Seko et al. Progress in Earth and Planetary Science (2015) 2:42
DOI 10.1186/s40645-015-0072-3

RESEARCH

Open Access

Ensemble experiments using a nested
LETKF system to reproduce intense vortices
associated with tornadoes of 6 May 2012 inJapan
Hiromu Seko1,2*, Masaru Kunii1, Sho Yokota1, Tadashi Tsuyuki4,1 and Takemasa Miyoshi3

Abstract
Experiments simulating intense vortices associated with tornadoes that occurred on 6 May 2012 on the Kanto Plain,
Japan, were performed with a nested local ensemble transform Kalman filter (LETKF) system. Intense vortices werereproduced by downscale experiments with a 12-member ensemble in which the initial conditions were obtained
from the nested LETKF system analyses. The downscale experiments successfully generated intense vortices in three
regions similar to the observed vortices, whereas only one tornado was reproduced by a deterministic forecast. The
intense vorticity of the strongest tornado, which was observed in the southernmost region, was successfully
reproduced by 10 of the 12 ensemble members. An examination of the results of the ensemble downscale
experiments showed that the duration of intense vorticities tended to be longer when the vertical shear of thehorizontal wind was larger and the lower airflow was more humid. Overall, the study results show that ensemble
forecasts have the following merits: (1) probabilistic forecasts of the outbreak of intense vortices associated withtornadoes are possible; (2) the miss rate of outbreaks should decrease; and (3) environmental factors favoringoutbreaks can be obtained by comparing the multiple possible scenarios of the ensemble forecasts.
Keywords: Ensemble forecast, Tornadoes, LETKF

Background
Local heavy rainfalls and tornadoes cause severe damage
due to flash floods, landslides, and strong winds. Accurate
forecasts of these phenomena, for example, by numericalmodels, could reduce the damages they cause. However,numerical models are not perfect, and the initial conditions of numerical forecasts include errors. In particular,
forecasts of convection cells generated in areas of weak
convergence, which frequently occur in urban areas suchas the Tokyo Metropolitan Area in summer, are affectedby small errors in the initial conditions. This means that
forecasts of the position and timing at which a thunderstorm will be generated from a weak convergence are* Correspondence: hseko@mri-jma.go.jp
1
Meteorological Research Institute, Japan Meteorological Agency, 1-1
Nagamine, Tsukuba, Ibaraki 305-0052, Japan
2
Japan Agency for Marine-Earth Science and Technology, 3173-25,
Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
Full list of author information is available at the end of the article

sensitive to errors in the initial conditions. Because of theresulting uncertainty in forecasts of convection cell generation, ensemble forecast techniques must be employed to
produce probabilistic forecasts. In addition to probabilistic
forecasts, ensemble forecasts produce multiple possible
scenarios. Thus, ensemble forecasts have the following
merits: (1) analyzed fields of ensemble forecasts (i.e., theensemble average of multiple scenarios) are statisticallymore accurate than those of deterministic forecasts and(2) multiple possible scenarios provide information regarding uncertainty (i.e., when there is large scatter among
the scenarios, the uncertainty is larger).
The ensemble forecasts operationally performed by
the Japan Meteorological Agency (JMA) with the highest
horizontal resolution are typhoon forecasts and 1 week
forecasts. However, these ensemble forecasts cannot represent convection cells of thunderstorms that cause local
heavy rainfall and tornadoes because the horizontal gridinterval (40 km) is too large to resolve the convection© 2015 Seko et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Seko et al. Progress in Earth and Planetary Science (2015) 2:42

cells. Recently, several ensemble forecast experiments
in which heavy rainfall or local heavy rainfall was reproduced have been performed with a local ensemble
transform Kalman filter (LETKF; Hunt et al. 2007;
Miyoshi and Aranami 2006). For instance, Kunii (2013)
reproduced a heavy rainfall in northern Kyushu by assimilating JMA’s operational observation data with a
LETKF system with a grid interval of 5 km. Probability
distributions for a 3 h rainfall amount exceeding 50 mm
were obtained by changing the initial time of the ensemble
forecasts. Heavy rainfall of the northern Kyushu heavy
rainfall event could be forecast with high probability evenwhen the initial time of the ensemble forecasts was 12 h
before the event.
One method of representing convection cells is toperform downscale forecasts with the model with a gridinterval of a few kilometers and to obtain the initialconditions by spatial and temporal interpolation of theanalyzed or predicted fields of a data assimilation system. For example, a local heavy rainfall, which caused aflash flood in the Toga River in Kobe that swept away
six people, was reproduced by downscale experimentswith JMA’s non-hydrostatic model (JMANHM), the initial fields of which were obtained with a LETKF system(Seko et al. 2011). Though the linear band of intense
rainfall responsible for the local heavy rainfall was represented as a weak rainfall region by the NHM-LETKF
system with a grid interval of 20 km, downscale simulations with a grid interval of 1.6 km were successful in reproducing the linear rainfall band. This result is one
example of a local heavy rainfall being reproduced bymodifying the environment around the rainfall systemthrough the assimilation of observed data.
A local heavy rainfall that occurred on the Osaka Plainon 5 September 2008 was reproduced by a nested
LETKF system (Seko et al. 2013). In this experiment,
high-resolution data (Doppler radar and GPS data) wereassimilated to reproduce the local heavy rainfall. The
number of ensemble members that reproduced the local
heavy rainfall was increased by the assimilation of highresolution data, compared with the number when theanalyzed fields were obtained with assimilating only
low-resolution conventional data. The results of these
ensemble experiments show that the use of a LETKF
system is a promising approach to the analysis and prediction of local heavy rainfall events.
In this study, we applied a LETKF system to the prediction of intense vortices associated with tornadoes. To
date, no numerical experiments have been conductedusing ensemble Kalman filters with a horizontal resolution of a few kilometers to reproduce intense vortices
associated with tornadoes occurring in Japan, although anumber of studies have used ensemble Kalman filters to
reproduce or to investigate tornadoes that occurred inPage 2 of 12

