GSMaP x NEXRA

Global precipitation nowcast

0 Animate

How to interpret the rainfall map

Colors indicate rain intensity: cold colors correspond to light rains, and warm colors correspond to heavy rains. Currently, we are not licensed to disclose forecasts for Japan and the surrounding areas (hatched). We are working on revising our weather forecasting license accordingly. In regions with little rainfall such as Central America to the Caribbean, the Sahara Desert, and southern Africa, our algorithm cannot produce forecast for the next 12 hours (hatched).

About GSMaP x NEXRA

The Data Assimilation Research Team (DA team) performs cutting-edge research on weather forecasting by integrating computer simulations and observation data. Based on an advanced data assimilation technique, we developed two precipitation forecasting systems; nowcasting (Note 1) and numerical weather prediction (NWP, Note 2). We developed a new algorithm of integrating these two predictions for the next five days. Operation of the prediction system involves 6-hourly updates and accuracy verification, and results are used for research purposes.

First, a new precipitation nowcasting technique was developed by incorporating data assimilation into the conventional precipitation nowcasting method to improve the forecast accuracy (GSMaP RIKEN Nowcast). Data assimilation is a method to incorporate observation data into simulations, which is the key to NWP. Although it is important to understand the moving direction and speed of precipitation areas at each location (motion vector), it is difficult to obtain stable motion vectors from the image data of ever-changing precipitation distributions. By applying data assimilation used in NWP, we can compute the motion vectors more stably. We have applied this new precipitation nowcast method to the global precipitation data (GSMaP, Note 3) and have been disseminating precipitation forecasts up to 12 hours lead since May 2017.

Next, a new NWP system NICAM-LETKF, which combines a NWP model NICAM (Non-hydrostatic ICosahedral Atmospheric Model, Note 4) and a local ensemble transform Kalman filter LETKF (Note 5), has been developed to successfully assimilate the GSMaP data. This is the only system in the world that directly uses GSMaP data. Using precipitation data for NWP has been one of the most difficult problems in meteorology, but we have solved this problem by applying the Gaussian transformation method (Note 6) to precipitation data. The NICAM-LETKF system is executed in real time by JAXA's supercomputer (JAXA Supercomputer System Generation 2; JSS2) and is available to the public as "JAXA realtime weather watch NEXRA."

Finally, we developed a new method to merge two different precipitation forecasts, 12-hour precipitation nowcast and 5-day NICAM-LETKF NWP, to create a single, highly accurate precipitation forecast. This method improves the accuracy of the precipitation forecast by applying a novel technique of local optimization that takes into account the statistical characteristics of each location. Beyond the first 12 hours, the accuracy of the precipitation nowcast becomes lower, so only the NWP is used.

Note

1. Precipitation nowcasting: A method of forecasting future precipitation based on the assumption that the most recent motion of the precipitation distribution based on observed data will persist. Because it does not take into account meteorological mechanisms such as the generation and development of rain clouds, the calculation is simple and fast, but the accuracy of the forecast decreases rapidly as the forecast time increases.
2. Numerical weather prediction: weather forecasting based on simulations that take meteorological processes into account, using complex calculations using a supercomputer.
3. The GSMaP, developed by the Japan Aerospace Exploration Agency (JAXA), is a global rainfall distribution information system based on satellite observations.
4. Numerical weather prediction model NICAM: A global atmospheric model that realizes highly accurate calculations by directly calculating the occurrence and behavior of clouds on a global scale. Conventional global atmospheric models require assumptions about the relationship between cloud systems and large-scale atmospheric circulation, such as high and low pressure systems, which is a major source of uncertainty. NICAM is usually run at the resolution of 870 m to 14 km. When the resolution of 870 m to 14 km is used, the model is called a global cloud resolving model, and when the resolution of 7 km to 14 km is used, it is called a global cloud system resolving model. Currently, we use the resolution of 112 km, which is about 10 times lower than that of 14 km.
5. Local Ensemble Transform Kalman Filter (LETKF): A practical method of data assimilation that is particularly efficient for parallel computing.
6. Gaussian transformation method: A method for transforming a random variable that follows a non-Gaussian distribution into a variable that follows a Gaussian distribution.

Disclaimer

This website provides precipitation forecasts five days in advance using hourly-updated Global Satellite Mapping of Precipitation Near-Real-Time product (GSMaP_NRT) provided by JAXA. We acquired the weather forecasting license from the Japan Meteorological Agency (JMA) for the area surrounding Japan defined by 0-60ºN and 100-180ºE. Areas on the map shaded in gray indicate out-of-service locations.

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 takes 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.

Copyright

All text, photographs, diagrams, and other materials on this website are copyrighted by RIKEN unless explicitly specified otherwise on the website. It is prohibited to use, reproduce, or modify any of the material without RIKEN's permission.

Reference

1. Miyoshi, T., S. Kotsuki, K. Terasaki, S. Otsuka, G.-Y. Lien, H. Yashiro, H. Tomita, M. Satoh, and E. Kalnay, 2020: Precipitation Ensemble Data Assimilation in NWP Models. in "Satellite Precipitation Measurement", Advances in Global Change Research, Vol. 69, edited by Levizzani, V., C. Kidd, D. Kirschbaum, C. Kummerow, K. Nakamura, and F. Turk, Springer, Cham.
2. Terasaki, K., S. Kotsuki, and T. Miyoshi, 2019: Multi-year analysis using the NICAM-LETKF data assimilation system. SOLA, 15, 41-46.
3. 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.
4. Kotsuki, S., K. Kurosawa, S. Otsuka, K. Terasaki, T. Miyoshi, 2019: Global precipitation forecasts by merging extrapolation-based nowcast and numerical weather prediction with locally optimized weights. Wea. Forecasting, 34, 701–714.
5. Otsuka, S., S. Kotsuki, M. Ohhigashi, and T. Miyoshi, 2019: GSMaP RIKEN Nowcast: Global precipitation nowcasting with data assimilation. J. Meteor. Soc. Japan, 97, 1099-1117.
6. Terasaki, K., and T. Miyoshi, 2017: Assimilating AMSU-A radiances with the NICAM-LETKF. J. Meteor. Soc. Japan, 95, 433-446.
7. Kotsuki S., Y. Ota, and T. Miyoshi, 2017: Adaptive covariance relaxation methods for ensemble data assimilation: Experiments in the real atmosphere. Quart. J. Roy. Meteor. Soc., 143, 2001-2015.
8. 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.
9. 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.
10. Lien, G.-Y., T. Miyoshi, and E. Kalnay, 2016: Assimilation of TRMM Multisatellite Precipitation Analysis with a low-resolution NCEP Global Forecast System. Mon. Wea. Rev., 144, 643–661.
11. Lien, G.-Y., E. Kalnay, T. Miyoshi, and G. J. Huffman, 2016: Statistical properties of global precipitation in the NCEP GFS model and TMPA observations for data assimilation. Mon. Wea. Rev., 144, 663–679.
12. 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.