Computationalcience All
SforK computer Newsletter
November 2016

13No.

A Major Advance
Made in Forecasting Local Downpours
Big data assimilation is going to revolutionize
weather prediction

A Major Advance
Made in Forecasting Local Downpours
Big data assimilation is going to revolutionize weather prediction

Takemasa Miyoshi
Team Leader of the Data Assimilation Research Team, RIKEN AICS

Unexpected torrential rain in local areas can be disruptive and hazardous. Until recently, timely
and accurate forecasting of local storms had not been possible. But by integrating the large amountof observed meteorological data that advanced phased-array weather radar can provide with accurate simulation data processed on the K computer, one of the worldʼs fastest supercomputers, Takemasa Miyoshi and his team have achieved some impressive results in forecasting local downpours.
Miyoshiʼs advanced forecasting method is called big data assimilation, and he believes it could
revolutionize the way local storms are forecast. This new cross-disciplinary approach makes it possible to update the forecast of torrential rain at extremely fast 30-second intervals in high resolutionover grid spacings of 100 meters.

Fig. 1 Simulation results using big
data assimilation (grid spacing of100 meters)
In the bottom section, segments in
red depict heavy rain, and those inblue light rain. Clouds are simulated
based on phased-array radar data on
precipitation distribution and winds.
The top section shows the cloudʼssurface, while in the bottom section,
the cloudʼs overall perspective isdepicted.

Simulation

Observation

What is data
assimilation?
Data
assimilation

Miyoshi, who previously worked for theJapan Meteorological Agency, describes
data assimilation in simple terms as feeding various data from real-world observations into computers to produce realistic
simulations ( Fig. 2).
Weather forecasting is performed
based on data obtained by observing thestate of the atmosphere at a certain point
in time. The data are used as the initialvalues for simulating future atmosphericstates by means of a numerical prediction
model. The simulations then serve as thebasis for forecasting atmospheric conditions on a long-term basis.
However, the longer computer simulations take to complete, the wider
becomes the gap between predictedvalues and real-world observation data.
To rectify this and to improve prediction
accuracy, a sequential time-stepping procedure called data assimilation is usuallyemployed. The procedure constantly
modiﬁes the predicted values based on
the changing observed data so as to provide updated values for further simulation. By modifying predictions in this way,
forecasting accuracy has been greatlyimproved.

K computer plus
phased-arrayweather radar
Improvements in computer performance in recent years have extended thescope of and raised the accuracy of simulations. At the same time, rapid advancesin sensor technology have led to moredetails being obtained from meteorological observations.
“As a researcher of data assimilation, Iʼmthrilled at the thought of what can now
be done when the best possible simulation is combined with state-of-the-art
real-world observations,” says Miyoshi.
“Better simulations and more accurate
observations are both praiseworthy inand of themselves. But when combined,
they can take weather prediction to thenext level. And itʼs our goal to make thishappen.”
Miyoshi has focused much attention onobservation data provided by phased-array weather radar used to detect localized
downpours and tornadoes. A given radar
installation can sweep an area of up to 60

Fig. 2 Data assimilation
orld observations and computer simulations. The accuracy ofData assimilation serves as a bridge between real-world
ety of observation data is used to help produce more realistic
weather forecasting is greatly improved when a variety
simulations by means of numerical prediction models.

km in radius and as high as 14 km in
altitude at intervals of 30-seconds, thenprovide the three-dimensional distribution of rain drops across grid spacings of
100 meters. This helped the researchers
detect and depict the ﬁrst signs of rainthrough to its passing. Miyoshi hypothesized that by integrating this large
amount of detailed data with the simulations performed on the K computer alsousing grid spacings of 100 meters, theaccurate prediction of localized downpours would be possible.

Forecasting local
downpours

Intense, localized storms have been on
the rise in recent years. They threatenpeopleʼs lives and can cause natural disasters. Consequently, the public is demanding more reliable forecasting to predict
such storms.
Localized heavy rain tends to beaccompanied by a cumulonimbus cloud,which forms and develops rapidly̶often
within a few minutes. As a result, rain suddenly pours down over a particular area.
Because the JMAʼs current operational
forecasting employs simulations based on
2-kilometer-grid spacing with hourly data
assimilation, it is far from eﬀective indetermining the spatial and temporal patterns of localized storms.
In 2016, Miyoshi developed a prototype
system aimed at predicting localized
storms̶a worldʼs ﬁrst. The system has
been tested using a couple of retrospective cases, one example being a stormthat lashed Kobe City in Hyogo Prefecture,

