Eiichi Nakakita

Over 5,000 Years of Ensemble Future Climate Simulations by 60-km Global and 20-km Regional Atmospheric Models

AbstractAn unprecedentedly large ensemble of climate simulations with a 60-km atmospheric general circulation model and dynamical downscaling with a 20-km regional climate model has been performed to obtain probabilistic future projections of low-frequency local-scale events. The climate of the latter half of the twentieth century, the climate 4 K warmer than the preindustrial climate, and the climate of the latter half of the twentieth century without historical trends associated with the anthropogenic effect are each simulated for more than 5,000 years. From large ensemble simulations, probabilistic future changes in extreme events are available directly without using any statistical models. The atmospheric models are highly skillful in representing localized extreme events, such as heavy precipitation and tropical cyclones. Moreover, mean climate changes in the models are consistent with those in phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensembles. Therefore, the results enable the a...

Short‐term rainfall prediction method using a volume scanning radar and grid point value data from numerical weather prediction

A physically based short-term rainfall prediction method, which uses a volume scanning radar, is extended so that it utilizes grid point values from a numerical weather prediction model as supplementary information. The original short-term prediction method mainly consists of a conceptual rainfall model that can predict rainfall distribution, particularly over mountainous regions, in a qualitative sense. On the other hand, the grid point values from the numerical weather prediction model, the Japan Spectral Model developed by the Japan Meteorological Agency, are operationally distributed as the grid point value (GPV) data. In the original short-term prediction method the three-dimensional wind field as well as initial distributions of the air temperature and water vapor were identified using topography and upper air observations. In the extended method, however, in identifying those initial values, the information from the GPV data is used instead of the upper air observations in order to accommodate large differences in temporal and spatial resolution between radar information and upper air observations. It is noted that this extended method does not use predicted GPV rainfall data. The conceptual rainfall model plays the role of bridging the gap between radar information and numerical weather prediction scales. This extended method is applied to a rainfall event which occurred in the bai-u season (one of the rainy seasons of Japan) in July 1994. Results show that for the extended lead time of three and four hours, prediction of the expanding rainfall area was improved.

A Real-Time Estimation of the Accuracy of Short-Term Rainfall Prediction Using Radar

The short-term rainfall prediction method proposed by the authors is extended to a stochastic method so that the method could provide in real-time the accuracy of the areal rainfall predicted using radar information. The extension was carried out based on the investigation on the stochastic properties of model parameters of the basic prediction model. First, we present results of theoretical analyses on the features of analytically predicted accuracy relating to the patterns of the movement of rainfall distribution. Next, we present a case study using actual rainfall distribution observed by radar during a Japanese typhoon. The results show that we can predict the mean square error of predicted areal rainfall at least within 1 hour ahead by the method proposed here.

A stochastic approach to short-term rainfall prediction using a physically based conceptual rainfall model

An improved method for short-term rainfall prediction is presented. A previously proposed deterministic rainfall prediction method for real-time hydrologic applications is extended to a stochastic method. This method mainly consists of a physically based conceptual rainfall model that includes water balance and thermodynamics. The important element in this method is the translation of radar data to the model parameter of the conceptual model, which is incorporated into the numerical scheme of the mesoscale model. The extended Kalman filter is used as a state estimator to update the model parameter of the conceptual model with new radar data and with forecasts from a numerical weather prediction model. The performance of the stochastic method is examined for a radar observation area that includes a mountainous region with a rainfall event that occurred along a front. The stochastic method performed better than the deterministic method.

Impact Assessment of Uncertainty Propagation of Ensemble NWP Rainfall to Flood Forecasting with Catchment Scale

The common approach to quantifying the precipitation forecast uncertainty is ensemble simulations where a numerical weather prediction (NWP) model is run for a number of cases with slightly different initial conditions. In practice, the spread of ensemble members in terms of flood discharge is used as a measure of forecast uncertainty due to uncertain precipitation forecasts. This study presents the uncertainty propagation of rainfall forecast into hydrological response with catchment scale through distributed rainfall-runoff modeling based on the forecasted ensemble rainfall of NWP model. At first, forecast rainfall error based on the BIAS is compared with flood forecast error to assess the error propagation. Second, the variability of flood forecast uncertainty according to catchment scale is discussed using ensemble spread. Then we also assess the flood forecast uncertainty with catchment scale using an estimation regression equation between ensemble rainfall BIAS and discharge BIAS. Finally, the flood forecast uncertainty with RMSE using specific discharge in catchment scale is discussed. Our study is carried out and verified using the largest flood event by typhoon “Talas” of 2011 over the 33 subcatchments of Shingu river basin (2,360 km2), which is located in the Kii Peninsula, Japan.

