JAXA Supercomputer System Annual Report April 2017-March 2018

Numerical Analysis of aerodynamic characteristics on H3 launch vehicle
Report Number：R17EK2300
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4453/
Responsible Representative
Masashi Okada, Space Technology Directorate 1, H3 Projcet Team

Contact Information
Hiroshi Ikaida ikaida.hiroshi@jaxa.jp

Members
Hiroshi Ikaida, Kazuhiro Imai, Fumihiro Itoo

Abstract
From the viewpoint of international competitiveness, develop a new launch system with low cost andhigh performance.

Reasons for using of JSS2
Aerodynamic data and load analysis resuls are necessary to confirm the feasibility in the H3 launchvehicle critical design phase. Therefore, we obtain aerodynamic characteristic data by CFD using JSS2.
In the past, aerodynamical development was dependet on databases, but due to technologicalinnovation, highly reliable caluculation using CFD became possible. Consequently, computational
approch is applied in this project.

Achievements of the Year
Under the ground wind environment at launch site, it is confirmed by the wind tunnel test that thevariable air force acting on the aircraft is affected by the launch facility due to the wind direction. Basedon these wind test results, design work for improving wind resistance is underway. Because of the
progress of detailed design, due to the change of the mast shape and the addition of the mast attachment,
the unsteady CFD analysis was carried out and the influence on H3 rocket variable air force wasconfirmed.
As an analysis case, we conducted seven cases of three-dimensional CFD and eight cases of semi-twodimensional analysis using wind direction / mast shape as a parameter. From the unsteady pressure
fluctuation data, fluctuating aerodynamic force at the time of change of shape / wind direction was
acquired and its influence was evaluated.

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.1 Cp distribution at test

Fig.2 Cp distribution at flightJAXA Supercomputer System Annual Report April 2017-March 2018

Fig.3 Velocity distribution at test

Fig.4 Velocity distribution at flightJAXA Supercomputer System Annual Report April 2017-March 2018

Publications
N/A

Usage of JSS2
 Computational Information
Parallelization Methods

MPI

Thread Parallelization Methods

Automatic Parallelizatio

Number of Processes

960

Elapsed Time per Case

170.00 hours

 Resources Used
Fraction of Usage in Total Resources*1 (%): 1.50
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

12,599,432.26

1.67

SORA-PP

18,825.57

0.24

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

254.63

0.18

/data

6,978.99

0.13

/ltmp

4,296.88

0.32

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

0.50

0.02

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Numerical Simulations for H3 Rocket Development
Report Number：R17EK2302
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4454/
Responsible Representative
Masashi Okada, Space Technology Directorate, H3 Project Team

Contact Information
Hideyo Negishi negishi.hideyo@jaxa.jp

Members
Seiji Tsutsumi, Masashi Kanamori, Hiroaki Amakawa, Masaaki Ino, Hironori Fujiwara,
Masaharu Abe, Hiroyuki Ito, Shinji Ohno, Takenori Nakajima, Hideyo Negishi, Yu Daimon,
Takanori Haga, Taro Shimizu, Junya Aono, Tetsufumi Ohmaru

Abstract
Please refer to the following URL: http://global.jaxa.jp/projects/rockets/h3/

Reasons for using of JSS2
Risk management is the key to make a success of a large-scale development project like H3 rocket. Thisis because its development schedule and cost could be significantly influenced by the face of imminent
risks when new technologies are under system-level verification and validation. In order to complete
the project under defined period and cost, a variety of efforts are essential for planning and process oftechnology development itself in terms of efficiency and certainty. In that context,

numerical

simulation technologies and JSS2 have been playing one of major roles to make the H3 project a success.

Achievements of the Year
With regard to the booster engine, 'LE-9', under detailed design phase, evaluation of design and risks
as well as studies for improvement were carried out by making full use of JSS2.

JAXA Supercomputer System Annual Report April 2017-March 2018

Publications
 Presentations
1) Funo, Y., et al., "Progress Report of Deatiled Design of H3 rocket," 40th Space Development andUtilization Division of Research and Development Bureau in MEXT(the Ministry of Education,
Culture, Sports, Science and Technology), Jan. 24th, 2018.
2) Azuma, N., et al., "Development Status of LE-9 Engine Turbopumps," AIAA Propulsion and Energy
Forum, 53rd AIAA/SAE/ASEE Joint Propulsion Conference, Atlanta, G.A., July 10-12, 2017.

Usage of JSS2
 Computational Information
Parallelization Methods

MPI

Thread Parallelization Methods

N/A

Number of Processes

2 - 2500

Elapsed Time per Case

300.00 hours

 Resources Used
Fraction of Usage in Total Resources*1 (%): 7.19
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

60,494,643.13

8.00

SORA-PP

6,871.84

0.09

SORA-LM

220.72

0.11

SORA-TPP

53,598.70

0.29

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

10,303.37

7.14

/data

69,123.46

1.28

/ltmp

5,110.81

0.39

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE104.20

4.48*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

ALOS/PALSAR Data Processing, Archiving and Distributing
Report Number：R17ER0100
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4455/
Responsible Representative
Shin-ichi Sobue, ALOS-2 Project, Space Technology Directorate I

Contact Information
Osamu Ochiai ochiai.osamu@jaxa.jp

Members
Shigeru Iguchi, Hidetoshi Hayasaka, Akiko Otomo, Takashi Goto, Osamu Ochiai,
Takashi Asama, Keiko Ishii, Hidenori Sakamoto, Jumpei Tsutsumi, Yukie Nakagawa

Abstract
Process ALOS/PALSAR data to Analysis Ready Data (ARD) and distribute to users.
http://global.jaxa.jp/projects/sat/alos2/index.html

Reasons for using of JSS2
In order for enhancing availability of JAXA's Earth observation data as Open and Free, it is necessary
to process the ALOS/PALSAR data to Analysis Ready Data (ARD) and make them available to users as
quick as possible.

Achievements of the YearIn this Fiscal Year 2017, a feasibility study on transmitting some sample ALOS/PALSAR Level 0 datafrom Tsukuba Space Center to JSS2 and also processing the data to Level 1.0 and 1.5 in SORA-PP has
successfully conducted.
In next Fiscal Year 2018, whole ALOS/PALSAR Level 0 data (700TB) will be transmitted andprocessed to Analysis Ready Data (ARD). The ARD will be archived in JSPACE and will be able toretrived and distributed to users.

