
tude of net latent heating rates were well corre-
lated between the two simulated events (r
greater than 0.94 for all regions) and were gener-
Figure 13. Cross section through the simulated 23–24 May
MCS at 24 May 2011 07:00 UTC. In Figure 13a, purple, green,
and brown regions represent CONV, STRA, and ANVL regions of
the MCS, respectively. The white contours represent conden-
sation rates of 1 g kg
1
5 min
1
at ~5 km agl. (b–d) Cross
sections through the blue line shown in Figure 13a. The black
solid lines in Figures 13b–13d represent total condensate
mixing ratio of 0.1 g kg
1
. In Figure 13b, vertical winds are
shaded, 5 m s
1
meridional winds (m s
1
) are contoured with
solid grey lines, and 5ms
1
meridional winds (m s
1
) are
contoured with dotted grey lines. In Figure 13c, relative
humidity with respect to liquid water is shaded, with
0.25 g kg
1
and 1.0 g kg
1
contours of ice water, cloud liquid
water, and rainwater shown in red, yellow, and green, respec-
tively. In Figure 13d, condensation rates are shaded, while
latent heating rates of 15 and 15 K h
1
are contoured with
solid and dotted grey lines, respectively.
Journal of Geophysical Research: Atmospheres 10.1002/2016JD024762
MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7930

ally in keeping with prior observation-based studies of midlatitude, continental MCSs [Gallus and Johnson,
1991; Braun and Houze, 1996], except for the convective regions where observations can be impaired by alias-
ing biases. This suggests that information from these simulations may be applicable to similarly structured
MCSs developing in comparable environments.
This research has also demonstrated that the typical latent heating magnitudes and profile shapes within
MCS regions can change substantially over the MCS lifetime, which is summarized in Figure 14 for both
MCS events. CONV latent warming and cooling magnitudes changed in an approximately linear manner,
decreasing by over 50% from the development stage to the decay stage. In the STRA regions, deviations
in both the latent heating magnitudes and profile shapes occurred. As the MCS transitioned from the
development stage to the mature stage, less frequent intense vertical motions and increased storm propa-
gation speeds resulted in more front-to-rear flow within the MCS, which more than doubled deposition and
condensation rates in the STRA regions, yet weakened the latent heating from these processes in the CONV
regions. In the decay stage, weaker updrafts reduced both the magnitude and altitude of peak latent
warming in CONV regions. In the STRA regions, deposition-based latent heating also weakened during
the decay stage, but condensation and evaporation rates spiked near 4 km agl, where widespread cloud
developed along a boundary of dry, descending rear inflow and warm, moist front-to-rear flow. In both
CONV and STRA regions, sublimation- and evaporation-based latent cooling from precipitating hydrome-
teors generally decreased with time due to atmospheric moistening and cooling from a variety of pro-
cesses. ANVL regions were subject to minimal changes in latent heating throughout the simulation, as
compared to CONV and STRA regions.
Understanding and quantifying the temporal evolution of MCS latent heating profiles can be useful for
developing parameterizations of organized convection for large-scale models that do not explicitly repre-
sent cloud processes. For the All MCS regions (Figures 14d and 14h) and the CONV regions (Figures 14a
and 14e), the shapes of the latent heating profiles can be better approximated as constant throughout
the evolution, in comparison with the STRA regions, which does not evolve in such a manner. Therefore,
for the CONV and All MCS regions, a latent heating parameterization can be more simply represented.
Figure 15 demonstrates the results of such a parameterization based on data from the 23–24 May All
MCS regions (Figure 14d), since this MCS event’s lifecycle was more fully simulated within the analysis
domain and period. The evolution of latent heating at the altitude of maximum latent warming (~6.7 km
agl; Figure 14d) and maximum latent cooling (~0.7 km agl; Figure 14d) was used as representative points
in order to estimate the entire profile evolution. The latent heating rates at these two altitudes were nor-
malized by the maximum latent warming and cooling rates from the 12 h analysis period at the respective
23-24 May Event
Altitude (km)
0
4
8
12
16 Convective
Develop.
Mature
Decay
Stratiform Anvil All MCS
Latent Heating (K h-1)
-15 0 15 30 45
20 May Event
Altitude (km)
0
4
8
12
16
Latent Heating (K h-1)
-10 -5 0 5 10
Latent Heating (K h-1)
-1 -0.5 0 0.5 1 1.5
Latent Heating (K h-1)
-3 -1.5 0 1.5 3 4.5
(a) (b) (c) (d)
(e) (f) (g) (h)
Figure 14. Schematic of the latent heating profile changes with MCS evolution. Profiles are shown for CONV, STRA, and
ANVL regions, as well as the entire MCS. Profiles were calculated for both (a–d) the 23–24 May event and (e–h) the 20
May event and were smoothed using a 5-point moving average. The decay stage for the 20 May event is not shown since
new, developing convection formed during this event’s decay stage.
Journal of Geophysical Research: Atmospheres 10.1002/2016JD024762
MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7931

