Characteristics and Nonlinear Growth of the Singular Vector Related to a Heavy Rainfall Case over the Korean Peninsula

In this study, singular vectors related to a heavy rainfall case over the Korean Peninsula were calculated using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model （MM5） adjoint modeling system. Tangent linear and adjoint models include moist physical processes, and a moist basic state and a moist total energy norm were used for the singular-vector calculations. The characteristics and nonlinear growth of the first singular vector were analyzed, focusing on the relationship between the basic state and the singular vector. The horizontal distribution of the initial singular vector was closely related to the baroclinicity index and the moisture availability of the basic state. The temperature-component energy at a lower level was dominant at the initial time, and the kinetic energy at upper levels became dominant at the final time in the energy profile of the singular vector. The nonlinear growth of the singular vector appropriately reflects the temporal variations in the basic state. The moisture-component energy at lower levels was dominant at earlier times, indicating continuous moisture transport in the basic state. There were a large amount of precipitation and corresponding latent heat release after that period because the continuous moisture transport created favorable conditions for both convective and nonconvective precipitation. The vertical propagation of the singular-vector energy was caused by precipitation and the corresponding latent heating in the basic state.

SPARC Local Workshop on “WCRP Grand Challenges and Regional Climate Change”

1.Overview SPARC（Stratosphere-Troposphere Processes and their Role in Climate）is one of the core projects of the World Climate Research Program（WCRP）,coordinating international efforts to address relevant issues in climate and climate prediction via better understanding of the stratosphere-troposphere system.

Grasping Force Estimation by sEMG Signals and Arm Posture: Tensor Decomposition Approach

Grasping force estimation using surface Electromyography (sEMG) has been actively investigated as it can increase the manipulability and dexterity of prosthetic hands and robotic hands. Most of the current studies in this area only focus on finding the relationship between sEMG signals and the grasping force without considering the arm posture. Therefore, regression models are not suitable to predict grasping force in various arm postures. In this paper, a method to predict the grasping force from sEMG signals and various grasping postures is developed. The proposed algorithm uses a tensor algebra to train a multi-factor model relevant to sEMG signals corresponding to various grasping forces and postures of the wrist and forearm in multiple dimensions. The multi-factor model is then decomposed into four independent factor spaces of the grasping force, sEMG signals, wrist posture, and forearm posture. Moreover, when a participant executes a new posture, new factors for the wrist and forearm are interpolated in the factor spaces. Thus, the grasping force with various postures can be predicted by combining these factors. The effectiveness of the proposed method is verified through experiments with ten healthy subjects, demonstrating the higher performance of proposed grasping force prediction method than the previous algorithm.