the USA. For instance, Snook et al. (2011) showed that it
was possible to obtain probabilistic forecasts by using an
ensemble Kalman filter analysis system that assimilated
Doppler radar data from the Collaborative Adaptive
Sensing of the Atmosphere and Weather Surveillance
Radar-1988 systems. Yussouf et al. (2013) conducted simulations with a mesoscale and convective scale ensemble
data assimilation system consisting of the Weather Research and Forecasting model and an ensemble adjustment
Kalman filter that used a double-moment microphysics
scheme, and they successfully reproduced a supercell
storm. Clark et al. (2013) investigated the relation between
the path lengths of tornadoes and simulated updraft
helicity by using an ensemble Kalman filter system with ahorizontal grid interval of 1.25 km. Recently, Snook et al.
(2015) demonstrated that use of an ensemble Kalman filter
system could provide skillful analyses and ensemble-based
probabilistic forecasts of tornadic mesoscale convective
systems in the USA.
Tornadoes in Japan are different from those in theUSA; Japanese tornadoes have an intensity equal to orless than F3 on the Fujita scale, and the low-level atmosphere in Japan is much more humid. Ensemble experiments to reproduce Japanese tornadoes are desirable for
improving the forecast accuracy of tornadoes in Japanand for understanding the generation mechanisms ofJapanese tornadoes.
We applied a nested LETKF system to a tornado eventon 6 May 2012, during which three tornadoes weregenerated on the Kanto Plain. To express the vortices
associated with these tornadoes, we performed downscaling experiments using the JMANHM model (Saito etal. 2006) with a horizontal grid interval of 350 m(NHM-350) and the analyzed fields produced by the
LETKF system. We expected the multiple possible scenarios obtained from these ensemble forecast experiments to
be useful for inferring environmental factors favoring thegeneration of intense vortices.
The rest of this paper is organized as follows: In the“Methods” section, the tornadoes of 6 May 2012 are first
briefly described, and then, the LETKF system anddownscale forecast experiments are explained. In the“Results and discussion” section, we present the resultsof the ensemble downscale experiments and discuss thefactors that favor the generation of the intense vortices
associated with tornadoes. In the “Conclusions” section,
we present our conclusions.

Methods
Observed features of the tornadoes of 6 May 2012

On 6 May 2012, three tornadoes occurred on the northern Kanto Plain in Japan. The surface weather map for
09:00 Japan Standard Time (JST = UTC + 9) on 6 May(Fig. 1a) shows that there was a low-pressure system

Seko et al. Progress in Earth and Planetary Science (2015) 2:42

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Fig. 1 a Surface weather map and b weather map at the height of 500 hPa at 09:00 JST on 6 May 2012. b Solid and broken contours indicate
height (m) and temperature (°C), respectively. Intervals of the solid and broken contours are 3 °C and 60 m, respectively

over eastern Russia and the Japan Sea. The presence ofthe low-pressure system over the Japan Sea caused theisobar contours over central Japan, including the Kanto
Plain, to be relatively densely spaced. As a result, strong
southerly winds were expected over the plain. At thesame time, a low-pressure system existed at the 500 hPa
height that was centered in eastern Russia, at the same
location as the center of the surface low-pressure system(Fig. 1b). Over the Japan Sea, there was a cold air mass
with temperature lower than −18 °C, which was associated with this high-level low-pressure system. This cold

air mass was moving southeast and approaching the
Kanto Plain.
At 12:00 JST, a cloud region, visible in a satellite image,extended from southwest to northeast over central Japan(Fig. 2a). Because this cloud region was located along theeastern edge of the cold air mass at the height of 500 hPa,
we deduced that it had been generated partly by the upper
cold air mass as it approached the Kanto Plain, thoughsurface temperature also contributed to the cloud generation. On the Kanto Plain, a brighter cloud area (A inFig. 2a) indicated the presence of intense convection cells.

