JAXA Realtime weather watch & GSMaPxNEXRA Global Precipitation Forecasts （Ver. 3.0）
Earth Observation Research Center, Japan Aerospace Exploration Agency
Updated July 2026

What is "JAXA Realtime weather watch"?

"JAXA Realtime weather watch" provides current and future meteorological data (e.g. surface wind direction / wind speed, surface air temperature, surface water vapor amount, accumulated precipitation amount, etc.). These are results of calculation based on the system NEXRA (NICAM-LETKF JAXA Research Analysis) which JAXA has jointly developed with the University of Tokyo and RIKEN, combining satellite data and numerical weather models.

In this NEXRA product, weather information can be provided by the data assimilation techniques. Research results of such data assimilation and weather prediction experiments through data assimilation cycles are useful for developments of the technique towards the operational use of satellite data.

NEXRA (NICAM-LETKF JAXA Research Analysis)

In daily weather forecasts, initial values are corrected using observation data such as JAXA’s Earth observation satellites (GPM core satellite DPR & GMI, GCOM-W AMSR 2 etc.) through “data assimilation” cycle.

Recently, progresses of computer performance and data assimilation technology have been remarkable, and JAXA has developed with the University of Tokyo, RIKEN and Chiba University, the Numerical Weather Prediction Model (Nonhydrostatic Icosahedral Atmospheric Model; NICAM, Satoh et al. 2014) and the data assimilation system (Local Ensemble Transform Kalman Filter; LETKF, Kotsuki et al. 2017a, 2017b; Terasaki and Misyohi, 2017). By promoting the joint research, we developed a state-of-the-art weather data assimilation system utilizing the large-scale computing performance of JAXA supercomputer system (JAXA Supercomputer System, JSS), named as "NICAM-LETKF JAXA Research Analysis (NEXRA)" (Kotsuki et al. 2019a).

Features of NEXRA combining satellite data and weather models

The NEXRA is a unique weather data assimilation system. One is that we assimilate the Global Satellite Mapping of Precipitation (GSMaP) as observational data. It is known that the assimilation of precipitation data may improve the analyzed precipitation but generally degrades other atmospheric variables. However, the NEXRA can assimilate not "precipitation" itself, but "the likelihood of its precipitation based on the past precipitation frequency distribution", and we succeeded in improving the accuracy of the atmospheric variables (Kotsuki et al., 2017a).

The other unique point is that the NEXRA calculates 128 ensemble members. Measurement error exists in observation value. Observed values will vary around true values of atmospheric conditions that we really want to know. In the same way, since forecasts of numerical models also fluctuate in the vicinity of true values, the variation themselves made by successfully combining errors between observations and numerical models is ensemble data. Ensemble data are created each time a data assimilation cycle is executed. NEXRA currently produces forecasts up to 10 days ahead using the ensemble mean as the initial condition, and the results are visualized on this website.

NEXRA Versions

Currently, NEXRA version 3.0 is in operation. For a comparison with the previous version, please see the table below and the Earth-graphy article “Advancing Space-Based Weather Analysis and Forecasting – Renewal of JAXA’s Realtime Weather Watch ‘NEXRA3’ and Performance Evaluation with the previous system (Overview Article)”.

System NEXRA2
(DOI link)		 NEXRA3
(DOI link)
NICAM version version 19.2 version 21.3
Assimilation Model
Horizontal resolution 112km 56km
Vertical layer 38 layers (up to 40km altitude) 78 layers (up to 50km altitude)
Cloud microphysics Coagulation process only NSW6-Roh Scheme
Handling of precipitation Diagnostic type Prognostic type (accounts for particle types such as rain, snow, graupel, etc.)
Handling of cumulus (cumulus parameterization) Diagnostic type, Arakawa-Schubert Scheme Chikira-Sugiyama Scheme
Forecast Model
(surface data are visualized on this webpage)
Horizontal resolution 14km 14km
Vertical layer 38 layers (up to 40km altitude) 78 layers (up to 50km altitude)
Cloud microphysics NSW6 Scheme NSW6-Roh Scheme
Handling of precipitation Prognostic type (considering particle types such as rain, snow, graupel, etc.) Prognostic type (considering particle types such as rain, snow, graupel, etc.)
Handling of cumulus (cumulus parameterization) Not applied Chikira-Sugiyama Scheme

What is "GSMaPxNEXRA Global Precipitation Forecasts"?

Under a joint work between the RIKEN and the JAXA, RIKEN developed the world's first global precipitation seamless forecast system by combining precipitation forecasts from NEXRA and GSMaP RIKEN Nowcast (GSMaP_RNC; Otsuka et al. 2016, 2019) in Kotsuki et al. (2019b).

The prediction of precipitation is obtained as a locally optimized weighted average of the forecasts from both NEXRA and GSMaP_RNC. The experiment was conducted with the training period for 1 year from September 2014 for finding the optimal weights at each location, and the verification period as the subsequent year. GSMaP_RNC outperformed NEXRA in the forecast accuracy up to 7 hours ahead but reversed after that. The result that the prediction became more accurate at all forecast lead times by merging both.

GSMaPxNEXRA Global Precipitation Forecasts” provides the locally-optimized weighted average of the forecasts from both NEXRA and GSMaP_RNC up to 5 days ahead.

Please see RIKEN's GSMaPxNEXRA website for details.

