Open AccessThe Tornadic Supercell on the Kanto Plain on 6 May 2012: Polarimetric Radar and Surface Data Assimilation with EnKF and Ensemble-Based Sensitivity Analysis
Author(s) -
Sho Yokota,
Hiromu Seko,
Masaru Kunii,
Hiroshi Yamauchi,
Hiroshi Niino
Publication year - 2016
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-15-0365.1
Subject(s) - supercell , data assimilation , environmental science , radar , meteorology , ensemble kalman filter , mesoscale meteorology , climatology , storm , geology , kalman filter , mathematics , physics , computer science , extended kalman filter , telecommunications , statistics
A tornadic supercell and associated low-level mesocyclone (LMC) observed on the Kanto Plain, Japan, on 6 May 2012 were predicted with a nonhydrostatic mesoscale model with a horizontal resolution of 350 m through assimilation of surface meteorological data (horizontal wind, temperature, and relative humidity) of high spatial density and C-band Doppler radar data (radial velocity and rainwater estimated from reflectivity and specific differential phase) with a local ensemble transform Kalman filter. With assimilation of both surface and radar data, a strong LMC was successfully predicted near the path of the actual tornado. When either surface or radar data were not assimilated, however, the LMC was not predicted. Therefore, both surface and radar data were essential for successful LMC forecasts. The factors controlling the strength of the predicted LMC, defined as a low-level maximum vertical vorticity, were clarified by an ensemble-based sensitivity analysis (ESA), which is a new approach for analyzing LMC intensification. The ESA showed that the strength of the LMC was sensitive to low-level convergence forward of the storm and to low-level relative humidity in the rear of the storm. Therefore, the correction of these low-level variables by assimilation of dense observations was found to be particularly important for forecasting and monitoring the LMC in the present case.