Fig. 2 aVisible image acquired at 12:00 JST on 6 May 2012 by a geostationary meteorological satellite of the Japan Meteorological Agency (JMA).
b Rainfall intensity at 12:40 JST on 6 May 2012 observed by operational radars of JMA. The regions shown in Figs. 3 and 4 are indicated by thebroken line and solid line rectangles, respectively

Seko et al. Progress in Earth and Planetary Science (2015) 2:42

At 12:40 JST, the operational radars of JMA showed thatseveral rainfall regions (Fig. 2b), which corresponded tothe cloud region over central Japan observed by satellite,
extended northeastward from central Japan. In addition,
another rainfall region (region A) that extended northward from the Kanto Plain corresponded to the brightcloud area seen over the northern Kanto Plain. Rainfall intensity in the southern part of region A in some places
exceeded 80 mm h−1. These intense rainfall areas in region
A were moving northeastward.
JMA determined the paths of the tornadoes that weregenerated on 6 May from the damage left in their wake
(Japan Meteorological Agency, Meteorological ResearchInstitute, Tokyo District Meteorological Observatory and
Sendai District Meteorological Observatory 2012). Three
tornadoes were generated at around 12:30 JST over thenorthern Kanto Plain. The intensity of the southernmost
tornado was F3 on the Fujita scale, the highest intensityof tornadoes observed in Japan, and this tornado damaged about 800 houses, killed one person, and injured 37
people. The two northern tornadoes had intensities ofF2 and F1.
Temperature and surface horizontal winds observed
by JMA’s Automated Meteorological Data Acquisition
System (AMeDAS) showed that a warm southerly and acold northerly airflow converged over the northern
Kanto Plain on 6 May (Fig. 3). Rainfall regions in which
rainfall intensity exceeded 56 mm h−1 were located in anarea with a steep temperature gradient produced by theconvergence that extended from southwest to northeast
over the plain. This distribution indicates that these

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intense rainfall regions were produced by the convergence of the warm southerly flow and cold northerly
flow. Just after the outbreak of the tornadoes (12:30
JST), two distinct regions of intense rainfall were observed (I and II in Fig. 3b). Wind speeds of the southerly
flow east of these intense rainfall regions were markedly
higher than wind speeds in the surrounding area. The
tornado tracks (red lines in Fig. 3b) extended northeastward from near the southern edges of intense rainfall
regions I and II.
Because the southernmost tornado passed near Narita
city, the reflectivity and radial winds within the rainfall
region in which the southernmost tornado was generated were observed by the Doppler radar at Narita International Airport (red dot in Fig. 3). A pairing of intense
winds of opposite direction, indicating the mesocycloneof the southernmost tornado, was observed in afilament-like rainfall band (arrows in Fig. 4) at thesouthern end of the intense rainfall region. This configuration of a rainfall region with a tornado locatednear the filament-like rainfall band at its southern tip,
resembling configurations that have been observed in
the USA (figure 8.10 in Houze 1994), suggested thatthe rainfall system that generated the southernmost
tornado had a classic supercell structure.
The Meteorological Research Institute (MRI) has analyzed the environmental conditions when these tornadoes
were generated by using JMA’s operational mesoscale analysis data (Meteorological Research Institute 2012). The
water vapor distribution at 12:00 JST on 6 May showedthat a moist airflow supplied the intense rainfall regions

Fig. 3 Horizontal distributions of horizontal wind and temperature observed by AMeDAS at a 12:20 JST and b 12:30 JST on 6 May 2012 in thearea enclosed by the broken line rectangle in Fig. 2. Colors indicate rainfall intensity observed by the operational radars of JMA. The red lines
indicate the tornado tracks, as revealed by the damage they caused. The red dot in each panel indicates the position of the Narita radarSeko et al. Progress in Earth and Planetary Science (2015) 2:42

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Fig. 4 Distributions of a rainfall intensity and b radial wind at 12:36 JST in the area within the solid line rectangle in Fig. 2, observed by the Narita
airport radar (elevation angle 0.7°). The arrows indicate a filament-like rainfall band

where the tornadoes were generated. In particular, a moist
airflow with a water vapor mixing ratio that exceeded
12 g kg−1 contributed to the generation of these tornadoes(Meteorological Research Institute 2012). To investigate
the detailed structure of the convection cell that produced
the southernmost tornado, MRI conducted a deterministic
experiment using a model with a horizontal grid intervalof 250 m. Although this experiment reproduced only thesouthernmost tornado, it showed that the rainfall systemthat generated the tornadoes had a supercell structure. In
the present study, we performed ensemble experiments tofurther investigate these three tornadoes generated on thenorthern Kanto Plain.
Outline of the experiments performed with the nested
LETKF system

