Linear and non-linear T–S models for the eastern North Atlantic from Argo data: Role of surface salinity observations

Linear and non-linear empirical models for salinity (S) are estimated from the Argo temperature (T) and salinity (delayed) data. This study focuses on the reconstruction of salinity in the upper 1200 m of the eastern North Atlantic Ocean, a region characterized by the presence of many different water masses. While previous studies have found it necessary to split this region by boxes to fit different polynomial models in each box, a unique model valid for the entire region is fitted here. Argo profiles are randomly distributed on two sets: one for fitting the models and one for testing them. Non-linear regressions are built using neural networks with a single hidden layer and the fitting data set is further divided into two subsets: one for adjusting the coefficients (training data) and one for early stopping of the fitting (validation data). Our results indicate that linear regressions perform better than the climatologic T–S relationship, but that non-linear regressions perform better than the linear ones. Non-linear training using a three-data subsets strategy successfully prevents overfitting even when networks with 90 neurons in the hidden layer are being trained. While the presence of local minima may complicate the generalization of non-linear models to new data, network committees (created by training the same network from different random initial weights) are shown to better reproduce the test data. Several predictors are tested, and the results show that geographical, or surface, information does provide significant information. These results highlight the potential applications of future satellite missions measuring sea-surface salinity to reconstruct, when combined with temperature profiles, vertical salinity profiles.     

Decision support using the Multistatic Tactical Planning Aid (MSTPA)

The Multistatic Tactical Planning Aid (MSTPA) is a tool currently in development at NATO Undersea Research Centre which may be used to model the performance of a given multistatic sensor network in terms of the probability of detection of a submarine, the ability to hold a track and whether such a track could be correctly classified as such. The tool therefore considers the entire chain of events from an initial calculation of signal excess, the generation of a contact considering localisation errors, followed by the subsequent tracking and classification process. In its current form, the tool may be used to plan a particular multistatic scenario through operational analysis of many Monte Carlo simulations. The future development of MSTPA will transition towards a real-time decision support tool to assist operators and planners at sea. This study introduces a number of generic decision support techniques which may be wrapped around the MSTPA tool. The acoustic performance metric that will drive decisions will of course be subject to uncertainty relating to environmental measurements and extrapolations. The effect of this uncertainty on acoustic performance is examined here. Future studies will consider the sensitivity of the eventual decision—in terms of optimum sensor positions—to the acoustic uncertainty.     

Relative performance of future altimeter systems and tide gauges in constraining a model of North Sea high-frequency barotropic dynamics

We evaluate in this paper the ability of several altimeter systems, considered separately as well as together with tide gauges, to control the time evolution of a barotropic model of the North Sea shelf. This evaluation is performed in the framework of the particular model errors due to uncertainties in bathymetry. An Ensemble Kalman Filter (EnKF) data assimilation approach is adopted, and observing-systems simulation experiments (OSSEs) are carried out using ensemble spread statistics. The skill criterion for the comparison of observing networks is, therefore, not based on the misfit between two simulations, as done in classic twin experiments, but on the reduction of ensemble variance occurring as a consequence of the assimilation. Future altimeter systems, such as the Wide Swath Ocean Altimeter (WSOA) and satellite constellations, are considered in this work. A single WSOA exhibits, for instance, similar performance as two-nadir satellites in terms of sea-level correction, and is better than three satellites in terms of model velocity control. Generally speaking, the temporal resolution of observations is shown to be of major importance for controlling the model error in these experiments. This result is clearly related to the focus adopted in this study on the specific high-frequency response of the ocean to meteorological forcing. Altimeter systems lack adequate temporal sampling for properly correcting the major part of model error in this context, whereas tide gauges, which provide a much finer time resolution, lead to better global statistical performance. When looking into further detail, tide gauges and altimetry are demonstrated to exhibit an interesting complementary character over the whole shelf, as tide gauge networks make it possible to properly control model error in a ∼100-km coastal band, while high-resolution altimeter systems are more efficient farther from the coast.     

A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment

The aim of this work is to report on an activity carried out during the 2010 Recognized Environmental Picture experiment, held in the Ligurian Sea during summer 2010. The activity was the first at-sea test of the recently developed decision support system (DSS) for operation planning, which had previously been tested in an artificial experiment. The DSS assesses the impact of both environmental conditions (meteorological and oceanographic) and non-environmental conditions (such as traffic density maps) on people and assets involved in the operation and helps in deciding a course of action that allows safer operation. More precisely, the environmental variables (such as wind speed, current speed and significant wave height) taken as input by the DSS are the ones forecasted by a super-ensemble model, which fuses the forecasts provided by multiple forecasting centres. The uncertainties associated with the DSS’s inputs (generally due to disagreement between forecasts) are propagated through the DSS’s output by using the unscented transform. In this way, the system is not only able to provide a traffic light map (run/not run the operation), but also to specify the confidence level associated with each action. This feature was tested on a particular type of operation with underwater gliders: the glider surfacing for data transmission. It is also shown how the availability of a glider path prediction tool provides surfacing options along the predicted path. The applicability to different operations is demonstrated by applying the same system to support diver operations.     

Multi-platform model assessment in the Western Mediterranean Sea: impact of downscaling on the surface circulation and mesoscale activity

Ocean Dynamics - In numerical ocean modeling, dynamical downscaling is the approach consisting in generating high-resolution regional simulations exploiting the information from coarser resolution...     

Uncertainty forecast from 3-D super-ensemble multi-model combination: validation and calibration

Measurements collected during the Recognized Environmental Picture 2010 experiment (REP10) in the Ligurian Sea are used to evaluate 3-D super-ensemble (3DSE) 72-hour temperature predictions and their associated uncertainty. The 3DSE reduces the total Root-Mean-Square Difference by 12 and 32% respectively with reference to the ensemble mean and the most accurate of the models when comparing to regularly distributed surface temperature data. When validating against irregularly distributed in situ observations, the 3DSE, ensemble mean and most accurate model lead to similar scores. The 3DSE temperature uncertainty estimate is obtained from the product of a posteriori model weight error covariances by an operator containing model forecast values. This uncertainty prediction is evaluated using a criterion based on the 2.5th and 97.5th percentiles of the error distribution. The 3DSE error is found to be on average underestimated during the forecast period, reflecting (i) the influence of ocean dynamics and (ii) inaccuracies in the a priori weight error correlations. A calibration of the theoretical 3DSE uncertainty is proposed for the REP10 scenario, based on a time-evolving amplification coefficient applied to the a posteriori weight error covariance matrix. This calibration allows the end-user to be confident that, on average, the true ocean state lies in the −2/+2 3DSE uncertainty range in 95% of the cases.