Data assimilation for precipitation nowcasting using Bayesian inference

Nowcasting of Precipitation

Bayesian Inference for Data Assimilation using Least-Squares Finite Element Methods

It has recently been observed that Least-Squares Finite Element methods (LSFEMs) can be used to assimilate experimental data into approximations of PDEs in a natural way, as shown by Heyes et al. in the case of incompressible Navier-Stokes flow [1]. The approach was shown to be effective without regularization terms, and can handle substantial noise in the experimental data without filtering. O...

Ensemble Assimilation of Nonconventional Observations for Nowcasting

The final goal of this project is to provide the US Navy with an increased capability of using Doppler radar observations in the detection and prediction of hazardous weather events that usually have a strong randomness in nature and affect the Navy operations, especially over oceans and in remote areas. By developing a high-resolution data assimilation capability that can effectively assimilat...

Bayesian data assimilation

Bayesian Inference for Ordinal Data Using Multivariate Probit Models

Multivariate ordinal data arise in many areas of applications. This paper proposes new efficient methodology for Bayesian inference for multivariate probit models using Markov chain Monte Carlo techniques. The key idea for our approach is the novel use of parameter expansion to sample correlation matrices. We also propose methodology for model selection. Our approach is demonstrated through sev...