Important Notice

  • This website provides precipitation forecasts up to five days ahead using the hourly updated Global Satellite Mapping of Precipitation Near-Real-Time product (GSMaP_NRT) provided by JAXA.
  • RIKEN has acquired a weather forecasting license from the Japan Meteorological Agency (JMA) for the area surrounding Japan, defined as 0–60°N and 100–180°E. The license number is 204.
  • Areas shaded in gray on the map indicate out-of-service locations.
  • Please note that the weather forecasts on this website may differ from those provided by the JMA. Please give precedence to the latest warnings and advisories issued by the JMA.
  • Use of information or data from this website is at the user’s own risk. RIKEN takes no responsibility for any direct or indirect damage that may arise from the use of this information or data.
  • Any part or all of this website may be changed, deleted, or removed without notice.

Use of Data and Website Images

When using images from this website, please refer to the terms of use for research data (https://earth.jaxa.jp/policy/en.html).

Some data from “JAXA Realtime weather watch” (NEXRA) version 2.0 and later are available free of charge in netCDF format through a password-protected FTP server. Users who wish to access the data are requested to register here.

* “GSMaP × NEXRA Global Precipitation Forecast” data are not publicly available.

Contacts

NEXRA Office Earth Observation Research Center, Japan Aerospace Exploration Agency
2-1-1, Sengen, Tsukuba-city, Ibaraki 305-8505 Japan
E-mail:

Please contact us at the NEXRA Office if you have any questions.

References

    JAXA Realtime weather watch (NEXRA)

  • Matsugishi, S., Y.-W. Chen, K. Terasaki, H. Yashiro, S. Kotsuki, K. Kanemaru, K. Yamamoto, M. Satoh, T. Kubota, and T. Miyoshi, 2025: Intercomparison of NICAM–LETKF JAXA Research Analysis (NEXRA) Version 2 and 3. SOLA, 21, 283–292. https://doi.org/10.2151/sola.2025-035
  • Matsugishi, S., Y.-W. Chen, K. Terasaki, K. Kanemaru, S. Kotsuki, H. Yashiro, K. Yamamoto, M. Satoh, T. Kubota, and T. Miyoshi, 2025: NICAM–LETKF JAXA Research Analysis (NEXRA) Version 2.0. Geoscience Data Journal, 12, e70011. https://doi.org/10.1002/gdj3.70011
  • Kotsuki, S., K. Terasaki, K. Kanemaru, M. Satoh, T. Kubota, and T. Miyoshi, 2019: Predictability of record-breaking rainfall in Japan in July 2018: Ensemble forecast experiments with the near-real-time global atmospheric data assimilation system NEXRA. SOLA, 15A, 1–7. https://doi.org/10.2151/sola.15A-001
  • Kotsuki, S., T. Miyoshi, K. Terasaki, G.-Y. Lien, and E. Kalnay, 2017: Assimilating the Global Satellite Mapping of Precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). Journal of Geophysical Research: Atmospheres, 122, 631–650. https://doi.org/10.1002/2016JD025355
  • Kotsuki, S., Y. Ota, and T. Miyoshi, 2017: Adaptive covariance relaxation methods for ensemble data assimilation: Experiments in the real atmosphere. Quarterly Journal of the Royal Meteorological Society, 143, 2001–2015. https://doi.org/10.1002/qj.3060

    • GSMaP × NEXRA Global Precipitation Forecast

  • Kotsuki, S., K. Kurosawa, S. Otsuka, K. Terasaki, and T. Miyoshi, 2019: Global precipitation forecasts by merging extrapolation-based nowcast and numerical weather prediction with locally optimized weights. Weather and Forecasting, 34, 701–714. https://doi.org/10.1175/WAF-D-18-0164.1

    • GSMaP RIKEN Nowcast

  • Otsuka, S., S. Kotsuki, M. Ohhigashi, and T. Miyoshi, 2019: GSMaP RIKEN Nowcast: Global precipitation nowcasting with data assimilation. Journal of the Meteorological Society of Japan. Ser. II, 97, 1099–1117. https://doi.org/10.2151/jmsj.2019-061
  • Otsuka, S., S. Kotsuki, and T. Miyoshi, 2016: Nowcasting with data assimilation: A case of Global Satellite Mapping of Precipitation. Weather and Forecasting, 31, 1409–1416. https://doi.org/10.1175/WAF-D-16-0039.1

    • Others

  • Satoh, M., H. Tomita, H. Yashiro, H. Miura, C. Kodama, T. Seiki, A. T. Noda, Y. Yamada, D. Goto, M. Sawada, T. Miyoshi, Y. Niwa, M. Hara, Y. Ohno, S. Iga, T. Arakawa, T. Inoue, and H. Kubokawa, 2014: The Non-hydrostatic Icosahedral Atmospheric Model: Description and development. Progress in Earth and Planetary Science, 1, 18. https://doi.org/10.1186/s40645-014-0018-1
  • Terasaki, K., and T. Miyoshi, 2017: Assimilating AMSU-A radiances with the NICAM-LETKF. Journal of the Meteorological Society of Japan. Ser. II, 95, 433–446. https://doi.org/10.2151/jmsj.2017-028
  • Terasaki, K., M. Sawada, and T. Miyoshi, 2015: Local Ensemble Transform Kalman Filter experiments with the Nonhydrostatic Icosahedral Atmospheric Model NICAM. SOLA, 11, 23–26. https://doi.org/10.2151/sola.2015-006

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