Bayesian estimation/inversion is commonly used to quantify and reduce modeling uncertainties in coastal ocean model, especially in the framework of parameter estimation. Based on Bayes rule, the posterior probability distribution function (pdf) of the estimated quantities is obtained conditioned on available data. It can be computed either directly, using a Markov Chain Monte Carlo (MCMC) approach, or by sequentially processing the data following a data assimilation approach, which is heavily exploited in large dimensional state estimation problems. The advantage of data assimilation schemes over MCMC-type methods arises from the ability to algorithmically accommodate a large number of uncertain quantities without significant increase in the computational requirements. However, only approximate estimates are generally obtained by this approach due to the restricted Gaussian prior and noise assumptions that are generally imposed in these methods. This contribution aims at evaluating the effectiveness of utilizing an ensemble Kalman-based data assimilation method for parameter estimation of a coastal ocean model against an MCMC Polynomial Chaos (PC)-based scheme. We focus on quantifying the uncertainties of a coastal ocean ADCIRC model with respect to the Manning's n coefficients. Based on a realistic framework of observation system simulation experiments (OSSEs), we apply an ensemble Kalman filter and the MCMC method employing a surrogate of AD

Ensemble Kalman (EnKF) filtering is an established framework for large scale state estimation problems. EnKFs can also be used for state-parameter estimation, using the so-called "Joint-EnKF" approach. The idea is simply to augment the state vector with the parameters to be estimated and assign invariant dynamics for the time evolution of the parameters. In this contribution, we investigate the efficiency of the Joint-EnKF for estimating spatiallyvarying Manning's n coefficients used to define the bottom roughness in the Shallow Water Equations (SWEs) of a coastal ocean model.Observation System Simulation Experiments (OSSEs) are conducted using the ADvanced CIRCulation (ADCIRC) model, which solves a modified form of the Shallow Water Equations. A deterministic EnKF, the Singular Evolutive Interpolated Kalman (SEIK) filter, is used to estimate a vector of Manning's n coefficients defined at the model nodal points by assimilating synthetic water elevation data. It is found that with reasonable ensemble size (O(10)), the filter's estimate converges to the reference Manning's field. To enhance performance, we have further reduced the dimension of the parameter search space through a Karhunen-Loéve (KL) expansion. We have also iterated on the filter update step to better account for the nonlinearity of the parameter estimation problem. We study the sensitivity of the system to the ensemble size, localization scale, dimension of retained KL modes, and number of iterations. The performance of the proposed framework in term of estimation accuracy suggests that a well-tuned Joint-EnKF provides a promising robust approach to infer spatially varying seabed roughness parameters in the context of coastal ocean modeling.

• The relative merits and trade-offs of diverse observational data streams on ocean state estimation and forecasting are assessed using two high-resolution reanalysis products of the East Australian Current (EAC) system • A reanalysis product that assimilates all available observations better represents the ocean state compared to one that assimilates traditionally available observations only. In particular, submesoscale surface vorticity and mesoscale eddies are effectively constrained through assimilation of radar-derived nearshore surface velocities and subsurface glider observations • In the forecast, the system that assimilates only traditionally available observations displays similar surface and subsurface predictive skill to the system assimilating all available observations

A new approach is developed for efficient data assimilation into adaptive mesh simulations with the ensemble Kalman filter (EnKF). The EnKF is combined with a wavelet-based multi-resolution analysis (MRA) scheme, namely to enable robust and efficient assimilation in the context of reducedcomplexity, adaptive spatial discretization. The wavelet representation of the solution enables us to use a different meshes that are individually adapted to the corresponding member of the EnKF ensemble. The analysis step of the EnKF is then performed by involving coarsening, refinement, and projection operations on the members. Depending on the choice of these operations, five variants of the MRA-EnKF are introduced, and tested on the one dimensional Burgers equation with periodic boundary condition. The numerical results suggest that, given an appropriate tolerance value for the coarsening operation, four out of the five proposed schemes significantly reduce the computational complexity of the data assimilation, with marginal accuracy loss with respect to the reference EnKF solution. Overall, the proposed framework offers the possibility of capitalizing on the advantages adaptive mesh techniques, and the flexibility of choosing suitable context-oriented criteria for efficient data assimilation.

this paper introduces a new approach for visualizing multidimentional weather-direction-related time-series data sets called "3D Spring Model". Spring Model is designed to visualize pattern behind large time-series weather data set and to clearify seasonal structure in the data. In addition, it supports visibility of seasonal shift and wind direction anomaly by direct comparison betweem successive spring cycles. The visualization contained three data types: (1) Weather parameter (such as windrun, temperature or rainfall etc.), (2) Wind directions and (3) time. We mapped the color to the model in such the way that it comply with human perception using color gradient. Level-Of-Detail scheme is applied and adjustable resulting different pattern time focus for users. Spring Model is highly self-contained for accumulative long term data. It is interactive, flexible and userfriendly. Spring Model is very well-suited to high computing power visualization environment. At the end of the paper, the observation of weather pattern in Nakhon Si Thammarat, Thailand using Spring Model was proposed as the case study to present the model vast applications.