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.1 ALOS/PALSAR Data Processing Archiving and Distributing - System Overview

Fig.2 ALOS/PALSAR Data Processing Archiving and Distributing - Data Interface

Fig.3 ALOS/PALSAR Data Processing Archiving and Distributing - Processing Module

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.4 ALOS/PALSAR Data Processing Archiving and Distributing System
Overview - Sample Processed Image

Publications
N/A

JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

N/A

Thread Parallelization Methods

N/A

Number of Processes

1

Elapsed Time per Case

2.00 hours

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.00
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

0.00

0.00

SORA-PP137.340.00

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

305.18

0.21

/data

11,196.14

0.21

/ltmp

14,648.44

1.10

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

0.26

0.00

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Earth observation satellite data processing for GPM/DPR
Report Number：R17ER0200
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4456/
Responsible Representative
Ichiro Naito, Satellite Applications and Operations Center(SAOC)

Contact Information
GPM Operation Officer GPM-MOS@ml.jaxa.jp

Members
Yuichiro Kitayama, Toshiyuki Konishi, Takahiro Minami, Masaya Torii, Tadahiro Yamamoto,Koki Ishimaru, Sachiko Kawase, Tomohiro Katoh, Mika Tajima, Masaki Takahashi

Abstract
In recent years, worldwide interest has been increasing about the necessity of grasping the global
environmental change. To deal with such problems, various approaches using observation technology
from space have been carried out by satellites.
Global Precipitation Measurement (GPM) mission, as follow-on and expansion of Tropical Rainfall
Measurement Mission (TRMM) satellite, is an international mission to achieve highly accurate andfrequent global rainfall observation. it is carried with multiple satellite, GPM core satellite with Dualfrequency Precipitation Radar (DPR) jointly developed by JAXA and NICT, and with GPM Microwave
Imager (GMI) developed by NASA, and another constellation satellites with Microwave Imager.
In addition, accumulation of long-term data is important to understand long-term climate change on a
global scale. It is important to be able to use the data of the TRMM satellite operated from 1997 to 2015
as well as the data of the GPM satellite.
http://global.jaxa.jp/projects/sat/gpm/index.html

Reasons for using of JSS2
Processing of earth observation data includes "routine processing" performed routinely and "reprocessing" performed once a year or so for several year data. The purpose of re-processing is tocorrespond with version-up of computing model and algorithm performed periodically. The amount ofobservation data grows year by year. Then, we need more and more time to complete reprocessing of allarchived observation data. By using supercomputers, the calculation time is greatly shortened, and It is
possible to provide products quickly to users.

JAXA Supercomputer System Annual Report April 2017-March 2018

Achievements of the Year
Version 8 of TRMM's data processing algorithm which contained 2 improvement has been released.
one is the continuity of between data of TRMM satellite operated for 17 years from 1997 to 2015 anddata of GPM satellite, another is unification of product format. In JFY 2017, we conducted reprocessingof TRMM's Level 1 product which is received power of precipitation radar for 17 years using
supercomputer(JSS2). By shortening the processing time, it was possible to release the product from therelease of the algorithm to the release of the product in a short period of about 2 months including the
preparation period. Higher order processing after Level 2 which is a physical quantity conversion of
precipitation radar will be implemented in fiscal 2018.
The period of reprocessing done in JSS 2 is as follows.
period of reprocessing : 1997/12/8 - 2015/4/1
CPU usage time : Approx. 44.1 hours
Number of products : L1B : 98,827 products
Total Output Product Capacity : L1B: 11.38TB

Fig.1 TRMM data of tropical - storm MAN-YI(16 W)(typhoon 18)(2013/09/12 - 2013/09/16)

Publications
N/A

JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

N/A

Thread Parallelization Methods

N/A

Number of Processes

1

Elapsed Time per Case

44.10 hours

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.13
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

0.00

0.00

SORA-PP

69,219.68

0.87

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

077.88

0.05

/data

116,699.27

2.16

/ltmp

9,505.21

0.72

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

3.61

0.16

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Research for data assimilation of satellite global rainfall map
Report Number：R17ER0201
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4457/
Responsible Representative
Kinji Furukawa, GPM/DPR Project teamContact Information
Takuji Kubota kubota.takuji@jaxa.jp

Members
Shunji Kotsuki, Koji Terasaki, Takemasa Miyoshi, Kaya Kanemaru, Masaki Satoh, Takuji Kubota

Abstract
This study explores an effective use of satellite data including GSMaP and GPM/DPR through anadvanced ensemble data assimilation method for improving numerical weather prediction (NWP) andpioneering a new precipitation product based on an NWP model and satellite observations.
http://www.eorc.jaxa.jp/GPM/index_e.htm

Reasons for using of JSS2
In this study, the JSS2 is used for the NICAM-LETKF experiments to assimilate satellite observations
and to conduct NWP model forecasts. The JSS2 is a necessary infrastructure for our study to conduct
massive computations for the ensemble-based data assimilation and ensemble atmospheric simulations.

Achievements of the Year
(1) Assimilating GPM/DPR reflectivity into NICAM-LETKF
This study pioneers to assimilate radar reflectivity measured by the Dual-frequency Precipitation
Radar (DPR) onboard the Global Precipitation Measurement (GPM) core satellite into NICAM. We
conduct the NICAM-LETKF experiments at 28-km horizontal resolution with explicit cloud
microphysics of a single-moment 6-class bulk microphysics scheme. To simulate GPM/DPR reflectivity
from NICAM model outputs, the joint-simulator (Hashino et al. 2013; JGR) is used. Our initial tests
were promising, showing a better match with the observed reflectivity by assimilating GPM/DPR
reflectivity (Fig. 1).

JAXA Supercomputer System Annual Report April 2017-March 2018

(2) Assimilating Microwave Humidity Sounder (MHS) into NICAM-LETKF
Microwave Humidity Sunder (MHS) observations in the clear sky condition are assimilated into the
NICAM-LETKF. Without the MHS, the NICAM-LETKF assimilates very few humidity observations
over the ocean. Assimilating the MHS generally improved temperature, wind and humidity fields (Fig.
2).
(3) The near-real-time system of the NICAM-LETKF
The near-real-time system of the NICAM-LETKF was installed on JSS2. The near-real-time NICAMLETKF system has been stably running over 6 month, with a delay of 8 hours relative to the real time.
The process includes pre-process and post-process (e.g. visualization) on JSS2 (Fig. 3).
(4) Ensemble forecast experiments with a high-resolution NICAM
Ensemble forecasts are performed with a high-resolution (14-km) NICAM from analyses of the
NICAM-LETKF. Here, we focused on a heavy rain event occurred at July 2017 in Kyushu. Sensitivity
experiments to the initial time revealed that the heavy rain would be predictable one-day before theheavy rain event by the NICAM (Fig.4).

