JAXA Special Publication JAXA-SP-19-003E

Study on development of satellite-based ocean data assimilation system
Report Number: R18ER2402
Subject Category: Space Technology
URL: https://www.jss.jaxa.jp/en/ar/e2018/9161/
Responsible Representative
Naoto Matsuura, Director, Space Technology Directrate I, Earth Observation Research Center
Contact Information
Misako Kachi (kachi.misako@jaxa.jp)
Members
Misako Kachi, Tsutomu Hihara, Hidenori Aiki, 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) inJapan to utilize satellite-based ocean, snow and ice products produced by JAXA at the maximum. At the sametime, we promote studies on climate variations, with a focus on polar region, to improve accuracy of prediction of
global warming and evaluation of its impacts.
Ref. URL: https://www.eorc.jaxa.jp/en/earth_observation_priority_research/ocean/Reasons for using JSS2
The data assimilation system and model cannot execute in Linux computers generally used at EORC, since theregional ocean models are high spatial resolution (3km), satellite data is assimilated to the system daily, andforecast three-dimensional ocean physical parameters about 10-80 days in future. Therefore, we need supercomputer to do this study.
Achievements of the Year
Under collaboration with Japan Agency for Marine Earth Science and Technology (JAMSTEC), we improved
and reconstructed the high-resolution ocean data assimilation system for South of Japan, which had been
constructed until last FY, with Nagoya university. We have changed the forcing data to run the ocean model fromUS National Weather Service (NOAA/NCEP) reanalysis data to JMA’s 55-year Reanalysis (JRA55) data, andincorporated flesh water flux data using Macro-scale Floodplain model (CaMa-Flood) river discharge data andGlobal Satellite Mapping of Precipitation (GSMaP) rain data. Furthermore, we have incorporated IncrementalAnalysis Updates (IAU) to make the assimilation system using Local Ensemble Transform Kalman Filter(LETKF) stable. Through this reconstructing, we recognized that this system became easier to transfer the otherenvironment and to change the calculating domain because this system was packaged.
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In addition, Under collaboration with Nagoya University, we have incorporated assimilation of satellite sea
surface salinity and freshwater fluxes (evaporation, precipitation, and river discharge) into a one-way nest highresolution ocean data assimilation system (large-scale western Pacific and southeast Asian coastal system with ahorizontal resolution of 1/12° and 1/36°, respectively) constructed in the last fiscal year, and conducted
integrations of the system on JSS2. The large-scale western Pacific system reproduces decrease of sea surface
temperature and salinity with the passage of typhoons and improves the Kuroshio and Kuroshio Extension path.
The southeast Asian coastal system shows low salinity water formation around the Mekong river during summer
with the large amount of river discharge as well as the localized cool SST in summer and cold tongue in winternear the Vietnam coast (Fig. 1), that provides a basis for marine environmental monitoring.

Fig. 1: Simulation results of August 2015 by South-Asia system. Upper panels aresea surface temperature, and lower panels are sea surface salinity. Left panels aresatellite observations, middle panels are simulation with data assimilation, and rightpanels are simulation without data assimilation.
(Video. Video is available on the web.)

Publications- Peer-reviewed papers
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.

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JAXA Special Publication JAXA-SP-19-003E- Oral Presentations
Aiki, H., Coastal physical data from satellite and assimilation, 11th GEOSS Asia-Pacific Symposium, Kyoto,
Oct. 2018
Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, and M. Kachi, An LETKF-based ocean reanalysis for
the Asia-Oceania region using Himawari-8 SSTs, JpGU 2018, Makuhari, Chiba, May 2018
Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, and M. Kachi, An LETKF-based ocean reanalysis for
the Asia-Oceania region using Himawari-8 SSTs, IWMO2018, Brazil, Jun. 2018
Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, and M. Kachi, An LETKF-based ocean reanalysis for
the Asia-Oceania region using Himawari-8 SSTs and SMOS/SMAP SSS, 4th International Joint Workshop on
Computationally-Intensive Modeling of the Climate System and 9th OFES International Workshop, Fukuoka, Mar.
2019
Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, and M. Kachi, An LETKF-based ocean reanalysis for
the Asia Oceania region using Himawari-8 SSTs and SMOS/SMAP SSS, 20th Pacific Asian Marginal Seas,Kaohsiung, Mar. 2019
Miyazawa, Y., T. Hihara, S. M. Varlamov, T. Miyama, M. Kachi, H. Murakami, N. Ono, Y. Kurihara, Utilizationof satellite observation data for ocean state forecasts, The Joint PI Meeting of JAXA Earth Observation Missions
FY 2018, Tokyo, Jan. 2019
- Poster Presentations
Hihara, T., Y. Miyazawa, T. Miyama, M. Kachi, H. Murakami, and Y. Kurihara, Bias Correction of satellite SSTfor ocean assimilation product using LETKF, GHRSST XIX, Darmstadt, Germany, Jun. 2018
Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, and M. Kachi, An LETKF-based ocean reanalysis forthe Asia Oceania region using Himawari-8 SSTs, The Joint PI Meeting of JAXA Earth Observation Missions FY
2018, Tokyo, Jan. 2019
Hihara, T., Y. Miyazawa, V. Sergey, T. Miyama, M. Kachi, and H. Murakami, The high resolution regional oceanassimilation product “JCOPE-T DA” using satellite SST data, The Joint PI Meeting of JAXA Earth Observation
Missions FY 2018, Tokyo, Jan. 2019
- Web
https://www.eorc.jaxa.jp/ptree/ocean_model/index.html

Usage of JSS2


Computational Information
Process Parallelization Methods

MPI

Thread Parallelization Methods

Automatic Parallelization

Number of Processes

20 - 32

Elapsed Time per Case

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JAXA Supercomputer System Annual Report (April 2018-March 2019)

Resources Used
Fraction of Usage in Total Resources*1 (%): 0.77
Details
Computational Resources
System Name

Amount of Core Time(core x hours)

Fraction of Usage*2 (%)

SORA-MA

6,661,725.81

0.82

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

42.92

0.04/data

83,866.16

1.48/ltmp

8,789.07

0.75

Archiver Resources
Archiver Name

Storage Used (TiB)

J-SPACE

Fraction of Usage*2 (%)
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.

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