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Masaru Kunii
,
Takemasa Miyoshi
, and
Eugenia Kalnay

Abstract

The ensemble sensitivity method of Liu and Kalnay estimates the impact of observations on forecasts without observing system experiments (OSEs), in a manner similar to the adjoint sensitivity method of Langland and Baker but without using an adjoint model. In this study, the ensemble sensitivity method is implemented with the local ensemble transform Kalman filter (LETKF) and the Weather Research and Forecasting (WRF) model with real observations. The results in the case of Typhoon Sinlaku (2008) show that upper-air soundings have the largest positive impact on the 12-h forecasts, and that the targeted impact evaluation performs as expected and is computationally efficient. Denying negative-impact observations improves the forecasts, validating the estimated observation impact.

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Masaru Kunii
,
Kosuke Ito
, and
Akiyoshi Wada

Abstract

An ensemble Kalman filter (EnKF) that uses a regional mesoscale atmosphere–ocean coupled model was preliminarily examined to provide realistic sea surface temperature (SST) estimates and to represent the uncertainties of SST in ensemble data assimilation strategies. The system was evaluated through data assimilation cycle experiments over a one-month period from July to August 2014, during which time a tropical cyclone (TC) as well as severe rainfall events occurred. The results showed that the data assimilation cycle with the coupled model reproduced SST distributions realistically even without assimilating SST and sea surface salinity observations, and atmospheric variables provided to ocean models can, therefore, control oceanic variables physically to some extent. The forecast error covariance calculated in the EnKF with the coupled model showed dependency on oceanic vertical mixing for near-surface atmospheric variables due to the difference of variability between the atmosphere and the ocean as well as the influence of SST variations on the atmospheric boundary layer. The EnKF with the coupled model reproduced the intensity change of Typhoon Halong (2014) during the mature phase more realistically than with an uncoupled atmosphere model, although there remained a degradation of the SST estimate, particularly around the Kuroshio region. This suggests that an atmosphere–ocean coupled data assimilation system should be developed that is able to physically control both atmospheric and oceanic variables.

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Sho Yokota
,
Hiromu Seko
,
Masaru Kunii
,
Hiroshi Yamauchi
, and
Hiroshi Niino

Abstract

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.

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Sho Yokota
,
Hiroshi Niino
,
Hiromu Seko
,
Masaru Kunii
, and
Hiroshi Yamauchi

Abstract

To identify important factors for supercell tornadogenesis, 33-member ensemble forecasts of the supercell tornado that struck the city of Tsukuba, Japan, on 6 May 2012 were conducted using a mesoscale numerical model with a 50-m horizontal grid. Based on the ensemble forecasts, the sources of the rotation of simulated tornadoes and the relationship between tornadogenesis and mesoscale environmental processes near the tornado were analyzed. Circulation analyses of near-surface, tornadolike vortices simulated in several ensemble members showed that the rotation of the tornadoes could be frictionally generated near the surface. However, the mechanisms responsible for generating circulation were only weakly related to the strength of the tornadoes. To identify the mesoscale processes required for tornadogenesis, mesoscale atmospheric conditions and their correlations with the strength of tornadoes were examined. The results showed that two near-tornado mesoscale factors were important for tornadogenesis: strong low-level mesocyclones (LMCs) at about 1 km above ground level and humid air near the surface. Strong LMCs and large water vapor near the surface strengthened the nonlinear dynamic vertical perturbation pressure gradient force and buoyancy, respectively. These upward forces made contributions essential for tornadogenesis via tilting and stretching of vorticity near the surface.

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Kosuke Ito
,
Masaru Kunii
,
Takuya Kawabata
,
Kazuo Saito
,
Kazumasa Aonashi
, and
Le Duc

Abstract

This paper discusses the benefits of using a hybrid ensemble Kalman filter and four-dimensional variational (4D-Var) data assimilation (DA) system rather than a 4D-Var system employing the National Meteorological Center (NMC, now known as NCEP) method (4D-Var-Bnmc) to predict severe weather events. An adjoint-based 4D-Var system was employed with a background error covariance matrix constructed from the NMC method and perturbations in a local ensemble transform Kalman filter system. The DA systems are based on the Japan Meteorological Agency’s nonhydrostatic model. To reduce the sampling noise, three types of implementation (the spatial localization, spectral localization, and neighboring ensemble approaches) were tested. The assimilation of a pseudosingle observation of sea level pressure located at a tropical cyclone (TC) center yielded analysis increments physically consistent with what is expected of a mature TC in the hybrid systems at the beginning of the assimilation window, whereas analogous experiments performed using the 4D-Var-Bnmc system did not. At the end, the structures of the 4D-Var-based increments became similar to one another, while the analysis increment by the 4D-Var-Bnmc system was broad in the horizontal direction. Realistic DA experiments showed that all of the hybrid systems provided initial conditions that yielded more accurate TC track and intensity forecasts than those achievable by the 4D-Var-Bnmc system. The hybrid systems also yielded some statistically significant improvements in forecasting local heavy rainfall events in terms of fraction skill scores when a 160 km × 160 km window size was used. The overall skills of the hybrid systems were relatively independent of the choice of implementation.

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