PUBLICATIONSJournal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2016JD024965
Key Points:• Convection precursors on the globe
are presented• The key factors, convergence and
CAPE, have different contribution in
the convection• Better estimation of convergence and
CAPE is critical for accurate forecast of
convection

Precursors of deep moist convection in a subkilometer
global simulation
Yoshiaki Miyamoto1,2, Tsuyoshi Yamaura2, Ryuji Yoshida2,3, Hisashi Yashiro2, Hirofumi Tomita2,4,
and Yoshiyuki Kajikawa2,3
1

Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA, 2RIKEN Advanced Institutefor Computational Science, Kobe, Japan, 3Research Center for Urban Safety and Security, Kobe University, Kobe, Japan,
4
Seamless Environmental Prediction Research, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Abstract Deep moist convection in the atmosphere plays an important role in cloudy weather disturbances,
Correspondence to:Y. Miyamoto,ymiyamoto@riken.jp

Citation:
Miyamoto, Y., T. Yamaura, R. Yoshida,
H. Yashiro, H. Tomita, and Y. Kajikawa(2016), Precursors of deep moist
convection in a subkilometer global
simulation, J. Geophys. Res. Atmos., 121,
12,080–12,088, doi:10.1002/
2016JD024965.
Received 18 FEB 2016
Accepted 26 SEP 2016
Accepted article online 28 SEP 2016
Published online 26 OCT 2016

such as hurricanes, and even in the global climate. The convection often causes disastrous heavy rainfall, andpredicting such convection is therefore critical for both disaster prevention and climate projection. Although
the key parameters for convection have been pointed out, understanding the preprocesses of convection isa challenging issue. Here we identiﬁed the precursors of convection by analyzing a global simulated data set
with very high resolution in time and space. We found that the mass convergence near the Earth’s surfacechanged signiﬁcantly several minutes before the initiation of early convection (the formation of cumulus
clouds), which occurred with the increase in the convective available potential energy (CAPE). Decompositionof the statistical data revealed that a higher-CAPE environment resulted in stronger convection than in thestronger-convergence case. Furthermore, for the stronger-convergence case, the precursor was detected
earlier than the total average (10–15 min before the initiation), whereas the amplitude of maximum velocity
was not so strong as the higher-CAPE case. This suggests that the strength of convection is connected with
CAPE, and the predictability is sensitive to the convergence.

1. Introduction
Different types of convection exist simultaneously in the global atmosphere. Deep moist convection develops vertically, extends through the entire troposphere, and is driven by buoyancy due to the phase changes
of water [Byers and Braham, 1948a]. Convection plays an essential role in transferring momentum, energy,
and chemical species in the atmosphere [LeMone and Zipser, 1980; Ching and Alkezweeny, 1986; Jorgensen
and LeMone, 1989; Takemi, 2008; Nasuno and Satoh, 2011; Masunaga and L’Ecuyer, 2014] and affects generalcirculation [Stevens and Bony, 2013; Bony et al., 2015]. Furthermore, convection is the source of local torrential
downpours that cause severe disasters. Along with understanding the mechanism of convection, one of thegreatest challenges in current meteorological research is the accurate prediction of convection [Doswell,1985; Stevens and Bony, 2013], i.e., when and where it will occur.
The driving mechanism of convection has been studied intensively under various conditions. Convection iscreated by thermodynamic and dynamic forces [Emanuel, 1994; Houze, 1994; Jorgensen and Weckwerth,
2003; Weckwerth and Parsons, 2006]. When the atmosphere is conditionally unstable, convection forms froma weak perturbed motion to make the atmosphere stable. On the other hand, convection can also be formed
by a strong mass convergence in the boundary layer, which causes upward motion in the upper or above the
top of the boundary layer. The vertical motion results in the condensation of water and acceleration of thevertical velocity. Once convection develops, it usually causes precipitation as a result of growing processesof water/ice droplets in convective clouds.

©2016. American Geophysical Union.
All Rights Reserved.

