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.
The major eruption of volcano Hunga Tonga‐Hunga Ha'apai in Tonga on 15 January 2022 generated atmospheric Lamb waves that propagated over the world. This seemed the first volcanic Lamb wave event that was clearly captured by the state‐of‐the‐art geostationary meteorological satellites. This paper provides visualization of the waves using thermal infrared images from geostationary satellite Himawari‐8. The second time derivatives of 10‐min interval images clearly illustrated the waves propagating at about 310 m s−1. The waves could be tracked for more than a week while they propagated five times round the earth. Automated wavefront identification illustrated nonuniform propagation speed. The signals in the satellite images well matched the surface pressure observations in Japan.
The phased-array weather radar (PAWR) is a new-generation weather radar that can make a 100-m-resolution three-dimensional (3D) volume scan every 30 s for 100 vertical levels, producing ~100 times more data than the conventional parabolic-antenna radar with a volume scan typically made every 5 min for 15 scan levels. This study takes advantage of orders of magnitude more rapid and dense observations by PAWR and explores high-precision nowcasting of 3D evolution at 1–10-km scales up to several minutes, which are compared with conventional horizontal two-dimensional (2D) nowcasting typically at O(100) km scales up to 1–6 h. A new 3D precipitation extrapolation system was designed to enhance a conventional algorithm for dense and rapid PAWR volume scans. Experiments show that the 3D extrapolation successfully captured vertical motions of convective precipitation cores and outperformed 2D nowcasting with both simulated and real PAWR data.
Time-lagged ensemble downscaling experiments with Japan Meteorological Agency-Nonhydrostatic model are performed to study the dependence of heavy precipitation simulated by the model on the horizontal resolution for five days during late January to early February 2007, when the Jakarta Flood event occurred. The model runs with horizontal resolutions of 2, 4, and 5 km downscaled from the model runs with a 20-km resolution demonstrate the ability to reproduce a region of strong convective activity to the north of Java Island during the event. Daily meridional propagation of enhanced precipitation signals is simulated in the model runs with 2-and 4-km resolutions.Cumulative distribution functions of precipitation rate in the model are analyzed for four different regions: ocean, northern coast, mountain, and southern coast. The northern coast region shows the highest contribution of heavy precipitation compared to other regions for all the experiments as well as for satellite-based precipitation estimates. The statistics on the frequency of heavy precipitation show that the diurnal variation of heavy precipitation produced by the model with a 2-km resolution agrees well with that of satellite-based precipitation estimates.
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