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

抄録

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.