MIYAMOTO ET AL.

In general, convection has traditionally been understood as an atmospheric phenomenon with a highdegree of randomness that can be predicted in advance at time scales only in the order of a few tensof minutes [Lorenz, 1969]. The temporal and spatial scales of convection are in the orders of 1 h to 1 km
[Byers and Braham, 1948b; Emanuel, 1994; Houze, 1994], respectively, which are 2 to 3 orders of magnitude
smaller than synoptic-scale or meso-alpha-scale systems and play important roles both in short- and longrange forecasts [Zhang et al., 2003; Petch, 2004; Hohenegger and Schär, 2007a, 2007b; Zhang et al., 2007;
Melhauser and Zhang, 2012]. From the synoptic-scale or mesoscale perspective, this convection forms
due to small-scale perturbations. Nonlinearity due to phase change processes also cause interference in

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accurately predicting the mechanisms in large-scale systems that determine the environmental conditionsof convection.
For accurate prediction, better understanding of preprocesses of convection is essentially needed. A numberof studies have investigated the processes leading to convection, and it is understood that low-level massconvergence plays an important role in initiating deep convection [Kalthoff et al., 2009; Barthlott et al.,2010; Behrendt et al., 2011; Birch et al., 2014]. However, the main focus of many studies has been on large-scaleconvective systems [Sherwood and Wahrlich, 1999; Mapes et al., 2009; Masunaga, 2014; Masunaga and
L’Ecuyer, 2014], while other studies have focused on convection in speciﬁc regions or under certain background conditions [Wulfmeyer et al., 2008; Plant, 2009; Barthlott et al., 2011; Behrendt et al., 2011; Wulfmeyer
et al., 2011; Birch et al., 2014]. Because convection appears to be sensitive to its environmental conditions,
it is expected to vary widely around the globe [Miyamoto et al., 2015].
This study was the ﬁrst attempt to investigate the precursors of deep moist convection under various conditions in the Earth’s atmosphere. We show the existence of precursors whose magnitude changed signiﬁcantly
before the initiation of moist convection based on a statistical analysis of the global simulation. The simulation was conducted with the extremely ﬁne grid point spacing (0.87 km) that is ﬁnest possible with thecomputer resources available [Miyamoto et al., 2013, 2015; Kajikawa et al., 2016]. These high-resolution global
simulation data provided tens of thousands of convection samples under a variety of cloud disturbances[Miyamoto et al., 2015].

