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

Abstract

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.

Comparison of GOSAT SWIR and Aircraft Measurements of XCH₄ over West Siberia

“…Subsequently, the aircraft-based XCH 4 values were estimated by accumulating CH 4 number densities using the CH 4 profile and by dividing the accumulated CH 4 densities by the total number density of dry air molecules. The dry air molecules were calculated using the temperature and water vapor data from the Japan Meteorological Agency Grid Point Value (JMA-GPV) (e.g., Nakakita et al 1996). Temperature data from the Committee on Space Research (COSPAR) International Reference Atmosphere (CIRA-86) (Fleming et al 1990) and water vapor data from the Air Force Geophysics Laboratory (AFGL) (Anderson et al 1986) were also used for altitudes above 10 hPa for which no JMA-GPV data were available.…”

Ensemble flood forecasting with stochastic radar image extrapolation and a distributed hydrologic model

“…These new schemes include mathematical and stochastic models integrated with a meteorological component (e.g. Georgakakos and Bras, 1984;Nakakita et al, 1996) and hybrid models, which are a combination of numerical weather prediction (NWP) and image extrapolation models (e.g. Golding, 2000;Ganguly and Bras, 2003).…”

Ensemble Rainfall-Runoff Prediction With Radar Image Extrapolation and Its Error Structure

“…Operated by the Japan Ministry of Land, Infrastructure, and Transportation, the radar data is converted to rainfall intensity covering a 240km× 240km area with 3 km spatial resolution. A more detailed description of the Miyama radar is given by Nakakita et al 6) .…”

“…Dynamics and microphysics of atmosphere are highly related to the topography for generation, growth, and decay of rainfall 6) . Because the translation model does not account this specific topography information, the prediction from the model contains inevitable errors, which relate to topographic information.…”

“…While the simple extrapolation techniques have been the mainstream of nowcasting until recently, vast research efforts by many hydrologists and meteorologists have been done over several decades with introductions of many new schemes. These new schemes include mathematical and stochastic models integrated with a meteorological component 5), 6) and hybrid models, which are combination of Numerical Weather Prediction (NWP) models and image extrapolation models 7), 8) . In addition, there have been complex statistical approaches like with fractal generation algorithms 9) or artificial neural networks 10) .…”