Fig.1 Spatial patterns of column maximum Ku-band radar reflectivity (dBZ) for (a)first guess, (b) analysis, and (c) observation at 0000UTC, June 17, 2014.

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.2 Time series of change in root mean square difference (RMSD) relative to the
ERA-Interim reanalysis for (a) temperature (K), (b) mixing ratio (g kg-1),
and (c) zonal wind (m s-1). The vertical and horizontal axes show thepressure level (hPa) and date-mont

Fig.3 An example of automatically-generated monitor of the NICAM-LETKF
output on at 12 UTC, January 14, 2018. Left, middle, and right panels show
the 6-hour precipitation [mm d-1] for the GSMaP observation, first guess
and analysis, respectively.

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.4 Time series of accumulated precipitation (blue boxes; mm h-1), andprecipitable water (red and purple lunes; mm) at the Northern Kyushu
region. Red lines represent forecasts by the 14-km NICAM initialized by(top) 00 UTC, July 3, (middle) 00 UTC, July 5,

Publications
 Peer-reviewed papers
1) Kotsuki S., Ota Y., and Miyoshi T., 2017b: Adaptive covariance relaxation methods for ensemble
data assimilation: Experiments in the real atmosphere. Quarterly Journal of the Royal
Meteorological Society, 143, 2001-2015. doi: 10.1002/qj.3060.
2) Terasaki, K., and T. Miyoshi, 2017: Assimilating AMSU-A Radiances with the NICAM-LETKF.
Journal of the Meteorological Society of Japan, 95, 433-446, doi:10.2151/jms.j2017-028.
 Invited Presentations
1) 2017/04/07

T. Miyoshi, "Data Assimilation Research at RIKEN: Toward extreme-scalecomputation", An examination of data assimilation algorithms, observations, and applications in thecontext of next-generation computing, National Science Foundation, Arlington, VA, USA.
2) 2017/05/22 K. Terasaki, T. Miyoshi, "Reconditioning the observation error covariance matrix ina local ensemble transform Kalman filter: experiments with Lorenz-96 model", JpGU, Makuhari

JAXA Supercomputer System Annual Report April 2017-March 20183) 2017/07/24 T. Miyoshi, "Data Assimilation: Integrating model and measurements", South-East
Asian School on Tropical Atmospheric Science (SEASTAS), Singapore
4) 2017/08/08 Takemasa Miyoshi, Shunji Kotsuki, Koji Terasaki, Keiichi Kondo, Guo-Yuan Lien,
Masaki Satoh,hirofumi Tomita, Eugenia Kalnay, "Enhancing Data Assimilation of GPM
Observations", Asia Oceania Geosciences Society (AOGS) 14th Annual Meeting, Singapore
5) 2017/09/11

Takemasa Miyoshi, "Data Assimilation Research at RIKEN: Numerical Weather

Prediction and Beyond", Atmosphere-ocean Joint Seminar, Univ. Tokyo.
 Oral Presentations.
1) 2017/04/27 S. Kotsuki, K. Terasaki, H. Yashiro, H. Tomita, M. Satoh, and T. Miyoshi, “Model
Parameter Estimation Using Ensemble Data Assimilation: A Case with the Nonhydrostatic
Icosahedral Atmospheric Model NICAM and the Global Satellite Mapping of Precipitation Data”,
The EGU General Assembly 2017, Vienna
2) 2017/05/21 S. Kotsuki, K. Terasaki, H. Yashiro, H. Tomita, M. Satoh, and T. Miyoshi, “Model
Parameter Estimation Using Ensemble Data Assimilation: A Case with the Nonhydrostatic
Icosahedral Atmospheric Model NICAM and the Global Satellite Mapping of Precipitation Data”,
JPGU-AGU Joint Meeting 2017, Chiba
3) 2017/07/19 Kotsuki S. and Miyoshi T., “Leading Research on Data Assimilation in Numerical
Weather Prediction”, AICS CAFE, 2017.
4) 2017/09/12

Kotsuki S., Terasaki K., and Miyoshi T., “Ensemble-Based Data Assimilation of

GPM/DPR Reflectivity into the Nonhydrostatic Icosahedral Atmospheric Model NICAM”, 7thInternational WMO Symposium on Data Assimilation, Florianópolis, Brazil
5) 2018/03/05

Kotsuki S., Terasaki K., and Miyoshi T.: Ensemble-Based Data Assimilation of

GPM/DPR Reflectivity into the Nonhydrostatic Icosahedral Atmospheric Model NICAM. 6th
International Symposium on Data Assimilation, Ludwig-Maximilians-Universitat, Munich,Germany6) 2017/10/26

金丸佳矢,佐藤正樹,寺崎康児,小槻峻司,三好健正,久保田拓志「JSS2 を用いた

NICAM-LETKF の準リアルタイム処理のシステム開発および利用」超高解像度大気モデル開発ワークショップ(NICAM 開発者会議 2017),淡路 Poster Presentations
1) 2017/05/20 K. Terasaki, S. Kotsukji, T. Miyoshi, "Two-year analysis experiments with NICAMLETKF", JpGU-AGU Joint Meeting 2017, Makuhari2) 2017/09/11

Kotsuki S., Ota Y., and Miyoshi T., “Adaptive covariance relaxation methods for

ensemble data assimilation based on innovation statistics”, 7th International WMO Symposium on
Data Assimilation
3) 2018/02/07 Terasaki, K., and Miyoshi T., Accounting for the observation error correlation in data
assimilation. AICS international symposium

JAXA Supercomputer System Annual Report April 2017-March 20184) 2018/02/08

Kotsuki S., Kurosawa K., and Miyoshi T.: Ensemble Forecast Sensitivity to

Observations Verified with Multiple References. The 8th AICS International Symposium, RIKENAICS, Kobe, Japan5) 2018/02/19

Kotsuki S., Kurosawa K., and Miyoshi T.: Ensemble Forecast Sensitivity to

Observations Verified with Multiple References. RIKEN International Workshop on Uncertainty
Quantification (UQWS), RIKEN-AICS, Kobe, Japan6) 2018/03/05 Kotsuki S., Greybush S. J., and Miyoshi T.: On the assimilation order of the serial
ensemble Kalman filter: A study with the Lorenz-96 model. 6th International Symposium on Data
Assimilation, Ludwig-Maximilians-Universitat, Munich, Germany7) 2018/03/05