2. Simulation Data and Deﬁnition of Cloudy Disturbances
We conducted a numerical simulation of a global atmosphere with subkilometer resolution using theNonhydrostatic Icosahedral Atmospheric Model (NICAM) [Tomita and Satoh, 2004; Satoh et al., 2008, 2014].
The experimental settings were identical to those of Miyamoto et al. [2013]. The horizontal model resolution
was 0.87 km, and the vertical resolution of 94 layers increased from 40 m at the bottom to 40 km at the top.
The physical processes were solved by the following parameterizations: long-wave and short-wave radiation[Sekiguchi and Nakajima, 2009], cloud microphysics [Tomita, 2008], boundary layer turbulence [Nakanishi andNiino, 2004; Noda et al., 2010], and surface ﬂuxes [Louis, 1979]. In the simulation, cumulus parameterization
was omitted. The initial condition was built from a 3 day simulation result, with 3.5 km resolution. The initialcondition for the 3 day integration was the linearly interpolated data at 2012082200UTC of the National
Center for Atmospheric Research ﬁnal analysis [Kalnay et al., 1996]. A 13 h simulation was conducted from2012082500UTC, and the results from the ﬁnal hour were used in this study. To examine the life cycle ofthe convection, the output interval was 1 min for a target period of 1 h from 2012082512UTC. Although
the period for the analysis is much shorter than the time scale of environmental ﬂow of convection, it is longenough to cover the lifetime of convection (a few tens of minutes), and a number of convection samples aredetected over various regions on the globe as shown below.
The predictability of convection was statistically examined by considering the characteristics of the physicalquantities before the initiation of convection. Hence, the life cycle of convection needed to be determined in
the simulated result. In this study, we refer to the onset of the convection-forming stage as the initiation of
convection (see below for the quantitative deﬁnition). We used the two-step methodology of Miyamoto
et al. [2013] for detecting the convective grid points and the convection core. The convective grid points weredeﬁned as the grid points in which the vertically averaged total water and ice mixing ratios exceeded
0.2 g kg3. The vertical averages were estimated from the 1.5 km height to the tropopause height. The convection core grid points were then detected as local peaks of the vertically averaged vertical velocity. In thisstudy, the vertically averaged water and ice mixing ratios were used to diagnose the convective grid points,
rather than the cloud optical thickness and cloud top pressure as done by Miyamoto et al. [2013] to utilize theInternational Satellite Cloud Climatology Project (ISCCP) table [Rossow and Schiffer, 1999]. Furthermore, the troposphere was divided into ﬁve layers and the vertical averages were estimated for each layer. The convection
core grid points were detected at each layer. This methodology was applied to each output time (every 1 min inthis case). As a result, we obtained a data set of convection core grid points that contained information on thehorizontal location, layer, and convection parameters, such as CAPE, at each output time.
We developed the following tracking algorithm for the convection core grid points. The core grid points werediagnosed to survive in the time interval from t = t1 to t1 + Δt, where Δt is the output interval. If there was a
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Figure 1. (a) Horizontal view of a simulated cloud ﬁeld at 201208251200UTC and (b–e) a magniﬁed view of a developing convective cell. The white color shows the
mixing ratio of hydrometeors. The topography and bathymetry are from Blue Marble (August) by Reto Stöckli, NASA Earth Observatory.

core grid point at t = t1 + Δt within two-grid point distance (~1.74 km) from a core grid point at t = t1, thesetwo cores were considered identical. The searching process at t = t1 + Δt started from the core grid point at
t = t1; then, the four neighboring grid points were examined; and ﬁnally, the other grid points, apart fromthe grid point at t = t1, were diagnosed. This seems to be a reasonable approach. Because the velocity tomove a grid point distance (0.87 km) in 60 s (the output interval) is 14.5 m s1, the possibility of the existenceof a convection core rarely exceeded this value after vertically averaging the horizontal velocity. The core grid
points that were sustained for at least 10 min were detected as the convection grid points. Applying thismethodology to the 1 h simulation data, we obtained more than 30,000 convection samples.

3. Results and Discussion
Figure 1a shows our simulated cloud ﬁeld on the globe at 201208251200UTC. A variety of clouds andcloudy disturbances were simulated, and the simulation accurately captured the realistic cloud ﬁeld
[Leinonen et al., 2015]. More detailed comparisons of the subkilometer simulation with observations canbe found in Kajikawa et al. [2016]. Figures 1b–1e depict the typical life cycle of a cloud cell located at
approximately (longitude, latitude) = (177, 0) at 15 min intervals. The cloud cell forms, or, more precisely, theMIYAMOTO ET AL.

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Figure 2. (a) Time series of maximum vertical velocity (the red line) and mixing ratio of water and ice at the core grid pointof detected convection that is vertically averaged from z = 1.5 km to the tropopause altitude (the blue line). They are thearithmetic mean of all the detected convection samples. (b–e) Composite radius-height cross sections of total water-mixing
1
ratio (shaded) and vertical velocity (contour). The contour interval is 0.5 m s . The vertical axis is nondimensionalized
by the altitude at which the vertical velocity reaches the lifetime maximum of each convection.

