Uncertainty in predictions of oil spill trajectories in open sea

A method is introduced to calculate and to account for the uncertainties in the predictions of oil spill trajectories using a classic oil spill model. The method considers the output of the oil spill model as a function of random variables, which are the input parameters, and calculates the standard deviation of the output results which gives a measure of the uncertainty of the model given the uncertainties of the input parameters.Instead of a single trajectory that is calculated by the oil spill model using the mean values of the parameters, a band of trajectories can be defined when various simulations are done taking into account the uncertainties of the input parameters. This band of trajectories defines envelopes of the trajectories that are likely to be followed by the spill given the uncertainties of the input.The method is applied to an oil spill that occurred in open sea near Madeira Islands, in the Atlantic Ocean, in December 1989. The simulations allow the understanding of how a change in the wind direction drove the spill towards the Islands.The envelope of likely trajectories that is obtained with the uncertainty modelling shows a band of trajectories that is in better agreement with the observations than the single trajectory simulated by the oil spill model, based on mean parameters.     

Simulation of Pollutant Transport in Marmaris Bay

The circulation pattern and the pollutant transport in the Marmaris Bay are simulated by the developed three-dimensional baroclinic model. The Marmaris Bay is located at the Mediterranean Sea coast of Turkey. Since the sp ring tidal range is typically 20- 30 cm, the dominant forcing for the circulation and water exchange is due to the wind action. In the Marmaris Bay, there is sea outfall discharging directly into the bay. and that threats the bay water quality significantly. The current patterns in the vicinity of the outfall have been observed by tracking drogues which are moved by currents at different water depths. In the simulations of pollutant transport, the coliforms-counts is used as the tracer. The model provides realistic predictions for the circulation and pollutant transport in the Marmaris Bay. The transport model component predictions well agree with the results of a laboratory model study.     

贡湖及其相关水域风生流模拟研究

在太湖风生流的数值模拟研究的基础上，设计模拟贡湖及其相关水域风生流的套网格数值模式。应用该模式研究不同风情下，梅梁湖，太湖主体湖水对贡湖的影响。     

From 2D TO 3D Through Modelling Based On A Single Image

Three-dimensional modelling from single images remains an interesting topic of investigation in the research community, even though range sensors are becoming a common alternative for the generation of 3D information. The interest in single-image-based modelling is motivated by a wide spectrum of applications such as cultural heritage, civil engineering, urban planning and even criminology. In this paper a complete new production flowline is presented for modelling based on a single image. The modelling process consists of a series of familiar steps in photogrammetry and computer vision: feature extraction, vanishing point computation, camera self-calibration, 3D reconstruction and dimensional analysis. In particular, the methodology developed for single-image-based modelling takes a scientific approach combining several proven techniques with robust estimators. Finally, in order to demonstrate its capabilities, the reported examples include several real situations applied in different contexts.     

A data-driven approach to local gravity field modelling using spherical radial basis functions

We propose a methodology for local gravity field modelling from gravity data using spherical radial basis functions. The methodology comprises two steps: in step 1, gravity data (gravity anomalies and/or gravity disturbances) are used to estimate the disturbing potential using least-squares techniques. The latter is represented as a linear combination of spherical radial basis functions (SRBFs). A data-adaptive strategy is used to select the optimal number, location, and depths of the SRBFs using generalized cross validation. Variance component estimation is used to determine the optimal regularization parameter and to properly weight the different data sets. In the second step, the gravimetric height anomalies are combined with observed differences between global positioning system (GPS) ellipsoidal heights and normal heights. The data combination is written as the solution of a Cauchy boundary-value problem for the Laplace equation. This allows removal of the non-uniqueness of the problem of local gravity field modelling from terrestrial gravity data. At the same time, existing systematic distortions in the gravimetric and geometric height anomalies are also absorbed into the combination. The approach is used to compute a height reference surface for the Netherlands. The solution is compared with NLGEO2004, the official Dutch height reference surface, which has been computed using the same data but a Stokes-based approach with kernel modification and a geometric six-parameter “corrector surface” to fit the gravimetric solution to the GPS-levelling points. A direct comparison of both height reference surfaces shows an RMS difference of 0.6 cm; the maximum difference is 2.1 cm. A test at independent GPS-levelling control points, confirms that our solution is in no way inferior to NLGEO2004.