The Transfer and Evolvement of Initial Temperature Perturbation in Ocean-Acoustic Coupled Process

<p>Focusing on the transfer and evolvement of initial perturbation from temperature of ocean to the underwater acoustic propagation,comparing with the remote sensing data,optimizing Ocean-Acoustic Coupled Model, the reliability of this model is verified. On this basis,global and regional error development experiments are carried out by adding perturbation on the initial temperature of a controlled test. The results show that after 5 days evolution of the initial temperature field,the global perturbation of the propagation loss is saturated, and the perturbation structure is basically consistent with the law of the dynamic ocean.For the target area in Kuroshio region, the initial perturbation in the upstream region is the fastest. This conclusion can provide the basis for the adaptive observation of ocean acoustics.</p>

Adaptive observation control for synchronization of coronary artery time-delay systems

This paper investigates novel adaptive observation control for synchronization of uncertain chaotic coronary artery system (CCAS). Novel adaptive observers are developed to obtain the estimated system states of uncertain CCAS which has unknown parameters. By utilizing the adaptive observers and controller we proposed, the adaptation of unknown parameters has been achieved and the asymptotic stability of the synchronization error as well as the estimation errors are guaranteed. For the purpose of further reducing the conservatism, new Lyapunov Krasovskii functions (LKFs) which maintain more system states have been constructed in terms of Wirtinger-based inequality, a new double integral inequality and improved reciprocally convex inequality. Using [Formula: see text] control to ensure robust performance of CCAS which has unknown parameters and external disturbance. The adaptive observer gain matrices and controller gain matrix could be obtained by employing a decoupling approach. The effectiveness of this control methodology has been illustrated by a numerical simulation result.

Non-Gaussian Probability Densities of Convection Initiation and Development Investigated Using a Particle Filter with a Storm-Scale Numerical Weather Prediction Model

Non-Gaussian probability density functions (PDFs) in convection initiation (CI) and development were investigated using a particle filter with a storm-scale numerical prediction model and an adaptive observation error estimator (NHM-RPF). An observing system simulation experiment (OSSE) was conducted with a 90-min assimilation period and 1000 particles at a 2-km grid spacing. Pseudosurface observations of potential temperature (PT), winds, water vapor (QV), and pseudoradar observations of rainwater (QR) in the lower troposphere were created in a nature run that simulated a well-developed cumulonimbus. The results of the OSSE (PF) show a significant improvement in comparison to ensemble simulations without any observations. The Gaussianity of the PDFs for PF in the CI area was evaluated using the Bayesian information criterion to compare goodness-of-fit of Gaussian, two-Gaussian mixture, and histogram models. The PDFs are strongly non-Gaussian when NHM-RPF produces diverse particles over the CI period. The non-Gaussian PDF of the updraft is followed by the upper-bounded PDF of the relative humidity, which produces non-Gaussian PDFs of QV and PT. The PDFs of the cloud water and QR are strongly non-Gaussian throughout the experimental period. We conclude that the non-Gaussianity of the CI originated from the non-Gaussianity of the updraft. In addition, we show that the adaptive observation error estimator significantly contributes to the stability of PF and the robustness to many observations.