Kotsuki S., Kurosawa K., and Miyoshi T.: Ensemble Forecast Sensitivity to

Observations Verified with Multiple References. 6th International Symposium on Data Assimilation,
Ludwig-Maximilians-Universitat, Munich, Germany8) 2017/10/17 Kaya Kanemaru, Masaki Satoh, Koji Terasaki, Shunji Totsuki, Takemasa Miyoshi,
and Takuji Kubota : A Near-Real-Time System of NICAM-LETKF Assmilating GSMaP, 2017 PMM
Science Team Meeting, San Diego URLs for the Research Results on the Web1) http://www.data-assimilation.riken.jp/nicam-letkf/index.html
2) http://cesdweb.aori.u-tokyo.ac.jp/~nicam/index-e.html

JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

MPI

Thread Parallelization Methods

OpenMP

Number of Processes

2 - 640

Elapsed Time per Case

10.00 hours

 Resources Used
Fraction of Usage in Total Resources*1 (%): 6.38
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

50,782,484.98

6.70

SORA-PP

28,731.17

0.36

SORA-LM

66.93

0.03

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

1,449.59

1.00

/data

557,022.35

10.30

/ltmp

9,765.63

0.74

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

18.41

0.79

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Precise Orbit Determination by using MADOCA on JSS2
Report Number：R17ER0800
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4458/
Responsible Representative
Koichi Inoue, Space Technology Directorate I, Satellite Navigation Unit

Contact Information
Hiroshi Takiguchi takiguchi.hiroshi@jaxa.jp

Members
Hiroshi Takiguchi

Abstract
Satellite Navigation Unit has been generating the precise orbit and clock products of GNSS satellites
by using MADOCA (Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis), andbeen distributing to user via network routinely. On the JSS2, we aim to realize fast computation for the
long-term data analysis and simulation.
https://ssl.tksc.jaxa.jp/madoca/public/public_index_en.html

Reasons for using of JSS2
To improve of the MADOCA products accuracy, we need to do long-term data analysis. By using JSS2,
we have been expecting the reduction of the data analysis time.

Achievements of the YearIn this financial year, we just installed and tested the MADOCA. And, we confirmed that the test result
was agreed with the result which calculated on the general-purpose PC in the permissible range.

Publications
N/A

JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

N/A

Thread Parallelization Methods

OpenMP

Number of Processes

1

Elapsed Time per Case

20.00 minutes

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.02
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

0.00

0.00

SORA-PP

97.52

0.00

SORA-LM

4.08

0.00

SORA-TPP

14,251.68

1.59

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

095.37

0.07

/data

2,489.09

0.05

/ltmp

976.560.07

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

0.00

0.00

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Research of Tropical Rainfall Measuring Mission (TRMM)/ Precipitation Radar (PR)
Report Number：R17ER1400
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4459/
Responsible Representative
Teruyuki Nakajima, Earth Observation Research Center

Contact Information
Takuji Kubota kubota.takuji@jaxa.jp

Members
Naofumi Yoshida, Yoriko Arai, Tomohiko Higashiuwatoko,Takuji Kubota

Abstract
Calculation of the global rainfall map derived from Tropical Rainfall Measuring Mission (TRMM)
during a period from 2000 to 2014 using the GSMaP algorithm (V07).
Checks of consistency between the TRMM Precipitation Radar (PR) algorithm (V08) and the
GPM/DPR algorithm (V05) on June 2014 when both TRMM and GPM missions were in operation.
http://www.eorc.jaxa.jp/TRMM/index_e.htm

Reasons for using of JSS2
The JSS2 is necessary for calculation of the long-term data which consists of multiple satellites andsensors for the precipitation measurement with earlier computational times for algorithm evaluations,improvements, and long-term production. Because of the complexity of the processing algorithms, strictbusiness progress management, emergency response, detailed user response by the operation side, etc.
are required. When we do not use the JSS2, it can be said that reprocessing in a short period cannot be
achieved.

Achievements of the YearTRMM/PR algorithms (V08 level2&level3) and GPM/DPR algorithms (V05 level2&level3) on June2014 were processed by 8 times to check of consistency between TRMM/PR and GPM/DPR associatedwith the algorithm version update.
Figure 1 shows a zonal mean of surface precipitation using the most recent algorithms. Top panel is a
result over ocean and land. Bottom left and right panels are results over ocean and land, respectively. It
suggests that precipitation observed by PR and DPR/Ku are very close, though DPR/Ku&Ka is slightJAXA Supercomputer System Annual Report April 2017-March 2018

heavy over Ocean. The MMR and MML modules of the GSMaP algorithm (V7) were reprocessed forfrom 2000 to 2014. Machine learning aimed at improving the accuracy of GSMaP was performed by
MATLAB.

Fig.1 It is a zonal mean of surface precipitation using the TRMM/PR and GPM/DPR
algorithms. Top panel is a result over ocean and land. Bottom left and right panels are
results over ocean and land, respectively.

JAXA Supercomputer System Annual Report April 2017-March 2018

Publications
 Peer-reviewed papers
1) K. Kanemaru, T. Kubota, T. Iguchi, Y. N. Takayabu, and R. Oki, 2017: Development of a
precipitation climate record by spaceborne precipitation radar. Part I: Mitigation in effects ofswitching to redundancy electronics in the Tropical Rainfall Measuring Mission Satellite
Precipitation Radar, J. Atmos. Oceanic Technol., vol. 34 No. 9, 2043-2057.
2) S. Kida, T. Kubota, S. Shige, and T. Mega, 2017: Development of a Rain/No-Rain Classification
Method over Land for the Microwave Sounder Algorithm, Remote Sensing of Aerosols, Clouds, andPrecipitation, Chapter 12, 249-265.
 Oral Presentation
1) T. Kubota et al., Recent progress in Global Satellite Mapping of Precipitation (GSMaP)product,
JpGU-AGU Joint Meeting 2017, 20 May 2017, Makuhari-Messe, Chiba.
2) T. Kubota et al. Recent progress in Global Satellite Mapping of Precipitation (GSMaP)product,
2017IGARSS, July 26 2017, Fort Worth, TX, US
3) T. Kubota, 2018: Global Satellite Mapping of Precipitation(GSMaP) overview, The 7th Global
Precipitation Measurement (GPM) Asia Workshop on Precipitation Data Application Technique,
Jakarta, Indonesia, Jan. 2018.
4) T. Kubota, 2018: JAXA's GSMaP Overview, SEMDP Workshop, Jakarta, Indonesia, Mar. 2018.
 URLs for the Research Results on the Web1) http://sharaku.eorc.jaxa.jp/GSMaP/JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