Downscale experiments were performed with NHM-350
to reproduce the intense vortices associated with thetornadoes. The initial conditions for NHM-350 were

Fig. 5 Schematic illustration of the nested LETKF system

obtained from outputs of a nested LETKF system (Sekoet al. 2013). The nested LETKF system (Fig. 5) was composed of two LETKF systems, each with 12 ensemble
members; the grid interval of the outer LETKF was
15 km and that of the inner LETKF was about 1.875 km.
The outer LETKF was used to reproduce the large-scaleconvergence by assimilation of part of JMA’s conventional data (horizontal wind, temperature, and relative
humidity data from upper air soundings and aircraft andwind profiler observations and surface pressure data recorded by meteorological observatories). The outer
LETKF domain consisted of 80 × 80 horizontal grids
covering Japan, except for Hokkaido and Kyushu, andthe domain of the inner LETKF, which was within theouter LETKF domain, consisted of 160 × 160 horizontal
grids covering the Kanto Plain. The inner LETKF gridinterval of 1.875 km was adopted so that the intense
rainfall regions that generated the tornadoes could be
represented. In both LETKFs, the number of verticalSeko et al. Progress in Earth and Planetary Science (2015) 2:42

layers was 50, and the layer depth stretched from 20 m
at the bottom to 890 m at the top of the domain. The
altitude of the top was 22.6 km. The assimilation window of the outer LETKF was 6 h, and the data assimilation slot interval was 1 h. To reduce the convergencetime required to get the analyzed fields of the outer
LETKF, initial seeds for the outer LETKF were obtained
at several prior points in time from the initial time ofthe outer LETKF from analyzed fields of JMA’s mesoscale model. Because analyzed fields were used to produce the boundary conditions of the outer LETKF, theresults of this study are, strictly speaking, not forecasts
but simulations of the tornadoes.
The assimilation window of the inner LETKF was 1 h,
and the slot interval was 10 min. In this study, conventional data (described above) from JMA were assimilated
in both LETKFs. Although higher resolution observation
data (e.g., radial wind from Doppler radars and GPS precipitable water vapor) are expected to be better able toreproduce smaller scale distributions of the initial conditions than conventional data, we did not use such highresolution data in this study because our main purpose
was to explore the merits of ensemble forecasts. Yokota
et al. (2015) have shown that the assimilation of highresolution data (e.g., radial winds from Doppler radars
and dense surface observation data of AMeDAS and theEnvironmental Sensor Network of NTT DOCOMO,
Inc.) could improve the positions of the reproduced vortices of the tornadoes of 6 May 2012. The analyzed fieldsof the inner LETKF were reflected to those of the outer
LETKF every 6 h, at which time the analyzed fields ofboth the inner and outer LETKFs were available. The
data assimilation cycles of the outer and inner LETKFs
began at 09:00 JST on 3 September and 03:00 JST on 6
September, respectively.
NHM-350, which performed the downscale experiments
to represent the intense vortices associated with the tornadoes, had 600 × 600 horizontal and 70 vertical grids, andits initial time was set to 10:30 JST on 6 September. The
initial and boundary conditions for NHM-350 were produced from the output of the inner LETKF. NHM-350
used a double-moment microphysics parameterization
scheme and the Deardorff (1980) planetary boundary
scheme.

Results and discussion
Intense vortices produced by the inner LETKF and NHM-350

NHM-350 successfully reproduced the rainfall regions
from 11:00 JST to 12:00 JST, which includes the period of
the intense vortices associated with the tornadoes (Fig. 6).
Because the rainfall regions were moving, the hourly rainfall region distribution extended northeastward over thenorthern Kanto Plain. An intense southerly flow from thesouth and a relatively weak flow under the relatively

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Fig. 6 The ensemble mean distributions of 1 h rainfall and horizontal
wind at the height of 20 m at 12:00 JST, analyzed by the inner LETKF. A
red arrow indicates the relatively intense rainfall region near which therelatively weak flow was generated