cloud water-mixing ratio becomes large enough to be drawn (approximately 0.2 g kg1). It grows vertically,
and the vertical extent of the high water content region reaches a maximum in approximately 20 min. Then,an anvil forms at the top of the troposphere. The structural evolution, as well as its time scale, is consistent withthe observed isolated thunderstorm [Byers and Braham, 1948b; Emanuel, 1994; Houze, 1994].
Applying the scheme to the simulation data, we obtained 30,100 convection samples throughout the globe
over 1 h of simulation. To validate the developed method, we examine the temporal evolutions of parameters
averaged by all the detected convection (Figure 2). The composites were constructed after the time is nondimensionalized by that when the vertical velocity reaches the lifetime maximum of each convection (τ = 1)
to remove inconsistency of convection lifetimes. Figure 2a shows the time series of maximum vertical velocity
and vertically averaged total hydrometeors. The maximum vertical velocity is initially small (τ = 0, when theconvection is ﬁrst detected) and evolves until the peak time (τ = 1). The vertically averaged total mixing ratiothat is the sum of water and ice evolves in a similar way to the vertical velocity: it ampliﬁes from a small value
to the maximum at τ = 1 when the vertical velocity reaches its lifetime maximum.
Figures 2b–2e show the composite radius-height cross sections of vertical velocity and total mixing ratios. It is
indicated that the vertical velocity and cloud water are initially weak and concentrated in the lower troposphere. The peaks of both quantities raise up to higher altitudes with amplifying their magnitudes. Whenthe vertical velocity reaches the maximum (τ = 1), the peaks are located in the upper troposphere. Then, they
decay afterward. The radial extents of large mixing ratio and vertical-velocity region are about 2–3 km. On
average, the time at which the vertical velocity reaches its maximum after the ﬁrst detection of convection(i.e., from τ = 0 to 1) is about 10 min. Comparison of the composites (and some examples) with observational
data indicates that the methodology of detecting the developed convection in this study is applicable to theglobal simulation data.

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Figure 3. Time series of the following parameters at the grid point when convection was ﬁrst detected, before andafter the initiation of convection: (a) CAPE, (b) CIN, (c) low-level convergence (LCON), and (d) vertical shear of horizontal
wind (ZSHR). The divergence is vertically averaged in the lowest 500 m. The vertical shear is deﬁned as
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

2 
2ﬃ
ubg;10:0  ubg;1:5 þ v bg;10:0  v bg;1:5 where the subscript bg indicates the 100 km mean around the convection
and is calculated between the 1.5 and 10.0 km altitudes. The thick black and gray lines indicate the averages at theconvection-formed grid points and within the 100 km radius, respectively. The dashed line represents a quarter of thestandard deviation.

We show the temporal variations of selected physical quantities that have been reported to play importantroles in driving the convection at the grid points where each convection event is ﬁrst detected in its life cycle.
Figure 3 represents the time series of the following quantities: the CAPE, the low-level mass convergence,the convection inhibition (CIN), and the vertical wind shear at the convection-formed grid points before
and after convection was initially detected. The CAPE calculated from the air parcel averaged in the lowest
500 m is larger than the surrounding mean and decreases until the convection initiation (i.e., t = t0)(Figure 3a). Note that the surrounding mean is calculated inside the 100 km radius of the convection. The
CIN that was calculated in the same way as CAPE keeps almost a constant value corresponding to thesurrounding mean. The mass convergence averaged below the 500 m altitude rapidly decreases about
5–6 min prior to the initiation (Figure 3c). The vertical shear was deﬁned as the wind difference between
the 1.5 km and 10.0 km altitudes. Similar to the CIN, the vertical shear has little changes within the
50 min period with a slightly decreasing trend with time (Figure 3d).
In order to clearly see the relative importances of the four parameters, the temporal changes in the normalized
parameters are shown in Figure 4. We calculated each parameter in the following manner: First, the averages
and variances of the time series were calculated for each sample; the time series were then normalized by theaverages and variances; and, ﬁnally, the normalized time series of all samples were composited. The deviations of CAPE and convergence from the environment before the initiation of moist convection were statistically signiﬁcant satisfying the 99% conﬁdence level by conducting the t test at each time (every 1 min). In
contrast to these quantities, neither the CIN nor the vertical wind shear showed a clear difference from theenvironmental mean. We conducted the same analysis for other quantities such as the low-level temperaturedeviation from the environment and found that only these two quantities showed a clear signal before theinitiation of moist convection. In short, convection on the global scale generally forms due to an enhanced
low-level convergence in the presence of a high-CAPE environment.
The question now arises: what are the differences in convection and its precursors between the dynamicallyfavorable (boundary layer convergence) and thermodynamically favorable (high-CAPE) conditions? Figure 5ashows a scatterplot of all the convection samples in an area of convergence and CAPE (convergence-CAPE