N/A

Thread Parallelization Methods

N/A

Number of Processes

1

Elapsed Time per Case

24.00 hours

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.20
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

0.00

0.00

SORA-PP

127,152.31

1.59

SORA-LM

7,366.04

3.79

SORA-TPP

37,548.09

4.19

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

063.58

0.04

/data

69,726.34

1.29

/ltmp

13,020.84

0.98

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

33.45

1.44

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Utilization of JSS2 for AMSR-E L1 data processing
Report Number：R17ER1500
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4460/
Responsible Representative
Teruyuki Nakajima, Derector of The Earth Observation Research Center

Contact Information
Kazuyoshi Nakamura nakamura.kazuyoshi@jaxa.jp

Members
Susumu Saitoh, Makoto Imanaka, Kazuyoshi Nakamura

Abstract
AMSR-E*1 can estimate various geophysical parameters by measuring radio waves in the weak
microwave band radiated from the Earth's surface and the atmosphere with multiple frequencies andmultiple polarizations.
Geophysical parameters include water vapor, cloud liquid water, precipitation, sea surface temperature,sea surface wind speed, sea ice concentration, snow water equivalent, and soil moisture.
The long-term geophysical record will play an important role in climate change monitoring and will
provide valuable information for understanding the Earth's climate system, including water and energy
circulation.
By reprocessing the AMSR-E product in the same format as AMSR2*2

which is succeeding sensor,

we provide a long-term data set useful for users.
*1 : Advanced Microwave Scanning Radiometer for EOS equipped in Earth Observation Satellite 'Aqua'*2 : The Advanced Microwave Scanning Radiometer 2 equipped in Global Change Observation Mission - Water"SHIZUKU" (GCOM-W)

http://sharaku.eorc.jaxa.jp/AMSR/index.html

Reasons for using of JSS2
By using JSS2 with a large scalability processing environment and executing processing of enormous
scenes in parallel, it becomes possible to provide products to users more quickly.

Achievements of the YearIn this fiscal year, we improved the results such as bias correction of brightness temperature andreprocessed products(L1B, L1R) in all observation periods (2002/06/01 - 2011/10/04). The product is

JAXA Supercomputer System Annual Report April 2017-March 2018

under registration in the data provision system, and it is released as soon as registration is completed.
The period of reprocessing done in JSS 2 is as follows.
period of reprocessing : 2017/08/08 - 2017/09/15, 2017/11/09
CPU usage time : Approx. 730 hours
Number of products : L1B : 98268 products, L1R : 98268 products
Total Output Product Capacity :L1B : 5.3TB, L1R : 5.3TB*3 : Product storing the auxiliary data (geometric information, radiometric information, land/sea flag and supplemental
data), and the brightness temperature obtained by converting the engineering value acquired by the observation
device using the radiometric correction coefficient.
*4 : Products that store resampled data of brightness temperature data in products of L1B. The resampling brightness
temperature is processed to match the difference of resolution in each frequency.

Publications
N/A

JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

N/A

Thread Parallelization Methods

N/A

Number of Processes

1

Elapsed Time per Case

23.10 seconds

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.07
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

0.00

0.00

SORA-PP

8,731.85

0.11

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

115.00

0.08

/data

82,897.13

1.53

/ltmp

12,858.08

0.97

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

0.49

0.02

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Utilization of JSS2 for AMSR-E higher level data processing
Report Number：R17ER1501
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4461/
Responsible Representative
Ichiro Naito, Derector of The Satellite Applications and Operation Center

Contact Information
Kazuyoshi Nakamura nakamura.kazuyoshi@jaxa.jp

Members
Kazuyoshi Nakamura, Yuji Taniguchi, Makoto Imanaka

Abstract
AMSR-E*1, AMSR2*2 estimates various geophysical parameters by measuring radio waves in the weak
microwave band radiated from the Earth's surface and the atmosphere with multiple frequencies andmultiple polarizations.
Geophysical parameters include water vapor, cloud liquid water, precipitation, sea surface temperature,sea surface wind speed, sea ice concentration, snow water equivalent, and soil moisture.
The long-term geophysical record will play an important role in climate change monitoring and will
provide valuable information for understanding the Earth's climate system, including water and energy
circulation.
We will reprocess and provide AMSR-E high level product and AMSR2 high level product with thesame algorithm (including improved algorithms), and provide a long-term data set useful for users.
*1 : Advanced Microwave Scanning Radiometer for EOS equipped in Earth Observation Satellite 'Aqua'*2 : The Advanced Microwave Scanning Radiometer 2 equipped in Global Change Observation Mission - Water"SHIZUKU" (GCOM-W)

Reasons for using of JSS2
By using JSS2 with a large scalability processing environment and executing processing of enormous
scenes in parallel, it becomes possible to provide products to users more quickly.

Achievements of the YearIn this fiscal year, the improvement result of the algorithm shown in Table 1 was reflected in the
reprocessing environment. We also conducted reprocessing of L2*3 and L3*4 products during theobservation period that had not been conducted last year.(AMSR2 : 2014/07/01 - 2016/12/31)

JAXA Supercomputer System Annual Report April 2017-March 2018

The product is under registration in the data provision system, and it is released as soon as registrationis completed.
*3 : Product storing information on geophysical quantity which calculated through the physical quantity estimation
algorithm. And the product is stored the position information of the data and the information on the quality.
Note that map projection of the geophysical quantity is not performed.
*4 : Product mapped with time and spatial average values of geophysical quantities. The product is stored the daily
average and monthly average global distribution of the brightness temperature and each physical quantity.

