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Session convener-recommended article JpGU Meeting 2013
Ensemble Experiments Using a Nested LETKF System to Reproduce Intense Vortices Associated with Tornadoes of 6 May 2012 in Japan
Seko H, Kunii M, Yokota S, Tsuyuki T, Miyoshi T
Ensemble forecast, Tornadoes, LETKF
(a) Rainfall regions at the height of 20 m at 12:00 JST reproduced by each of the 12 ensemble members of NHM-350. Enlarged views of the distributions of rainfall and horizontal wind by (b) #004 and (c) #007. The red rectangles in (a) indicate the regions shown in (b) and (c). The red dots in (a) and (b) show the positions of vorticity exceeding 0.1 s–1 at 12:00 JST, and the red circle in (b) shows where convergence is occurring near the northern rainfall region. The thin contours are the stream lines of horizontal wind.
Experiments simulating intense vortices associated with tornadoes that occurred on 6 May 2012 on the Kanto Plain, Japan, were performed with a nested local ensemble transform Kalman filter (LETKF) system. Intense vortices were reproduced by downscale experiments with a 12-member ensemble in which the initial conditions were obtained from the nested LETKF system analyses. The downscale experiments successfully generated intense vortices in three regions similar to the observed vortices, whereas only one tornado was reproduced by a deterministic forecast. The intense vorticity of the strongest tornado, which was observed in the southernmost region, was successfully reproduced by 10 of the 12 ensemble members. An examination of the results of the ensemble downscale experiments showed that the duration of intense vorticities tended to be longer when the vertical shear of the horizontal wind was larger and the lower airflow was more humid. Overall, the study results show that ensemble forecasts have the following merits: (1) probabilistic forecasts of the outbreak of intense vortices associated with tornadoes are possible; (2) the miss rate of outbreaks should decrease; and (3) environmental factors favoring outbreaks can be obtained by comparing the multiple possible scenarios of the ensemble forecasts.