intense rainfall region (indicated by a red arrow in Fig. 6)
were seen to converge along the southern and easternedges of the rainfall regions. These distributions indicatethat the rainfall regions were generated by this convergence. In general, this relationship between the rainfall
regions and horizontal winds was the same as that observed, except that the northerly component of the airflow
reproduced on the north side of the convergence line wasmuch weaker than the observed airflow.
All 12 ensemble members of NHM-350 reproduced a
few rainfall regions at 12:00 JST that extended to thenorth-northeast (Fig. 7a). As mentioned above, theserainfall regions were moving northeastward. The positions of the rainfall regions and the rainfall intensities
varied among the ensemble members. Intense vortices,where vertical vorticity exceeded 0.1 s−1 at 12:00 JST(red dots in Fig. 7), were located near the southern tip of
the rainfall regions generated by ensemble members#000, #002, #003, #004, and #008; this position corresponds to that of the southernmost tornado generated inintense rainfall region I (Fig. 3). In #006, an intense vortex was generated near the southern tip of the northernrainfall region; this position corresponds to one of thenorthern tornadoes generated in the observed intense
rainfall region II (Fig. 3).
Ensemble members #004 and #007 generated thestrongest and weakest vorticities among the ensemble
members, respectively, at the height of 20 m. An intense vortex was generated near the southern tip of theintense convection by #004 at 11:45 JST (Fig. 7b),45 min earlier than the observed southernmost tornado
was generated, but only weak vorticity was generatedby #007 (Fig. 7c). The position of the southernmost
intense vortex relative to the rainfall region in #004 wasSeko et al. Progress in Earth and Planetary Science (2015) 2:42

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Fig. 7 a Rainfall regions at the height of 20 m at 12:00 JST reproduced by each of the 12 ensemble members of NHM-350. Enlarged views of the
distributions of rainfall and horizontal wind by b #004 and c #007. The red rectangles in a indicate the regions shown in b and c. The red dots
in a and b show the positions of vorticity exceeding 0.1 s−1 at 12:00 JST, and the red circle in b shows where convergence is occurring near thenorthern rainfall region. The thin contours are the stream lines of horizontal wind

the same as that of the observed vortex relative to theobserved rainfall region. This result indicates that #004
reproduced the observed rainfall region that generated
the tornado. An intense vorticity was not reproducedby #007, even though #007 produced more intense
rainfall than #004 did. The relationship between vorticity and rainfall intensity in #007 suggests that rainfall
intensity might not be a factor controlling the generation of intense vortices. The convergence reproducedby #004 at the southern edge of the northern rainfall region (red circle in Fig. 7b) corresponds to the northern intense vortex that was reproduced by #006 (Fig. 7a),though the vorticity generated by #004 was weaker than
0.1 s−1. The stream lines, which show the small-scale distributions of horizontal wind, indicate that in #004
(Fig. 7b) the airflow that converged at the position of thenorthern vortex passed near the southern rainfall region.
If the southern rainfall region modified the airflows thatwere supplied to the northern rainfall region, it would prevent the development of the northern rainfall region.
We investigated the airflows that entered the northernrainfall region by performing a back trajectory analysis(Fig. 8). The end points of the tracers were positioned
around the southern tip of the northern rainfall region
at 12:00 JST and traced backward in time for 90 min.

The tracers were assumed to move with the speed of thehorizontal wind at the height of 0.8 km, so that the pathsof the air inflows before their arrival at the northernrainfall region could be used to check the influence ofthe southern rainfall region on the northern vortices.
We chose ensemble members #002 and #006 for the
trajectory analysis because intense vorticity was generated near the northern rainfall region by #006 but not by#002 (Fig. 7).
Both members showed most airflows approaching thesouthern tip of the northern rainfall region from thesouth. In #002, the airflows passed the southern rainfall
region at 11:30 JST, and their potential temperature
decreased before their arrival at the northern rainfall
region. In #006, in contrast, some airflows passed far to
the east of the southern rainfall region, and theirtemperature did not decrease. After the passage of these
airflows from the east of the southern rainfall region,
their direction of movement was changed by the presence of low pressure near the rainfall region, causing
them to approach the southern tip of the northern rainfall region. That is, the southern rainfall region in #006 is
deduced to have a smaller effect on the airflows that were
supplied to the northern rainfall region. This difference ofthe paths, as a result, might be one reason why #006 wasSeko et al. Progress in Earth and Planetary Science (2015) 2:42

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Fig. 8 Rainfall regions at the height of 20 m at 11:30 JST (contours) and 12:00 JST (blue regions) reproduced by two NHM-350 ensemble members:
a #002 and b #006. The red dots in each panel show where vorticity exceeded 0.1 s−1 at 12:00 JST. The small dots show the trajectories traced
backward in time from positions around the southern edge of the northern rainfall region. The colors of the dots indicate the potential temperature at
the tracer positions. The black dots on the tracer paths show the tracer positions, from south to north, at 10:30, 11:00, 11:30, and 12:00 JST

