Impact of the Window Length of Four-Dimensional Local Ensemble Transform Kalman Filter: A Case of Convective Rain Event

DOI Web Site 2 Citations 17 References
  • Maejima Yasumitsu
    RIKEN Center for Computational Science
  • Miyoshi Takemasa
    RIKEN Center for Computational Science RIKEN interdisciplinary Theoretical and Mathematical Sciences Program RIKEN Cluster for Pioneering Research Department of Atmospheric and Oceanic Science, University of Maryland, College Park Application Laboratory, Japan Agency for Marine-Earth Science and Technology

Abstract

<p>This study aims to investigate the tradeoff between the computational time and forecast accuracy with different data assimilation (DA) windows of four-dimensional local ensemble transform Kalman filter (4D-LETKF) for a single-case severe rainfall event. We perform a series of Observing System Simulation Experiments (OSSEs) with 1-, 3-, 5- and 15-minute DA window in a severe rainstorm event in Kobe, Japan, on July 28, 2008, following the prior OSSEs by Maejima et al. (2019). Running 1-minute DA cycles showed the best forecast accuracy but with the highest computational cost. The computational cost could be reduced by taking a long DA window, but the forecast became less accurate even though the same number of observations were used. A significant gap was found between the 3-minute window and 5-minute window. With the 1- and 3-minute windows, the forecasts captured the intense rainfall, while with the 5-minute window or longer, the rainfall intensity was drastically underestimated. This single-case study suggests that 3-minute or shorter DA window be a promising method for a severe rainfall forecast, although more case studies are necessary to draw general conclusion.</p>

Journal

  • SOLA

    SOLA 16 (0), 37-42, 2020

    Meteorological Society of Japan

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