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.