altitudes and is shown in Figure 15a. A
seven-term polynomial was used to fit
this evolution and produced a correla-
tion coefficient (r) of ~0.98 (red dashed
line in Figure 15a). The polynomial
function is
ωtðÞ¼a0þa1tþa2t2þa3t3þa4t4
þa5t5þa6t6
where a
0
= 0.54, a
1
= 10.4, a
2
=92.4,
a
3
= 305.4, a
4
=485.3, a
5
= 371.8, and
a
6
=110.4 from the polynomial regres-
sion, ωrepresents a weighting applied
to all vertical levels of a specified latent
heating profile, and tis the fractional
time of the MCS simulation. For the 23–
24 May event, tcan be approximated
to also represent the fraction of the
MCS lifetime. The specified latent heat-
ing profile consists of the maximum
latent heating rates for an MCS event
and can be adjusted based on environ-
mental conditions. For example, for
environments with higher CAPE values,
more intense latent heating is expected,
as was the case for the 23–24 May event.
Figure 15a also includes the evolution of
latent heating at the altitude of maxi-
mum latent warming and cooling for
the 20 May event, which also correlated
well with the polynomial fit(rof 0.92).
The slightly lower correlations in this
case were largely driven by the fact that
the 20 May event simulation did not cap-
ture the full evolution of the decay stage,
as was noted in section 5. Figure 15b dis-
plays the result of such a parameterized
All MCS latent heating evolution, along-
side the All MCS latent heating evolution
for the 23–24 May event (Figure 15c) and
20 May event (Figure 15d). Despite omit-
ting some of the finer features in the All
MCS latent heating, such as the spike in
the latent heating gradient that occurs
during the decay stage, this simple for-
mula does reproduce the general evolu-
tion of the All MCS latent heating. To
better account for the finer details that
are associated with individual MCS sub-
regions (i.e., CONV, STRA, and ANVL), a
similar methodology can be applied to
the individual region profiles, with the
added complexity of developing a
time-dependent function to represent
Figure 15. Latent heating parameterization. (a) The evolution of normal-
ized latent heating and cooling rates at the altitudes where these values
are maximized and minimized in the All MCS profiles, as well as a poly-
nomial regression fit for the 23–24 May event evolution. (b) The results of
the simple latent heating parameterization discussed in the text, and (c
and d) the actual evolution of the all MCS latent heating profiles for the
23–24 May and 20 May events, respectively.
Journal of Geophysical Research: Atmospheres 10.1002/2016JD024762
MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7932

how the area partitioning of the different regions evolve. Furthermore, for the evolution of profiles where the
shape changes significantly (i.e., the STRA regions), the latent heating profile may need to be decomposed
into several profiles, each with its own evolution weighting function (ω).
As latent heating evolves throughout the lifetime of the MCS, the forcing that it imposes on the storm system
itself and its ambient environment will also vary. The changing latent heating magnitudes and profile shapes
in both CONV and STRA regions will alter the structure of the gravity waves that form, and how they couple
with each other and the environment. Weakening rates of latent cooling in both subcloud CONV and STRA
regions will also impact how the MCS cold pool develops, and subsequently, how the MCS will propagate
and force new convection. The couplet of intense condensation and evaporation that intensifies the latent
heating vertical gradient in the STRA regions during the decay stage can create more favorable conditions
for MCV development. Large-scale models that have not been designed to reproduce the complex structure
of latent heating profiles within MCSs and their temporal variability will be unable to properly reproduce
these and other downstream effects of MCS latent heating. Del Genio et al. [2012] argued that given the
organized nature of MCSs, GCM parameterizations require a “mesoscale memory”to properly represent
the evolution of these systems. The results provided in this study support that argument and demonstrate
the dynamic yet regulated nature of MCS latent heating throughout the MCS lifecycle.
CRMs continue to be one of the best tools to assess this temporal evolution of latent heating and microphy-
sical process rates. For this reason, latent heating algorithms for TRMM [e.g., Tao et al., 2006; Shige et al., 2009]
have all been based, to some degree, on CRM simulations. CRM simulations continue to improve with more
sophisticated parameterizations and enhanced grid resolutions, thus offering more confident guidance to
the development of improved convective parameterizations and satellite algorithms that require cloud-scale
information, such as latent heating rates. However, further observations are needed to assist in the validation
of CRM microphysical processes and the dynamic motions driving them.
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MARINESCU ET AL. LATENT HEATING PROFILES IN TWO MC3E MCSS 7933
Acknowledgments
This work was supported by the
Department of Energy under Grant
DE-SC0010569. The simulation data are
available upon request from Peter
Marinescu (peter.marinescu@colostate.
edu). The Stage IV precipitation data
were obtained from National Center for
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ucar.edu/codiac/dss/id=21.093. The
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aircraft observations and Steven A.
Rutledge, Brenda Dolan, and Alyssa
Matthews for processing and providing
us with the MC3E radar data for the two
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