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Figure 4. Time series of the normalized four parameters in Figure 3, which were obtained by ﬁrst normalizing the quantitiesof each sample by the average and variance of the time series and then taking an average of all samples. The averages andstandard deviations of quantities are listed in the ﬁgure. The red, blue, black, and gray lines stand for CAPE, low-level
convergence (LCON), CIN, and vertical wind shear (ZSHR), respectively. The thin dashed line indicates the zero value. The total
number of samples is 30,100, and the averaged time when the vertical maximum velocity of a convective cell reaches (tm) is
9.97 min after the initial detection.

space). We divided them into four groups using thresholds, beyond which the sample number was 10% of thetotal (90th percentile). In the ﬁrst group both exceeded the thresholds (marked with D); in the second group
CAPE satisﬁed the top 10% condition, but convergence did not (C); in the third group convergence satisﬁed
the top 10% condition, but CAPE did not (B); and in the fourth group neither satisﬁed the threshold (A).
Figures 5b–5e show the structure and temporal change in precursors of the convection in groups B and C.
The convection structures were clearly different in the two groups, whereas the temporal overall evolutionof the structure in both groups was qualitatively similar to that of the global average (ﬁgure not shown). In
particular, the vertical velocity was stronger and the water content was higher in the high-CAPE group (C)than in the strong-convergence group (B) and the global average (Figures 5b and 5c). Thus, convection grew
to reach a stronger intensity and higher altitude under the higher-CAPE environment, despite the fact thatthe time at which the vertical velocity reached its lifetime maximum did not differ signiﬁcantly. The
differences in maximum velocity and its altitude were statistically signiﬁcant at the 95% conﬁdence level.
In contrast, in the strong-convergence group (B), the convergence started deviating from the average earlier
than from the high-CAPE group (C) and global average (about 15 min).
In conclusion, the convection could obtain a strong intensity when CAPE was high before its initiation,
whereas it was not as intense under the strong-convergence condition. The strong-convergence case produced a signal 15 min before the initiation of convection. In other words, the strength of convergencechanges the time when the convergence departs from the environmental average. It suggests that the predictability time scale of convection is related to the strength of low-level convergence, whereas CAPE does
not produce signiﬁcant changes in the time scale; rather, it changes the strength of convection.
Figure 6 depicts the horizontal and latitudinal distributions of the difference in the number density of convection between the two groups. Convection in the high-CAPE group mainly occurred in the tropics as expected(red squares, Figure 6a). However, the samples in the strong-convergence group were not solely concentrated in the tropics but were also found in the midlatitudes, where convection was accompanied by midlatitudinal systems such as fronts [Miyamoto et al., 2015]. At a given latitude, there was a large variety in thedominant precursors, especially in the Indian Ocean and in the western North Paciﬁc (Figure 6a). In contrast,
central Africa and the western part of South America were dominated by a single group. The convective cells
in the former could be categorized as the high-CAPE group, whereas those in the latter were mainly in thestrong-convergence group. The diversity among the continents may be due to the time difference: since
the analyzed time was 00UTC, Africa is in the afternoon whereas North and South America are in the earlymorning. It should be noted that the present results are obtained from a 1 h integration and hence longer
integrations are necessary to more comprehensively discuss the areal variations.