Advanced use into rainfall prediction of three-dimensionally scanning radar

A computational method for the determination of rainfall distribution for applications in short term rainfall prediction is presented here. The method is strongly influenced by the experience gained from the observation and analysis of data gathered on a heavy rainfall event in 1986 that occurred during the Baiu Season in Japan. The method is based on the concept that rainfall occurs as an interaction between an instability field, appropriately modeled, and a field of water vapor under the influence of topography. The results from this computational method showed good agreement with the temporal variation in the rainband that moved across the observation field in 1986. Towards determination of the parameters in the computational model, another method for the determination of the rainfield is also developed. This second method determines the rainfall distribution from estimation of the conversion rate of water vapor to liquid water through use of data from a three dimensional scanning radar. The results are consistent with those obtained from the first method.

Vertical distribution of precipitation particles in Baiu frontal stratiform intense rainfall around Okinawa Island, Japan

The vertical distribution of precipitation particles in an intensely precipitating stratiform cloud associated with the Baiu front around Okinawa Island was observed. X-band polarimetric radar, disdrometer, and hydrometeor videosonde data were used to examine the precipitation processes. The cloud top was approximately 12 km above sea level, as convection was depressed while stratiform regions developed near Okinawa Island. In the rain region below 3 km, the mean median volume diameter of the raindrop size distribution (DSD) estimated from the radar variables was 1.55 mm, and the mean normalized intercept parameter was 104.12 mm−1 m−3 with a mean radar reflectivity of 40.5 dBZe. The DSD indicates that the stratiform precipitation was characterized by higher number concentrations of smaller drops than observed previously in convective cells in a Baiu frontal convective precipitation region around Okinawa Island. The DSD also suggests the presence of larger raindrops than in convective cells embedded in a Baiu frontal stratiform precipitation region around Okinawa Island. In the ice region at 5–6 km, just above the melting layer and 6 km below the cloud top, the differential reflectivity and specific differential phase showed positive values, and videosonde measurements revealed that the number concentration of column-, plate-, and capped-column-like crystals (maximum dimensions of ≥0.1 mm) was 112 L−1. The high number concentration of these crystals contributed to the intense stratiform rainfall associated with the Baiu front.

Mapping of Japanese areas susceptible to snow cover change

Abstract Many of the Japanese regions subject to seasonal snow cover are characterized by low elevations and relatively high winter temperatures. A small change in winter temperatures could render many of these areas susceptible to snow cover change and consequently affect water resources management. This paper describes a climatological approach combined with an AGCM output to identify the regions and main river basins most sensitive to snow cover change in the case of climate change in Japan. It was found that a 1°C rise in temperature during the winter season could increase the snow-free area of Japan by 6%. The snow cover of Tohoku region and Mogami and Agano river basins was found to be the most sensitive to climate change. The AGCM output for a future scenario presents a reduction in total snowfall and an earlier peak in snowmelt for all regions. Editor Z.W. Kundzewicz Citation Chaffe, P.L.B, Takara, K, Yamashiki, Y, Apip, Luo, P., Silva, R.V., and Nakakita, E., 2013. Mapping of Japanese areas susceptible to snow cover change. Hydrological Sciences Journal, 58 (8), 1718–1728.

Early Detection of Baby-Rain-Cell Aloft in a Severe Storm and Risk Projection for Urban Flash Flood

In July 2008, five people were killed by a tragic flash flood caused by a local torrential heavy rainfall in a short time in Toga River. From this tragic accident, we realized that a system which can detect hazardous rain-cells in the earlier stage is strongly needed and would provide an additional 5 to 10 min for evacuation. By analyzing this event, we verified that a first radar echo aloft, by volume scan observation, is a practical and important sign for early warning of flash flood, and we named a first echo as a “baby-rain-cell” of Guerrilla-heavy rainfall. Also, we found a vertical vorticity criterion for identifying hazardous rain-cells and developed a heavy rainfall prediction system that has the important feature of not missing any hazardous rain-cell. Being able to detect heavy rainfall by 23.6 min on average before it reaches the ground, this system is implemented in XRAIN in the Kinki area. Additionally, to resolve the relationship between baby-rain-cell growth and vorticity behavior, we carried out an analysis of vorticity inside baby-rain-cells and verified that a pair of positive and negative vertical vortex tubes as well as an updraft between them existed in a rain-cell in the early stage.

Recent Application of Weather Radar Observation into Hydrologic Forecasting in Japan

This paper introduces recent research efforts on hydrologic flood forecasting in Japan using multiple weather radar observation networks. After illustrating the details of weather radar observation systems in Japan, several noticeable researches of the authors are introduced.