Fig.1 Reprocessing Product List

Publications
N/A

JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

N/A

Thread Parallelization Methods

N/A

Number of Processes

1

Elapsed Time per Case

5.00 seconds

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.10
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

0.00

0.00

SORA-PP

21,975.67

0.28

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

148.48

0.10

/data

105,152.11

1.94

/ltmp

10,384.12

0.78

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

0.49

0.02

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Greenhouse gases Observing SATellite (GOSAT) mission
Report Number：R17ER2100
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4462/
Responsible Representative
Masakatsu Nakajima, Space Technology Directorate I, GOSAT-2 Project Team

Contact Information
Yoko UEDA ueda.yoko@jaxa.jp

Members
Makiko Hashimoto, Kenji Kowata, Hideki Suetake, Hisanori Tanaka, Yuuji Shinotsuka,
Hiroshi Suto, Makoto Imanaka, Yoshifumi Yamamoto, Akiko Otomo, Yoko Ueda,
Tomoo Yamasaki, Shigeru Iguchi, Takashi Goto, Hidetoshi Hayasaka, Mamoru Sugawara

Abstract
GOSAT observe carbon dioxide and methane - two major green-house effect gasses - from space, thenwe obtain better understanding in the science of behavior of these gasses. The knowledge serves as abasis for devising global warming countermeasure means.
http://www.eorc.jaxa.jp/GOSAT/index.html

Reasons for using of JSS2GOSAT mission utilizes JSS2 as one of the GOSAT Mission Operation System which processes theobservation data of GOSAT. When processing algorithm is updated, JSS2 reprocesses all data observedin the past. When we calibrate products, JSS2 processes all data in the past. Also, JSS2 is used as aremote storage of all data required for its reprocessing.
As the reprocessing targets of GOSAT products extends to all data observed in the past, more computerresources (core, memory, storage, etc.) are required than in the real-time processing.
It is necessary to use JSS2 to shorten the reprocessing time and to provide the reprocessing products
to GOSAT users more quickly.

Achievements of the Year
(1) GOSAT TANSO-FTS Level-1 Reprocessing
We reprocessed 7 year TANSO-FTS data by utilizing JSS2 (30nodes) to correspond with next versionup evaluation sample (V204204,V205205). It took only 20 days, and we could verify speed-up more

JAXA Supercomputer System Annual Report April 2017-March 2018

than 30 times comparing to one year by using the conventional GOSAT L1 processing computers.
(2) Calibration and validation of GOSAT TANSO-CAI data
On-orbit sensor sensitivity change is evaluated from TANSO-CAI observation data. We expect todetermine the CAI Band 1 radiometric calibration factor (or formula) by calculation of radiative transfer
model using in-situ and other satellite data. The CAI Band 1 has an important information of aerosol
size distribution. Hence, Band 1 radiance is optimized to agree with calibrated radiances of Band 2 andBand 3. Calibration formula of Band 2 and Band 3 are obtained as three candidates. Thus, Band 1
calibration factor is determined by calculations in these three cases. Figure 1 shows a schematic flow of
the CAI radiometric calibration method. Figure 2 shows the radiometric calibration factors.
Data period: June 2017 - February 2018 (every 4 months)(3) Research processing of GOSAT TANSO-FTS thermal infra-red data
Level 2 algorithm is improved for utilization of TANSO-FTS thermal infra-red (TIR) data. After
implementation of TIR Level 2 programs to JSS-2 system and re-processing of TIR Level 2 product, weknow the processing performance of JSS-2.
(i)Implementation of TIR Level 2 programs(a) Ozone (O3) profile
Processing performance of the TIR Level 2 ozone algorithm is 125 sec for one sounding data at JSS2.
We processed the TIR Level 2 ozone products by using the L1B V205.205 for RA users equivalent toV210.210 for general users from February to December 2015, and every September from 2009 to 2017,
totally 19 months. The ozone products, which contains amounts of a total ozone and a troposphericozone (from surface to about 10 km altitude) were evaluated in temporal variation. We confirmed theozone retrieval result by using L1B V205.205 had an equivalent accuracy with by using L1B V161.161.
Figure 3 shows a global map of the tropospheric ozone by the GOSAT observation in September 2017.

Fig.1 GOSAT CAI processing flow (top) and radiometric calibration flow (bottom)

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.2 CAI radiometric calibration factor by inter-satellite cross calibration method

Fig.3 GOSAT FTS TIR tropospheric ozone retrieved by using the L1B v205 in September 2017.

Publications
 URLs for the Research Results on the Web1) https://data2.gosat.nies.go.jp/index_en.html (Release of V210.210 FTS L1B product)

Usage of JSS2
 Computational Information

JAXA Supercomputer System Annual Report April 2017-March 2018

Parallelization Methods

N/A

Thread Parallelization Methods

N/A

Number of Processes

1

Elapsed Time per Case

480.00 hours

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.58
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

0.00

0.00

SORA-PP531,325.48

6.65

SORA-LM

0.00

0.00

SORA-TPP

3,109.17

0.35

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

951.49

0.66

/data

278,380.84

5.15

/ltmp

50,618.51

3.82

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

4.12

0.18

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Atmospheric Environmental Monitoring Simulation
Report Number：R17ER2401
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4463/
Responsible Representative
Teruyuki Nakajima, Earth Observation Research Center

Contact Information
Maki Kikuchi kikuchi.maki@jaxa.jp

Members
Kentaroh Suzuki, Daisuke Goto, Maki Kikuchi, Tomoki Fujiwara, Masamitsu Hayasaki, Tie Dai,
Yueming Cheng

Abstract
This project is aimed at development of an integrated system of numerical modeling and satellite
observation of air pollution for its reliable monitoring and prediction. For this purpose, seamless
atmospheric modeling system is operated on JSS2 to conduct simulations of air pollution transport overEast Asia. The results are also validated against satellite observations of aerosols.

Reasons for using of JSS2
In the project, we need to conduct numerical simulations with high spatial resolutions over wide area
including Japan and East Asia for reliable monitoring and predictions of air pollutions over the region.
This requires us to exploit high performance super computing resources of JSS2 available at JAXA.