on September 11, 2014. Fig. 3 shows partof the data describing the storm.
The picture top left shows the result ofobservations produced by the phased-array radar. The other three pictures show
simulation results produced by the K
computer. These simulations employed
the meteorological data collected immediately before the symptoms of the storm
were apparent as initial values, and thenthe simulations were performed over a
period of 30 minutes covering the stormʼsprogress.
The bottom left picture shows theresults obtained without data assimilation. No rain clouds are displayed. The
bottom right picture shows the results ofdata assimilation of a 1-kilometer-gridspacing updated at 30-second intervals.
Rain clouds can be seen, but they are
diﬀerent in structure from the observation data.
The picture top right gives the results ofbig data assimilation. Here the observation data̶obtained from a 100-meter-grid spacing updated at 30-second
intervals̶were used. This reproduces
clearly the observation data. Assimilationof data from such a narrow grid spacingat such fast intervals is unprecedented.
“Assimilation of data from a 1-kilometerspacing every several minutes has been
regarded as cutting-edge research until
now,” says Miyoshi. “So weʼve realized an
outstandingly high level of big data
assimilation.”
He added, however, “I was fortunate
enough to have had access to the K computer and to the data from phased-arrayradar at the same time. Not many
researchers pursuing big data assimilation will be so lucky.”Revolutionizing
weather prediction?

Miyoshiʼs research ﬁndings on localized-storm forecasting were publishedin the August 2016 issue of the. It
appeared under the title.
Regarding the title, Miyoshi says heexpected the journalʼs editors andreviewers to object to the use of “revolutionizing,” as the term might be considered an exaggeration. But it was cleared
for publication. “Their approval testiﬁes
to the importance of our ﬁndings as astep towards high-speed, high-accuracy
weather prediction, which will ultimately revolutionize forecasting,” he says
(Fig. 1).
Two major hurdles still have to be
overcome before the system can beconsidered practical: calculation speeds
and prediction accuracy both need to
be greatly improved.
As real-world atmospheric observations are updated every 30 seconds, aseries of processing steps̶feeding theobserved data into the computer andintegrating it with the ongoing simulations̶ needs to be completed withineach 30-second sequence. Currently,
this takes as long as several minutes toexecute, while Miyoshi is looking for anMiyoshi joined the Japan Meteorological Agency(JMA) after graduating from university as a physics
major. While still at JMA, he went to study in theUS, and in just two years obtained an M.S. andPh.D. in meteorology at the University of Maryland. Later, he assumed the post of AssistantProfessor at that university after quitting JMA. In2013, he came back to Japan to perform data
assimilation on the K computer.

about 10-fold improvement. “It once
took an hour or more, but weʼve
reduced it to several minutes,” says Miyoshi. “And we have ideas for furtherimprovement.”
To achieve such a speed gain, he iscollaborating with AICS colleagues whoare conversant with the K computer,and who know how best to use it toperform such simulations. “By exploiting
their competence, Iʼm sure we can get
the process completed within 30 seconds,” says Miyoshi.
As for prediction accuracy, theresearchers are exploring ways to prevent a sharp fall oﬀ in accuracy ﬁve minutes after the process of simulation
begins. In part, this is due to the numerical prediction models used. These weredeveloped for simulations based on agrid spacing of several kilometers. So
they do not work well for 100-meter-grid-based simulations.
Miyoshi is currently testing a newnumerical model called SCALE developed by an AICS Computational Climate
Science research team led by Hirofumi
Tomita. Miyoshi is impressed by SCALEʼscore concept, which targets high-resolution simulations. Tomitaʼs team is also
helping to upgrade the process of data
assimilation.
“As these researchers share the samebuilding as us, this helps make AICS anoutstanding research environment,”
says Miyoshi.

Data assimilation isa rich ﬁeld for
future research
Miyoshi notes that the ﬁeld of data
assimilation is short of researchers. Thisis largely because few universities andcolleges oﬀer courses related to thesubject in Japan. But he stresses thatdata assimilation can be applied to all
ﬁelds that employ simulation̶not justweather forecasting. In particular, hebelieves big data assimilation will
become important in better utilizing big
data in the future. “So I hope morescientists will take it up,” he says. “Itʼs arich and rewarding area to conduct
research.”Fig. 3 Distribution of rain clouds over Kobe
City (at 8:25 a.m. on Sept. 11, 2014)
With big data assimilation (using a grid spacing of 100 meters), real-world observations
can be seen in the microstructure of a cumulonimbus cloud together with the distribution of precipitation. Standard data assimilation (grid spacing of 1 km) is not suitable forsuch full observation-data reproduction.
Segments in red show heavy rain and thosein blue light rain.
Top-left: Phased-array radar observation data
Bottom-left: Simulation results without data
assimilation
Top-right: Simulation results with big data assimilation (grid spacing of 100 meters
updated at 30-second intervals)
Bottom-right: Simulation results with dataassimilation (grid spacing of 1kilometer updated at 30-second intervals)

RIKEN Advanced Institute for Computational Science (AICS)
7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan
TEL: + 81 78 940 5555 FAX: + 81 78 304 4964 E-mail: aics-koho@riken.jp http://www.aics.riken.jp/en/