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.
Himawari-8, a next-generation geostationary meteorological satellite that has been in operation since July 2015, incorporates significant improvements in resolution, scan frequency, and number of bands, bringing new capabilities to weather forecasting. By taking advantage of the availability of high-frequency data with high spatial resolution, an ensemble Kalman filter implemented with a mesoscale regional model assimilated rapid-scan atmospheric motion vectors (RS-AMVs) from Himawari-8. Data assimilation and ensemble forecast experiments were conducted for a heavy rainfall event that occurred in September 2015 in the Kanto and Tohoku regions of Japan. The results showed that the inclusion of RS-AMVs improved precipitation scores, especially for weak and moderate rainfall. In addition, the subsequent model forecast simulated successfully the band of heavy rainfall. Ensemble-based probabilistic forecasts showed that when RSAMVs were assimilated, the results captured the occurrence of torrential rainfall with a relatively high probability. The ensemble-based correlation analysis indicated that the strong rainfall was related to advection of moisture at low to mid levels and moisture flux convergence at lower levels. Simulations with a higher resolution model initialized by nested data assimilation showed that the assimilation of frequent RS-AMVs improved the forecast results.(Citation: Kunii, M., M. Otsuka, K. Shimoji, and H. Seko, 2016: Ensemble data assimilation and forecast experiments for the September 2015 heavy rainfall event in Kanto and Tohoku regions with atmospheric motion vectors from 12,[209][210][211][212][213][214]
To discuss the feasibility of the Himawari follow-on program, impacts of a hyperspectral sounder on a geostationary satellite (GeoHSS) is assessed using an observing system simulation experiment. Hypothetical GeoHSS observations are simulated by using an accurate reanalysis dataset for a heavy rainfall event in western Japan in 2018. The global data assimilation experiment demonstrates that the assimilation of clear-sky radiances of the GeoHSS improves the forecasts of the representative meteorological field and slightly reduces the typhoon position error. The regional data assimilation experiment shows that assimilating temperature and relative humidity profiles derived from the GeoHSS improves the heavy rainfall in the Chugoku region of western Japan as a result of enhanced southwesterly moisture flow off the northwestern coast of the Kyushu Island. These results suggest that the GeoHSS provides valuable information on frequently available vertically resolved temperature and humidity and thus improves the forecasts of severe events.
Atmospheric motion vectors (AMVs) derived from 5-min rapid scan (RS) imagery of the Multi-functional Transport Satellite are expected to capture small-scale distributions of airflows better than typical AMVs derived from 30-min imagery because the observation interval of RS-AMV is shorter. The impact of these high-frequency data on the numerical forecasting of a heavy rainfall near a stationary front was investigated by conducting data assimilation experiments. As a part of preparation for the assimilation, RS-AMVs were compared with the firstguess field obtained from the Japan Meteorological Agency (JMA) nonhydrostatic model (NHM). The comparison result indicated that the RS-AMVs were of good quality and could be used in the JMA's operational NHM with 4D variational data assimilation (JNoVA). Assimilation experiments investigating a heavy rainfall event were conducted using different lengths of assimilation time slot and time intervals of spatial thinning for the assimilation of the RS-AMV data. The assimilation of RS-AMVs caused the initial wind fields to enhance the upper-level divergence and low-level convergence around the front. Consequently, the forecast of the rainfall amount was increased near the front, and the verification scores were slightly improved over the control experiment in the early forecast hours.
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