4. Concluding Remarks
A global simulation with very high spatiotemporal resolution, the grid spacing of 870 m and output interval of
1 min, was conducted using a nonhydrostatic global model (NICAM) with one of the world’s fastest supercomputers (K computer). We developed a simple methodology detecting the deep convection core grid

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Figure 5. (a) Scatterplot of all the convection samples in the convergence-CAPE space. The thick lines indicate the normalized frequency. The thin dashed and solidlines are the average and average plus one standard deviation, respectively. The blue and red lines indicate the convergence and CAPE, respectively. The groups, A, B,
C, and D, are deﬁned in the text. (b) The vertical proﬁle of vertical velocity for groups B (blue) and C (red) when the vertical velocity reaches its lifetime maximum(τ = 1). (c) Same as Figure 5b but for the hydrometeor mixing ratio. (d, e) Same as Figure 4, but for group B (Figure 5d) and group C (Figure 5e) and showing only the
low-level convergence and CAPE.

Figure 6. (a) Global view of the difference in number density of convection samples from the high-CAPE group (C) to the strong-convergence group (B). The dots
represent the location of all the convection samples. The unit of color label is the standard deviation of the difference in number density (σ). (b) Latitudinal proﬁles of
the number densities in the strong-convergence (blue) and the high-CAPE groups (red). The number densities were estimated in a 5 × 5° area and normalized by the
total number of each group.

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points and life cycle from a spatiotemporary discretized data. Since the method requires less thresholds, it
can be utilized for the global data. Applying the developed method to the high-resolution global simulation
output, 30,000 convection samples were approximately detected. The composites of temporal evolution of
maximum vertical velocity and vertically averaged mixing ratio of hydrometeors, and radius-height crosssection of convection samples were reasonable compared with observations of the convective cell (isolated
thunderstorm [e.g., Byers and Braham, 1948a]).
We analyzed the obtained convection samples to examine the temporal change in several parameters
related to the deep convection at the grid point where the convection cell was initially detected before
and after the initiation of convection. The results presented here indicate that the parameters that act as precursors were ampliﬁed before the initiation of deep moist convection. In particular, the mass convergence inthe boundary layer was ampliﬁed several minutes before the convection initiation and CAPE kept larger
values than the surrounding mean (inside the 100 km distance from the convection core) until the initiation.
The convection samples were divided into the four groups according to the magnitudes of low-level convergence and CAPE. We found that the low-level convergence deviated from the surrounding mean earlier inthe group with strong convergence than that in all samples. The samples in the high-CAPE group obtained
stronger maximum vertical velocity. Thus, it strongly suggests that the predictability time scale of convection
depended on the magnitude of low-level convergence, whereas CAPE was a key function that determined
the strength of convection and it was not directly related to the predictability of convection.
It is also shown that the dominant parameters were different from region to region on the globe. In theregion where the convection was active in the present simulation [Miyamoto et al., 2015], we observed a large
variety of the dominant parameter. In contrast, a single parameter dominated the continents (e.g., Africa orSouth America). This may be related to the time difference among the continents. Furthermore, it issuggested that the dominant precursor parameter is possibly different between the convection formed on
the ocean and land. Hence, longer integrations are desired for more comprehensive discussions.
Particularly, a daylong integration for diurnal cycle and a yearlong integration for seasonal cycle are desired
for future works.
Acknowledgments
The authors thank three anonymous
reviewers for their helpful comments,and H. Seko, S. Iga, S. Nishizawa, Y. Sato,
S. Adachi, H. Miura, M. Satoh, S. Otsuka,
K. Saito, T. Kawabata, Wei-Ting Chen,
Chien-Ming Wu, and our colleagues forfruitful discussions. The simulations
were performed using the K computerat the RIKEN Advanced Institute for
Computer Science. The data are stored
in a storage server at RIKEN, and it isavailable on request (ymiyamoto@riken.
jp). Figure 1 was drawn using softwaredeveloped by NCAR, VAPOR (www.
vapor.ucar.edu).

MIYAMOTO ET AL.

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