The estimation of lower refractivity uncertainty from radar sea clutter using the Bayesian-MCMC method

The estimation of lower atmospheric refractivity from radar sea clutter（RFC） is a complicated nonlinear optimization problem.This paper deals with the RFC problem in a Bayesian framework.It uses the unbiased Markov Chain Monte Carlo（MCMC） sampling technique,which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework.In contrast to the global optimization algorithm,the Bayesian-MCMC can obtain not only the approximate solutions,but also the probability distributions of the solutions,that is,uncertainty analyses of solutions.The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar seaclutter data.Reference data are assumed to be simulation data and refractivity profiles are obtained using a helicopter.The inversion algorithm is assessed（i） by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data;（ii） the one-dimensional（1D） and two-dimensional（2D） posterior probability distribution of solutions.

Ocean internal waves interpreted as oscillation travelling waves in consideration of ocean dissipation

Most studies of the synthetic aperture radar remote sensing of ocean internal waves are based on the solitary wave solutions of the Korteweg-de Vries （KdV） equation, and the dissipative term in the KdV equation is not taken into account. However, the dissipative term is very important, both in the synthetic aperture radar images and in ocean models. In this paper, the traveling-wave structure to characterize the ocean internal wave phenomenon is modeled, the results of numerical experiments are advanced, and a theoretical hypothesis of the traveling wave to retrieve the ocean internal wave parameters in the synthetic aperture radar images is introduced.

New reconstruction and forecasting algorithm for TEC data

To reconstruct the missing data of the total electron content （TEC） observations, a new method is proposed, which is based on the empirical orthogonal functions （EOF） decomposition and the value of eigenvalue itself. It is a self-adaptive EOF decomposition without any prior information needed, and the error of reconstructed data can be estimated. The interval quartering algorithm and cross-validation algorithm are used to compute the optimal number of EOFs for reconstruction. The interval quartering algorithm can reduce the computation time. The application of the data interpolating empirical orthogonal functions （DINEOF） method to the real data have demonstrated that the method can reconstruct the TEC map with high accuracy, which can be employed on the real-time system in the future work.

Monitoring of ducting by using a ground-based GPS receiver

In this paper,we describe the estimation of low-altitude refractivity structure from simulation and real ground-based GPS delays.The vertical structure of the refractive environment is modeled using three parameters,i.e.,duct height,duct thickness,and duct slope.The refractivity model is implemented with a priori constraints on the duct height,thickness,and strength,which might be derived from soundings or numerical weather-prediction models.A ray propagation model maps the refractivity structure into a replica field.Replica fields are compared with the simulation observed data using a squarederror objective function.A global search for the three environmental parameters is performed using a genetic algorithm.The inversion is assessed by comparing the refractivity profiles from the radiosondes to those estimated.This technique could provide near-real-time estimation of the ducting effect.The results suggest that ground-based GPS provides significant atmospheric refractivity information,despite certain fundamental limitations of ground-based measurements.Radiosondes are typically launched just a few times daily.Consequently,estimates of temporally and spatially varying refractivity that assimilate GPS delays could substantially improve over-estimates caused by using radiosonde data alone.