In FY 2018, the Japan Meteorological Agency (JMA) has started considering the next geostationary satellite program. JMA will pursue a seamless geostationary Earth orbit satellite system, including in particular the deployment of hyperspectral infrared sounder (HSS) across the full GEO ring. In the study reported here, the impacts of the hyperspectral sounder on a geostationary satellite (GeoHSS) were assessed via the observing system simulation experiment (OSSE) to help evaluate the Himawari follow-on program. Hypothetical GeoHSS observations were simulated using an accurate reanalysis dataset for 2018 heavy rainfall and typhoon events in western Japan. The global data assimilation experiment demonstrated that the assimilated GeoHSS clear-sky radiances improved representative meteorological field forecasts and significantly reduced typhoon positional errors. A regional data assimilation experiment showed that assimilated temperature and relative humidity profiles derived from GeoHSS data improved information on heavy rainfall associated with enhanced southwesterly moisture flow off the northwestern coast of Kyushu Island. These results indicate that the GeoHSS provides valuable information on frequently provided data regarding vertically resolved temperature and humidity, thereby helping to improve forecasting of extreme meteorological events.

Wind is fundamental in many atmospheric phenomena. Global wind profile observation is important to improve numerical weather prediction (NWP) and various meteorological studies. Wind profile observations are measured mainly by radiosonde networks. Most of the weather stations are on land, while weather stations on oceans and remote land areas are sparsely distributed. The current global wind observing systems do not satisfy the globally homogeneous wind profile observation. A space-based Doppler wind lidar is one of the candidates for future global wind profile observations. In the paper, we present results from feasibility study of space-based Doppler Wind Lidar.

Following the invention of the telegraph, electronic computer, and remote sensing, “big data” is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” Data assimilation (DA) is a key to numerical weather prediction (NWP) by integrating the real-world sensor data into simulation. However, the current DA and NWP systems are not designed to handle the “big data” from next-generation sensors and big simulation. Therefore, we propose “big data assimilation” (BDA) innovation to fully utilize the big data. Since October 2013, the Japan's BDA project has been exploring revolutionary NWP at 100-m mesh refreshed every 30 s, orders of magnitude finer and faster than the current typical NWP systems, by taking advantage of the fortunate combination of next-generation technologies: the 10-petaflops K computer, phased array weather radar, and geostationary satellite Himawari-8. So far, a BDA prototype system was developed and tested with real-world retrospective local rainstorm cases. This paper summarizes the activities and progress of the BDA project, and concludes with perspectives toward the post-petascale supercomputing era.