- Meteorological Society of Japan
- Journal of the Meteorological Society of Japan. Ser. II (ISSN:00261165)
An intense rainband associated with Typhoon 1326 (Wipha) induced a fatal debris flow on Izu Oshima, Japan, on October 15-16, 2013. This rainband formed along a local front between the southeasterly humid warm air around the typhoon and the northeasterly cold air from the Kanto Plain. In this paper, the Japan Meteorological Agency Nonhydrostatic Model was optimized for the “K computer,” and ultra-high-resolution (500-250 m grid spacing) numerical simulations of the rainband with a large domain were conducted.
Two of main factors that affect a numerical weather prediction (NWP) model, (1) grid spacing and (2) planetary boundary layer (PBL) schemes [Mellor–Yamada–Nakanishi–Niino (MYNN) and Deardorff (DD)], were investigated. Experiments with DD (Exps_DD: grid spacings of 2 km, 500 m, and 250 m) showed better reproducibility of the rainband position than experiments with MYNN (Exps_MYNN: grid spacings of 5 km, 2 km, and 500 m). Exps_DD simulated distinct convective-scale up/downdraft pairs on the southeast/northwest sides of the front, whereas those of Exps_MYNN were not clear. Exps_DD yielded stronger cold pools near the surface than did Exps_MYNN. These differences in the boundary layer structures likely had a large impact on the position of the front and the associated rainband. Exps_DD with the 500-m grid spacing showed the best precipitation performance according to the Fractions Skill Score.
To check other factors of the precipitation forecast, model domain sizes, lateral boundary conditions in nesting simulations, and terrain representations were investigated. In the small domain experiments, the rainband shapes were very different from the observations. In the experiment using a nesting procedure, the deterioration of the forecast performance was acceptably reduced. The model with fine terrains better reproduced the intense rain over the island. These results demonstrate that the ultra-high-resolution NWP model with a large domain has the possibility to improve predictions of heavy rain.