Achievements of the YearIn this fiscal year, we have continued operations of simulations with the NICAM-SPRINTARS modelthat has been installed on JSS2 last fiscal year. Regional simulations have been conducted over Japan area
to investigate how aerosols are transported in the area. The simulation results were validated with
Himawari-8 satellite observations in the form of the joint probability distribution functions of aerosol
optical thickness (AOT), which depicts statistical evaluations of the model against satellite data (Fig. 1).
Also shown in the figure are the isolated probability distribution functions of model-based and satellitebased AOTs separately, together with quantitative numbers for mean, skewness and kurtosis of AOTs.
These comparisons expose how the model tends to overestimate AOTs relative to satellite observations
over the target area and how the statistical behaviors obtained from high spatiotemporal resolutions data
are different in the model from satellite measurement. Such a comparison enables us to better evaluate

JAXA Supercomputer System Annual Report April 2017-March 2018

and constrain the model in the context of its representation of detailed characteristics of aerosol transport
processes through evaluations with unprecedentedly high spatiotemporal resolution measurements
available from Himawari-8. The model-satellite synergy has further been explored in this fiscal year
through testing data assimilation experiments. Namely, the Himawari-8 satellite-derived AOTs wereassimilated into NICAM-SPRINTARS to better predict the aerosol transport characteristics over theJapan area. An example of the results is shown in Fig. 2, which describes how the simulations withassimilation better represents the time series of AOT at a particular location (Osaka in this case) against
AERONET ground-based measurements, compared to those without assimilation. The assimilation
results were also evaluated at a broader spatial scale as shown in Fig. 3 in the form of the assimilation
efficiency that quantifies how much error reduction occurs. The figure illustrates that time series of
AOTs at multiple locations are consistently improved through assimilation. These results demonstrate
how the aerosol modeling system is now appropriately operating in JSS2 and how it is unified with
Himawari-8 satellite measurements through assimilations for better predictions of aerosol pollutions
over Japan area.

Fig.1 Joint probability distribution function (PDF) of aerosol optical thickness (AOT)
obtained from the NICAM-SPRINTARS model (ordinate) and the Himawari-8
satellite observations (abscissa) shown in color, and each PDF separately for themodel and satellite ob

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.2 Time series of AOT at Osaka obtained from NICAM-SPRINTARS model simulations
with and without assimilation of Himawari-8 data. Black circle indicates the
AERONET ground-based measurements of AOT.

Fig.3 Assimilation efficiency of aerosol optical thickness obtained from the assimilation
experimentwith

NICAM-SPRINTARS

againstAERONETground-based

measurements. The red color indicates that the assimilation reduces the model error
relative to the ground-bas

JAXA Supercomputer System Annual Report April 2017-March 2018

Publications
 Presentations
1) Goto D., Kikuchi M., Suzuki K., Hayasaki M., Yoshida M., Nagao T., Sugimoto N., Shimizu A.,
Nakajima T. (2017) Model evaluation using a geo-stationary satellite and in-situ measurements
around Japan in May 2016, 2017 American Geophysical Union (AGU) Fall Meeting, New Orleans,
USA, December 2017

Usage of JSS2
 Computational Information
Parallelization Methods

MPI

Thread Parallelization Methods

N/A

Number of Processes

32 - 160

Elapsed Time per Case

3.50 hours

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.06
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

254,366.99

0.03

SORA-PP

2,020.05

0.03

SORA-LM

0.02

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

1,645.09

1.14

/data

26,248.94

0.49

/ltmp

12,695.32

0.96

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

0.00

0.00

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Study on development of satellite-based ocean data assimilation system
Report Number：R17ER2402
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4464/
Responsible Representative
Teruyuki Nakajima, Space Technology Directrate I, Earth Observation Research Center

Contact Information
Misako Kachi kachi.misako@jaxa.jp

Members
Misako Kachi, Tsutomu Hihara, Hidenori Aiki, Yasumasa Miyazawa, Shun Ohishi

Abstract
To enable satellite information ore familiar and easy to utilize, we develop "ocean forecast" without
missing area by using the ocean model and data assimilation system with the highest spatial resolution(about 3km) in Japan to utilize satellite-based ocean, snow and ice products produced by JAXA at the
maximum. At the same time, we promote studies on climate variations, with a focus on polar region, toimprove accuracy of prediction of global warming and evaluation of its impacts.
http://www.eorc.jaxa.jp/en/earth_observation_priority_research/ocean/Reasons for using of JSS2
The data assimilation system and model cannot execute in Linux computers generally used at EORC,
since the regional ocean models are high spatial resolution (3km), satellite data is assimilated to thesystem daily, and forecast three-dimensional ocean physical parameters about 10-80 days in future.
Therefore, we need super computer to do this study.

Achievements of the Year
In JFY2017, we had installed the satellite sea surface temperature (SST) data assimilation system andthe 3-km high-resolution ocean model over the south of Japan, which was developed by JAXA andJAMSTEC, and constructed the system for operation on JSS2. In this fiscal year, we implemented
experiments to assimilate several satellite SSTs, AMSR2 on GCOM-W, AHI on Himawari-8, and
MODIS on Aqua and Terra, with 20 ensemble members to calculate variation of the Kuroshio Current
at the sea surface and undersea over the south of Japan. Fig. 1 shows impacts of each SST to results ofdata assimilation. Different seasonal biases in each satellite SST (MODIS has negative bias in summer,

JAXA Supercomputer System Annual Report April 2017-March 2018

Himawari-8 has positive bias in summer, and AMSR2 is neutral over the year) used in data assimilation
have influenced to model simulated sea temperature profiles in summer. To avoid this impact, we
improved the system to reduce biases between satellite SSTs just before data assimilation.
Furthermore, we developed movies to show temporal variation of the Kuroshio Current and its large
meander, which has started since the end of September 2017, under collaboration with JAXASupercomputer Division. Fig.2 shows daily ocean variation over the south of Japan from August to
September 2017, and ocean current is shown in vector and strong current area is denoted by color. Largemeander of the Kuroshio in the end of September is clearly seen in the south of Kanto area.
We also applied this 3km resolution ocean model and data assimilation system to the area around
Thailand Bay under collaboration with Nagoya University. We also installed a coarse (1/12 degree)
resolution extensive ocean model over the Western Pacific region to provide boundary conditions to the
3km resolution model and started research to use simulation results to respond issues of environmentand disasters in the Asia-Oceania region. Fig.3 is animation of Thailand Bay area with 3km resolution
model SST assimilated satellite SSTs (left) and 2km resolution Himawari-8 SST (right). Upwelling (lowtemperature indicated by blue color) around the east coast of Indochina Peninsula can be demonstrated
by model output.
Promotion of satellite data assimilation to model will provide continuous ocean environmental dataset
without missing by satellite observations and/or including geophysical parameters that cannot be
observed from satellites directly. We expect to utilize the dataset to ocean environmental monitoring,fisheries, and ship navigations.

