Special Edition on Global Precipitation Measurement (GPM) : 5th Anniversary : GSMaP RIKEN Nowcast : Global Precipitation Nowcasting with Data Assimilation

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  • OTSUKA Shigenori
    RIKEN Center for Computational Science, Kobe, Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan RIKEN Cluster for Pioneering Research, Kobe, Japan
  • KOTSUKI Shunji
    RIKEN Center for Computational Science, Kobe, Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan RIKEN Cluster for Pioneering Research, Kobe, Japan
  • OHHIGASHI Marimo
    RIKEN Center for Computational Science, Kobe, Japan
  • MIYOSHI Takemasa
    RIKEN Center for Computational Science, Kobe, Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan RIKEN Cluster for Pioneering Research, Kobe, Japan University of Maryland, Maryland, USA Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

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  • GSMaP RIKEN Nowcast: Global Precipitation Nowcasting with Data Assimilation

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<p> Since January 2016, RIKEN has run an extrapolation-based nowcasting system of global precipitation in real time. Although our previous paper reported the effectiveness of using data assimilation in a limited verification period, the long-term stability of the forecast accuracy through different seasons has not been investigated. In addition, the algorithm was updated seven times between January 2016 and March 2018. Therefore, this paper aims to examine how motion vectors can be derived more accurately, and how data assimilation can stably constrain an advection-diffusion model for extrapolation for the long-term operation. The Japan Aerospace Exploration Agency's Global Satellite Mapping of Precipitation (GSMaP) near-real-time product is the only input to the nowcasting system. The motion vectors of precipitation areas are computed by a cross-correlation method, and the Local Ensemble Transform Kalman Filter is used to generate a smooth, complete set of motion vectors. Precipitation areas are extrapolated in time up to 12 hours ahead, and the product, called GSMaP RIKEN Nowcast, is disseminated on a webpage in real time. Most of the algorithmic updates involved improving the estimation of the motion vectors, and the forecast accuracy was gradually and consistently improved by these updates. In particular, the threat scores increased the most at approximately 40°S and 40°N. A performance decrease in the northern hemisphere winter was also reduced by reducing noise in advection. The time series of the ensemble spread demonstrated that an increase in the number of available motion vectors by a system update led to a decrease in the ensemble spread, and vice versa.</p>

収録刊行物

  • 気象集誌. 第2輯

    気象集誌. 第2輯 97 (6), 1099-1117, 2019

    公益社団法人 日本気象学会

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