A B S T R A C T Experiments with two ensemble systems of resolutions 10 km (MF10km) and 2 km (MF2km) were designed to examine the value of cloud-resolving ensemble forecast in predicting precipitation on small spatio-temporal scales. Since the verification was performed on short-term precipitation at high resolution, uncertainties from small-scale processes caused the traditional verification methods to be inconsistent with the subjective evaluation. An extended verification method based on the Fractions Skill Score (FSS) was introduced to account for these uncertainties. The main idea is to extend the concept of spatial neighbourhood in FSS to the time and ensemble dimension. The extension was carried out by recognising that even if ensemble forecast is used, small-scale variability still exists in forecasts and influences verification results. In addition to FSS, the neighbourhood concept was also incorporated into reliability diagrams and relative operating characteristics to verify the reliability and resolution of two systems.The extension of FSS in time dimension demonstrates the important role of temporal scales in short-term precipitation verification at small spatial scales. The extension of FSS in ensemble space is called the ensemble FSS, which is a good representative of FSS for ensemble forecast in comparison with the FSS of ensemble mean. The verification results show that MF2km outperforms MF10km in heavy rain forecasts. In contrast, MF10km was slightly better than MF2km in predicting light rains, suggesting that the horizontal resolution of 2 km is not necessarily enough to completely resolve convective cells.

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The Meteorological Research Institute of the Japan Meteorological Agency has developed a cloudresolving nonhydrostatic 4-dimensional variational assimilation system (NHM-4DVAR), based on the Japan Meteorological Agency Nonhydrostatic Model (JMA-NHM), in order to investigate the mechanism of heavy rainfall events induced by mesoscale convective systems (MCSs). A horizontal resolution of the NHM-4DVAR is set to 2 km to resolve MCSs, and the length of the assimilation window is 1-hour. The control variables of the NHM-4DVAR are horizontal wind, vertical wind, nonhydrostatic pressure, potential temperature, surface pressure and pseudo relative humidity. Perturbations to the dynamical processes, and the advection of water vapor are considered, but these to the other physical processes are not taken into account.The NHM-4DVAR is applied to the heavy rainfall event observed at Nerima, central part of Tokyo metropolitan area, on 21 July 1999. Doppler radar's radial wind data, Global Positioning System's precipitable water vapor data, and surface temperature and wind data are assimilated as high temporal and spatial resolution data. The Nerima heavy rainfall is well reproduced in the assimilation and subseCorresponding author: Takuya Kawabata, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: tkawabat@mri-jma.go.jp 1 Present affiliation: Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.( 2007, Meteorological Society of Japan quent forecast, with respect to time sequence of 10-minute rainfall amount. The formation mechanism of the Nerima heavy rainfall is clarified from this study. A surface convergence line of horizontal winds was made of a southerly sea breeze and north-easterly winds over the Kanto plain around Nerima. Since the rise of temperature over the northern part of the Kanto plain was suppressed, due to a shield of clouds against sunshine, the difference of temperature between the convergence line and its northern side became large. Consequently, the wind convergence was enhanced around Nerima. An air with high equivalent potential temperature was lifted over this enhanced convergence line to generate cumulonimbi that caused the Nerima heavy rainfall.

Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.

A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity.The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity.An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.48 were assimilated at 1-min intervals.The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.