GOSAT data analysis and Greenhouse gases Observing SATellite (GOSAT) mission

JAXA Supercomputer System Annual Report February 2025-January 2026

Report Number: R25ER2100

Subject Category: Space Technology

Responsible Representative: Hiroshi Suto, Space Technology Directorate I Satellite Applications and Operations Center, Mission Manager
Contact Information: Kei Shiomi, Shin ISHIDA, Rio KAJIURA(shiomi.kei@jaxa.jp;ishida.shin@jaxa.jp;kajiura.rio@jaxa.jp)
Members: Takashi Goto, Makiko Hashimoto, Hidetoshi Hayasaka, Shin Ishida, Makoto Imanaka, Kenji Kowata, Fumie Kataoka, Mizuki Kato, Nobuhiro Kikuchi, Itaru Nishido, Hiroshi Suto, Takuya Sawada, Hideki Suetake, Kai Uematsu, Yasufumi Saijoh, Kei Shiomi, Rio Kajiura

Abstract

The Greenhouse Gases Observing Satellite (GOSAT)has been continued observations over 17 years. As the characteristics of its onboard sensors (TANSO‑FTS and TANSO‑CAI) change as the mission duration extends, radiometric, geometric, and spectral properties are continuously evaluated through calibration and validation. Furthermore, by appropriately reflecting the obtained calibration results in the processing algorithms, the long-term consistency of the dataset is ensured and the accuracy of greenhouse gas concentration estimates is improved.

Reference URL

Please refer to http://www.eorc.jaxa.jp/GOSAT/index.html .

Reasons and benefits of using JAXA Supercomputer System

Processing the large volume of data accumulated through GOSAT's long-term operation requires greater computational resources, including more CPU cores, memory, and storage. To shorten the processing period and provide processed products to users more rapidly, the use of JSS3 is essential.

Achievements of the Year

(1)Calibration and validation of GOSAT TANSO-CAI data

We analyzed the TANSO‑CAI observation data to evaluate changes in sensor sensitivity and to determine the radiance calibration coefficients, and accordingly determined the Band 1 calibration coefficient for the relevant period. 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 and Band 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.

Data period: February 2025 – October 2025 (every 4 months)

(2) Development and validation of algorithms for TANSO‑FTS research products

A higher level processing algorithm is being developed that simultaneously utilizes short‑wavelength infrared (SWIR) and thermal infrared (TIR) observation spectra derived from the Level‑1 data of the GOSAT TANSO‑FTS products to retrieve carbon dioxide and methane concentrations in the troposphere in two vertical layers. In addition, by incorporating the polarization information available in the SWIR data, the algorithm aims to improve the accuracy of aerosol correction. Since the version released in 2023 revealed significant differences in CO₂ and CH₄ concentrations over oceanic regions, improvements to the algorithm were implemented, and processing was carried out using GOSAT data from 2009 to 2017. Figure 3 shows the 2.5‑degree grid monthly mean distributions of column‑averaged CO₂ and CH₄ concentrations.

(3) Reprocessing associated with the TANSO‑FTS Level‑1 product version upgrade

Following the restart of the FTS in April 2025, changes occurred in the DC offset of the TIR detector output, necessitating an update to the DC offset table. In addition, the validity periods of the SWIR radiance‑saturation threshold table and the TIR orbital‑variation correction table were extended. With these Level‑1 processing parameter updates applied, a supercomputer was used to generate one year of reprocessed data products (V300301).

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

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

Fig.3: The 2.5‑degree grid monthly mean values of column averaged greenhouse gas concentrations derived from GOSAT data using the TANSO‑FTS research product algorithm. (Top left) Column averaged concentration of carbon dioxide in June 2009. (Top right) Column averaged concentration of methane in June 2009. (Bottom left) Column averaged concentration of carbon dioxide in June 2017. (Bottom right) Column averaged concentration of methane in June 2017.

Publications

- Web

https://www.eorc.jaxa.jp/GOSAT/currentStatus_11.html

https://data2.gosat.nies.go.jp/gallery/CAIL3oneday_en.html

Usage of JSS

Computational Information

Process Parallelization Methods: N/A
Thread Parallelization Methods: Items (1) of this year's achievements are not applicable, while item (2) and (3)are corresponds to OpenMP.
Number of Processes: 1
Elapsed Time per Case: 8 Hour(s)

JSS3 Resources Used

Fraction of Usage in Total Resources*1(%): 0.07

Details

Please refer to System Configuration of JSS3 for the system configuration and major specifications of JSS3.

Computational Resources
System Name	CPU Resources Used (Core x Hours)	Fraction of Usage*2(%)
TOKI-SORA	0.00	0.00
TOKI-ST	193919.81	0.20
TOKI-GP	699.25	0.01
TOKI-XM	0.00	0.00
TOKI-LM	0.00	0.00
TOKI-TST	0.00	0.00
TOKI-TGP	0.00	0.00
TOKI-TLM	0.00	0.00

File System Resources
File System Name	Storage Assigned (GiB)	Fraction of Usage*2(%)
/home	0.00	0.00
/data and /data2	449840.00	2.96
/ssd	0.00	0.00

Archiver Resources
Archiver Name	Storage Used (TiB)	Fraction of Usage*2(%)
J-SPACE	60.00	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.

ISV Software Licenses Used

ISV Software Licenses Resources
	ISV Software Licenses Used (Hours)	Fraction of Usage*2(%)
ISV Software Licenses (Total)	0.00	0.00

*2: Fraction of Usage：Percentage of usage relative to each resource used in one year.