able to generate intense vorticity near the northern rainfall
region. This relationship between the southern rainfall
and the intense vortex near the northern rainfall region
was obtained with the NHM-350 system. To confirm theexistence of a corresponding observed relationship, observation data of airflows within the lower atmosphere over
the Kanto Plain would be needed.
The intense vortices, with vertical vorticity larger than
0.1 s−1, reproduced by the 12 ensemble members from11:00 JST to 13:00 JST in the downscale experiments(colored dots in Fig. 9) occurred in the same three regions as the observed tornadoes (Fig. 3), though theprecise positions of the observed tornadoes were not
reproduced. The southernmost intense vorticity, which
was also reproduced by the deterministic forecast mentioned above, was generated by most of the ensemble
members. In fact, an intense vortex associated with thesouthernmost tornado was reproduced by 10 of the 12
ensemble members (thus, the occurrence probability of
an intense vortex was 83 %). The ensemble experimentalso reproduced the two northern intense vortices,though their generation probability was low. This result
indicates that probabilistic forecasts of intense vortices
associated with tornadoes are possible and that ensemble
forecasts can decrease the number of tornado outbreaks
that are missed. Among the ensemble members, thepaths of the southernmost intense vortex were clustered,
whereas those of the northern vortices were scattered.
This result indicates that the uncertainty of the paths
was larger in the case of the northern vortices than it
was in the case of the southernmost vortex. The ability
to obtain information on uncertainty is one of the meritsof ensemble forecasts mentioned in the “Background”
section.

Next, we compared the outputs of NHM-350 aroundthe mature vortices among the ensemble members toinvestigate the environmental factors favoring the generation of the intense vortices. We focused on the relationship between the durations of intense vortices and thevalues of certain environmental factors, because a tornado with longer duration causes damage over a wider
area (Fig. 10). Here, for each member, duration was defined as the period during which vorticities exceeded
0.1 s−1, and the environmental values (water vapor at
0.8 km height, vertical wind shear between the heights

Fig. 9 Positions of intense vortices reproduced by NHM-350. The
colored dots indicate points where ensemble members reproduced
vorticities exceeding 0.1 s−1. Contours indicate coastlines and land
elevations of 300 and 600 m, respectively

Seko et al. Progress in Earth and Planetary Science (2015) 2:42

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Fig. 10 Relationships between the duration of vortices (larger circles indicate longer durations) and environmental factors, obtained by NHM-350.
a The vertical shear of horizontal wind (ΔUV) between the heights of 0.8 and 2.5 km (horizontal axis) and low-level water vapor (Qv) at the heightof 0.8 km (vertical axis). b Storm relative helicity (SReH; horizontal axis) and low-level water vapor at the height of 0.8 km (vertical axis). c The number ofgrids in the averaged region where vertical shear 17 m s−1 (horizontal axis) and water vapor exceeded 12 g kg−1 (vertical axis). The averaged region
was a rectangle (6.3 km east–west by 8.75 km north–south) on the southeastern side of the southern rainfall region and moved with the rainfall regionof 0.8 and 2.5 km, and storm relative helicity (SReH))
are the spatial averages within a rectangle (6.3 km east–west × 8.75 km north–south) on the southeastern side of
the southern rainfall region at the time the vorticities
reached 0.1 s−1. This rectangle moved with the moving
speed of the southern rainfall region. SReH is defined by
the following equation:
SReH ¼

Z 3km 0

∂V ðzÞ ðV ðzÞ−C Þ ∙kdz∂z

ð1Þthresholds indicated that the favorable region for intense vortex generation within the averaged area, thatis, within the rectangle on the inflow side of the southern rainfall region, was larger. Therefore, these results
indicate that ensemble members reproducing large
areas with a favorable environment reproduce vortices
with a long duration. Thus, favorable environmental
factors can be extracted by comparing the outputs ofthe ensemble members.
Environmental factors affecting intense vortex generation

where V(z) is the horizontal wind vector at height z, k is
the vertical unit vector, and C is the moving vector of thestorm. To estimate SReH, the moving speed C of thesouthern rainfall region is needed. In this study, the moving speeds of the reproduced southern rainfall regions
were used. A scatter diagram of the durations of intense
vortices and the values of certain environmental factors
shows that the duration became longer as the low-level
layer became more humid and the vertical wind shear became larger (Fig. 10a). The duration also became longer
when the SReH in the averaged region was larger.
In addition, comparison of duration with the numberof grids in which water vapor and vertical wind shear in
the averaged area exceeded 12 g kg−1 and 17 m s−1, respectively (Fig. 10c), showed that the durations of theintense vortices became longer as the number of grids
exceeding these thresholds became larger. This result
was expected because the number of grids in which
values exceeded these thresholds was correlated positively with the averaged values shown in Fig. 10a. A
larger number of grids with values exceeding these

The temporal variations of intense vortices and the horizontal distributions of environmental factors (e.g., lowlevel water vapor and vertical shear of horizontal winds)
are examined in this section to show how environmental
factors affect intense vortex generation. Figure 11 shows
the temporal variations of maximum vorticity obtainedby NHM-350 within the whole domain of NHM-350
from forecast time (FT) = 0 min to 180 min. Because thevorticities of ensemble members #004 and #007 were
the strongest and weakest, respectively, we expected
them to provide information about the key factors that
affect the outbreak of intense vortices. To show the impacts of environmental factors on the intensity of vertical vorticities more clearly, we performed a series ofexperiments in which the initial conditions were produced by combining the initial conditions of #004 and#007 with different weights as follows:
I combined ¼ w  I 004 þ ð1−wÞ  I 007
where w is the weight of ensemble member #004.

