JAXA Realtime weather watch & GSMaPxNEXRA Global Precipitation Forecasts (Ver. 2.0)
Earth Observation Research Center, Japan Aerospace Exploration Agency
Updated July 2021

What is "JAXA Realtime weather watch"?

JAXA provides various useful data by Earth Observation Satellites. By utilizing (fusing) not only observation data obtained from satellites but also numerical models, it is possible to create continuous data and to provide physical quantities that cannot be obtained by satellites by data assimilation methods. JAXA is working with universities and research institutes to develop the data assimilation methods and construct simulation systems by utilizing such observation data.

"JAXA Realtime weather watch" provides you images of current surface wind direction / wind speed, surface air temperature, surface water vapor amount, accumulated water vapor amount, surface pressure, accumulated precipitation amount, Outgoing Longwave Radiation (OLR). 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.

Since 14th July 2021, "JAXA Realtime weather watch" has provided weather simulation data with 14km horizontal resolutions.

The introduction of NICAM-LETKF JAXA Research Analysis (NEXRA) system

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 Generation 2, JSS2), named as "NICAM-LETKF JAXA Research Analysis (NEXRA)" (Kotsuki et al. 2019a). The NEXRA is now being calculated four times a day, and it is operated about 8 hours behind real time.

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 100 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.

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.

Please note that the weather forecasts on this website can differ from weather forecasts provided by the JMA. Please give precedence to the latest warnings and advisories from the JMA.
Use of information or data from this website is undertaken at the user's own risk. RIKEN and JAXA take no responsibility for any direct or indirect damage that may arise through the use of this information or data. Any part or all of this website may be changed, deleted, or removed without notice.

Terms of Use of images:

Please refer to "Terms of Use of Research Data" (https://earth.jaxa.jp/policy/en.html) for the use of images.


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

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


  • Kotsuki S., K. Kurosawa, S. Otsuka, K. Terasaki and T. Miyoshi T. 2019b: Global Precipitation Forecasts by Merging Extrapolation-based Nowcast and Numerical Weather Prediction with Locally-optimized Weights. Wea. and Forecasting, 34, 701-714. https://doi.org/10.1175/WAF-D-18-0164.1.
  • Kotsuki S., K. Terasaki, K. Kanemaru, M. Satoh, T. Kubota and T. Miyoshi, 2019a: 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.
  • Otsuka, S., S. Kotsuki, M. Ohhigashi, T. Miyoshi, 2019: GSMaP RIKEN Nowcast: Global Precipitation Nowcasting with Data Assimilation, Journal of the Meteorological Society of Japan. Ser. II, 2019, Volume 97, Issue 6, Pages 1099-1117, https://doi.org/10.2151/jmsj.2019-061.
  • 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. J. Geophys.Res. Atmos., 122, 1-20. doi:10.1002/2016JD025355
  • Kotsuki, S., Y. Ota, T. Miyoshi, 2017: Adaptive covariance relaxation methods for ensemble data assimilation: Experiments in the real atmosphere. Quart. J. Roy. Meteorol. Soc., 143, 2001-2015. doi:10.1002/qj.3060
  • Satoh, M., Tomita, H., Yashiro, H., Miura, H., Kodama, C., Seiki, T., Noda, A. T., Yamada, Y., Goto, D., Sawada, M., Miyoshi, T., Niwa, Y., Hara, M., Ohno, Y., Iga, S., Arakawa, T., Inoue, T., Kubokawa, H., 2014: The Non-hydrostatic Icosahedral Atmospheric Model: Description and development. Progress in Earth and Planetary Science. 1, 18. doi: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, Vol. 95, No. 6, pp. 433-446, 2017 doi:10.2151/jmsj.2017-028
  • Otsuka, S., S. Kotsuki, and T. Miyoshi, 2016: Nowcasting with data assimilation: a case of Global Satellite Mapping of Precipitation. Wea. Forecasting, 31, 1409-1416
  • 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. doi:10.2151/sola.2015-006

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