Nowcasting of Precipitation Based on Complementary Application of X-band Polarimetric Radar and C-band Conventional Radar

DOI Web Site 10 Citations 36 References
  • KATO Atsushi
    National Research Institute for Earth Science and Disaster Prevention Storm, Flood, and Landslide Research Department
  • MAKI Masayuki
    National Research Institute for Earth Science and Disaster Prevention Storm, Flood, and Landslide Research Department
  • IWANAMI Koyuru
    National Research Institute for Earth Science and Disaster Prevention Storm, Flood, and Landslide Research Department
  • MISUMI Ryouhei
    National Research Institute for Earth Science and Disaster Prevention Storm, Flood, and Landslide Research Department
  • MAESAKA Tsuyoshi
    National Research Institute for Earth Science and Disaster Prevention Storm, Flood, and Landslide Research Department

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Other Title
  • Ｘバンドマルチパラメータレーダ情報と気象庁レーダ情報を用いた降水ナウキャスト
  • Xバンドマルチパラメータレーダ ジョウホウ ト キショウチョウ レーダ ジョウホウ オ モチイタ コウスイ ナウキャスト

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Description

Very short-time quantitative precipitation forecasting (nowcasting) using X-band polarimetric radar is proposed as a means of quantitative rainfall estimation to support flood prediction in urban areas. The method includes C-band conventional radar as complementary data. Inland flooding tends to occur more frequently in urban areas due to the large proportion of impervious surfaces, and flooding in such areas is of considerable public concern. Due to the rapid response of rivers and drainage infrastructure in urban areas, nowcasting is required in order to realize timely flood prediction. However, as the X-band has a relatively short observation range and is affected by strong signal attenuation under heavy rainfall, X-band alone are insufficient for nowcasting. In the proposed method, X-band polarimetric radar provides highly accurate rainfall data, and real-time corrected C-band conventional radar provides the gap filling data. The results of the method are demonstrated to accord well with rain-gauge data and superior to a conventional radar rainfall. The accuracy of the proposed method is also comparable to that of Radar-AMeDAS, a radar rainfall method corrected using a high-density rain-gauge network in near real-time. The nowcasting experiments were conducted using the conventional radar rainfall and the radar rainfall based on the proposed method. The results from the three cases showed that the method can improved rainfall nowcasting.

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