ð2Þ

Seko et al. Progress in Earth and Planetary Science (2015) 2:42

Fig. 11 Temporal variations of maximum vorticity from FT = 0 min(10:30 JST) to 180 min (13:30 JST) reproduced by each ensemble
member of NHM-350. The last six columns show the results of aseries of experiments in which the initial conditions were producedby combining the initial conditions of #004 and #007 with different
weights and by replacing water vapor, horizontal wind, or potential
temperature in the initial conditions of #004-007_025 with thecorresponding values of #007

As w became smaller, the maximum vorticity generated
during FT = 0 min to 180 min became weaker (Fig. 11).
This result is expected because the impact of #004 is
reduced when w is smaller; the timing of the maximum
vorticity, however, did not change linearly from that of#007 as w decreased. When the weight was changed from0.25 (#004-007_025) to 0.125 (#004-007_0125), the vorticity decreased greatly. This large change of vertical vorticity suggests that the #004-007_025 experiment wasparticularly sensitive to environmental factors; therefore,
we expected this experiment to show the impact of environmental factors more clearly than the others. Thus, weinvestigated the impact of each environmental factor byreplacing water vapor, horizontal wind, or potential
temperature in the initial conditions of #004-007_025 withthose of #007.
When the horizontal wind or water vapor was replaced with those of #007 (#004-007_025_UV, #004007_025_QV), the vorticities became weaker. In contrast,
when the potential temperature was replaced with that of#007 (#004-007_025_PT), vorticity became more intense.
These results indicate that the horizontal wind and water
vapor in the initial conditions of #004 were favorable
for the generation of an intense vortex but that thetemperature distribution of #004 suppressed the generation of an intense vortex.
We therefore examined the horizontal distributions of
the vertical shear of horizontal winds and of the lowlevel water vapor at 11:40 JST (5 min before the outbreak of the reproduced southernmost intense vortex in#004) reproduced by #004 and #007 to see where thedifferences existed between them (Fig. 12). On the

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southeastern side of the rainfall regions, that is, on the
inflow side of the southernmost intense vortex, both#004 and #007 simulated an intense vertical shear region
where wind shear between the heights of 0.8 and 2.5 km
was larger than 12 m s−1 (circles in Fig. 12a, b). As thisregion of intense vertical shear moved northward andthe rainfall regions approached it from the west, the intensity of the vertical shear became larger. This regionin which vertical shear exceeded 12 m s−1, however, waslarger in #004 than in #007 (Fig. 12a, b). This result
suggests that the intensity of vertical shear affects theoutbreak of intense vortices, with the likelihood of an
outbreak increasing with the intensity of vertical shear.
The distributions of low-level water vapor show that ahumid region also existed on the eastern side of therainfall regions in both members #004 and #007 and that
more water vapor was supplied to the rainfall regions in#004 than in #007 (Fig. 12c, d). This difference in lowlevel water vapor between #004 and #007 might be one
reason why intense vorticity was not generated in #007.
The temporal variations of water vapor and vertical
shear of horizontal wind on the southeastern side of thesouthernmost intense vortex in #004 are compared withthose of vorticity in Fig. 13. The estimation region of thetemporal variations was the same as that used in producing Fig. 10. The water vapor supplied to the rainfall region gradually increased up until the outbreak of thesouthernmost intense vortex (outbreak time in #004,
11:45 JST), though there was a relatively small water
vapor period from 11:30 JST to 11:40 JST. The vorticity
became rapidly larger just after an abrupt increase in thevertical shear of the horizontal winds. These temporalvariations in water vapor and vertical shear show that, inthe case of this event, the intense vertical shear determined the timing of the outbreak of intense vorticity.

Conclusions
The intense vortices associated with tornadoes occurringon 6 May 2012 were reproduced by a nested LETKF system that simulated the environments and convection
cells simultaneously. The results of this study demonstrated three merits of ensemble forecasts based on the
outputs of the nested LETKF system. The occurrence
probability of an intense vortex with a vertical vorticity
exceeding 0.1 s−1 during this tornado event was 83 %.
This result shows the first merit: probabilistic forecastsof the outbreak of intense vortices associated with tornadoes are possible. In addition, the multiple possible scenarios revealed two other merits of ensemble forecasts.
Although a deterministic forecast had reproduced only
the southernmost of the three tornadoes, several ensemble members reproduced the intense vortices associatedwith the two northern tornadoes. Thus, the second meritis that ensemble forecasts can be expected to decreaseSeko et al. Progress in Earth and Planetary Science (2015) 2:42