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.1 Differences caused by different satellite-based sea surface data. (a) Impact of MODISin Jan-Apr. 2017, (b) same as (a) but in May-Aug. 2016, (c) impact of AMSR2 in Jan.Apr. 2017, (d) same as (c) but in May-Aug. 2016, (e) impact of Himawari-8 in Jan.Apr

Fig.2 Simulation of daily ocean variation over the south of Japan from Aug. to Sep. 2017.

JAXA Supercomputer System Annual Report April 2017-March 2018

Fig.3 Movie of sea surface temperature by ocean model (left) and satellite (right)
around Thailand Bay in Aug. 2015.

Publications
 Peer-reviewed papers
1) Miyazawa, Y., S. M. Varlamov, T. Miyama, X. Guo, T. Hihara, K. Kiyomatsu, M. Kachi, Y. Kurihara,
H. Murakami, "Assimilation of high-resolution sea surface temperature data into an operational
nowcast/forecast system around Japan using a multi-scale three dimensional variational scheme",
Ocean Dynamics, 67, 713-728, 2017.
 Presentations
1) Hihara, H.,

Y. Miyazawa, T. Miyama, M. Kachi, H. Murakami, Y. Kurihara, N. Ono, H. Aiki,"Constructing an ocean data assimilation product using satellite sea surface temperature", JpGU,
Makuhari, Japan, May 2017.
2) Hihara, H., Y. Miyazawa, T. Miyama, M. Kachi, H. Murakami, Y. Kurihara, N. Ono, H. Aiki,"Constructing an ocean data assimilation product using satellite sea surface temperature", 18-th
International GHRSST Science Team Meeting, Qingdao, China, June 2017.
3) Kachi, M., H. Murakami, M. Kikuchi, T. Kubota, R. Oki, T. Nakajima, "Current and Future Statusof the JAXA's Missions and Earth Observation Priority Researches", EUMETSAT Meteorological
Satellite Users Conference 2017, Rome, Oct. 2017.
4) Kachi, M., M. Kikuchi, T. Kubota, R. Oki, M. Hori, H. Murakami, T. Nakajima, "JAXA Himawari
Monitor and its Synergies to Earth Observation Missions", 8th Asia Oceania Meteorological Satellite
Users Conference, Vladivostok, Oct. 2017.
5) Hihara, H., Y. Miyazawa, T. Miyama, M. Kachi, H. Murakami, Y. Kurihara, "Ocean data assimilation
experiments using high resolution satellite sea surface temperature around Japan", 2018 Ocean
Sciences Meeting, Portland, US, Feb. 2018.
6) Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, M. Kachi, "Investigation of Himawari-8
SSTs for oceanic data assimilation off Thailand/Vietnam", 2018 Ocean Sciences Meeting, Portland,
USA, Feb. 2018.

JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

MPI

Thread Parallelization Methods

Automatic Parallelizatio

Number of Processes

20 - 32

Elapsed Time per Case

50.00 minutes

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.72
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

5,534,231.01

0.74

SORA-PP

0.00

0.00

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

042.92

0.03

/data

83,866.16

1.55

/ltmp

8,789.07

0.66

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

0.00

0.00

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year

JAXA Supercomputer System Annual Report April 2017-March 2018

Greenhouse gases Observing SATellite-2 (GOSAT-2) Project
Report Number：R17ER3500
Subject Category：Space Technology
URL：https://www.jss.jaxa.jp/ar/e2017/4465/
Responsible Representative
Takeshi Hirabayashi, GOSAT-2 Project Team, Space Technology Directorate I

Contact Information
Shin Ishida ishida.shin@jaxa.jp

Members
Yoko Ueda, Tomoo Yamasaki, Yoshiyuki Hirosaki, Yuki Kobayashi, Taro Makino,Atsuhiko Murakami, Takaaki Nishino, Kouhei Sugimoto, Yugi Yata, Shin Ishida,
Kenji Kowata, Takehito Yoshida, Fumie Kataoka

Abstract
GOSAT-2 project retrieve and estimate the global concentration distribution of major greenhousegases including the sources and natural absorbers with high level of accuracy to contribute toenvironmental administration as follows.
- Improved precision of climate change predictions- Early detection of changes in the Earth system- Better understanding of emission reduction level of the anthropogenic greenhouse gases andchanging natural sink
- Contribution to air pollution monitoring policies
Also, GOSAT-2 project researches and develops new earth observation technologies required for futureearth observing satellites.
http://global.jaxa.jp/projects/sat/gosat2/index.html

Reasons for using of JSS2GOSAT-2 project utilizes JSS2 as one of the GOSAT-2 Mission Operation System which processes theobservation data of GOSAT-2. When processing algorithm is updated, JSS2 reprocesses all data observedin the past. Also, JSS2 is used as a remote storage of all data required for its reprocessing.
As the reprocessing targets of GOSAT-2 products extends to all data observed in the past, morecomputer resources (core, memory, storage, etc.) are required than in the real-time processing.
It is necessary to use JSS2 to shorten the reprocessing time and to provide the reprocessing products
to GOSAT-2 users more quickly.

JAXA Supercomputer System Annual Report April 2017-March 2018

Achievements of the YearIn the process of manufacturing and testing of the GOSAT-2 MOS (Mission Operation System), we
completed the following test with JSS2.
(1) Interface test between GOSAT-2 MOS and JSS2
To confirm the interface specification which is defined in ICD between GOSAT-2 MOS and JSS2.
(2) Reprocessing test with JSS2
To confirm the reprocessing function of GOSAT-2 MOS can be worked with JSS2 as system
requirement.
To confirm the reprocessing performance of GOSAT-2 MOS with JSS2.

Publications
N/A

JAXA Supercomputer System Annual Report April 2017-March 2018

Usage of JSS2
 Computational Information
Parallelization Methods

N/A

Thread Parallelization Methods

N/A

Number of Processes

1

Elapsed Time per Case

5.00 minutes

 Resources Used
Fraction of Usage in Total Resources*1 (%): 0.09
Details
Computing Resources
System Name

Amount of Core Time (core x hours)

Fraction of Usage*2 (%)

SORA-MA

0.00

0.00

SORA-PP635.790.01

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage assigned(GiB)

Fraction of Usage*2 (%)/home

163.72

0.11

/data

100,930.58

1.87

/ltmp

33,528.66

2.53

Archiver Resources
Archiver System Name

Storage used(TiB)

Fraction of Usage*2 (%)

J-SPACE

0.01

0.00

*1 Fraction of Usage in Total Resources: Weighted average of three resource types (computing, file system,
and archiver)*2 Fraction of Usage: Percentage of usage relative to each resource used in one year