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Fig. 12 Vertical shear of horizontal wind between the heights of 2.5 and 0.8 km at FT = 70 min (11:40 JST) in a #004 and b #007 and the horizontal
distribution of the water vapor mixing ratio at the height of 0.8 km at FT = 70 min (11:40 JST) in c #004 and d #007. The thick black contours indicate arain mixing ratio of 1.0 g kg−1. The circles indicate the area in which airflows were supplied to the rainfall regions

the miss rate of outbreaks because they can reproduce
low-probability phenomena. Comparison of the multiple
possible scenarios showed that the duration of thesouthernmost tornado was related to the low-level water
vapor and the vertical wind shear between heights of 0.8
and 2.5 km. Thus, the third merit is that the multiple
possible scenarios of the ensemble forecast make it possible to determine the environmental factors favorable
for the formation of severe weather phenomena such as
intense vortices associated with tornadoes.

Fig. 13 Temporal variations of vorticity, water vapor mixing ratio,
and vertical wind shear reproduced by #004 of NHM-350

Although we demonstrated the merits of ensemble forecasts in this paper, there are several areas where improvements are needed. First, the initial seeds used might
influence the probability of outbreaks of intense vortices.
In this study, the initial seeds of the outer LETKF were
provided by JMA’s mesoscale model analysis fields frombefore the occurrence of the observed tornadoes, not byadding random perturbations. In this experiment, differences among the initial seeds might have been too small
to cause dispersion of intense vortices to be large.
The second area is the number of ensemble members.
The merits of the ensemble forecasts of convective
scale phenomena, such as the possible distribution of
intense vortices, can be demonstrated with as few as 12
ensemble members. For quantitative discussion, however, more ensemble members would be preferable. In
this study, we investigated the relationships between
environmental parameters and intense vortices in eachensemble member, but we did not discuss the ensemble
mean distributions in depth. As shown in Fig. 3, a region of intense southerly winds existed east of the rainfall regions. Because of the small ensemble size, theensemble mean distribution, if it was produced by simple
averaging of the outputs of the ensemble members, would
show several separate regions of intense southerly winds;

Seko et al. Progress in Earth and Planetary Science (2015) 2:42

as a result, the relationships between the southerly wind
and the rainfall regions would be obscured. Therefore, therelationships in this study were extracted from the output
of each ensemble member. It would also be possible toextract these relationships from composite distributions
relative to the positions and timings of the occurrence ofintense vortices; this approach was discussed by Yokota etal. (2014).
The third area is the assimilation data of the inner
LETKF. High-resolution and high-frequency data areneeded to reproduce convection cells in rainfall regions.
Because such data were not used in this study, the intense vortices were not reproduced at the positions
where they were actually observed. For more realistic
reproduction of rainfall regions and tornadoes, highresolution and high-frequency data should be used.
Abbreviations
AMeDAS: Automated Meteorological Data Acquisition System; JMA: Japan
Meteorological Agency; JMANHM: Japan Meteorological Agency non-hydrostatic
model; JST: Japan Standard Time; LETKF: Local Ensemble Transform Kalman Filter;
MRI: Meteorological Research Institute; NHM-350: Non-Hydrostatic Model with agrid interval of 350 m.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
HS, TT, MK, and TM developed the nested LETKF system. HS performed theanalyses obtained from the nested LETKF and downscale experiments. HS
and SY developed the interpretations. All authors read and approved thefinal manuscript.
Acknowledgements
The authors express their gratitude to Drs. Kazuo Saito, Teruyuki Kato, and
Wataru Mashiko of MRI, to Mr. Hiroshi Yamauchi of JMA, and to two
anonymous reviewers for valuable comments. Mesoscale analysis data andconventional observation data from JMA were used to produce the initialseeds and boundary conditions of the outer LETKF and in the data
assimilation. “Kasaneru 3D” and “Multi-screen monitoring tool” were used
to produce Figs. 2, 4, and 5. The authors extend their gratitude to theTokyo District Meteorological Observatory and the Observation
Department and Numerical Prediction Division of JMA. This study was
supported in part by “Strategic Programs for Innovative Research (SPIRE),
Field 3 (ID: hp120282, hp130012, hp140220)” and “Tokyo Metropolitan Area
Convection Study for Extreme Weather Resilient Cities (TOMACS).”
Science section to which this research article belongs: 2) Atmospheric andhydrospheric sciences
Author details
Meteorological Research Institute, Japan Meteorological Agency, 1-1
Nagamine, Tsukuba, Ibaraki 305-0052, Japan. 2Japan Agency for Marine-Earth
Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama,
Kanagawa 236-0001, Japan. 3RIKEN Advanced Institute for Computational
Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047,
Japan. 4Meteorological College, 7-4-81 Asahicho, Kashiwa, Chiba 277-0852,
Japan.

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1

Received: 22 April 2015 Accepted: 5 November 2015

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