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Oceanic fronts are regions over the oceans where a significant change in the characteristics of the water masses is observed. Advanced Very High Resolution Radiometer (AVHRR) satellite imagery over the Bay of Bengal shows regions that are populated by frontal structures. Over the Bay of Bengal, some of the strongest gradients in temperature and salinity are observed. In recent years, there has been a tremendous growth in the availability of satellite imagery and the necessity of automated fast detection of the frontal features is needed for services like potential fishing zones over open oceans. In this article, an algorithm to infer oceanic fronts over the Bay of Bengal is described using changepoint analysis. The changepoint algorithm is combined in a novel way with a contextual median filter to detect frontal features in AVHRR imagery. The changepoint analysis is a non-parametric technique that does not put thresholds on the gradients of brightness temperatures of the satellite imagery. In the open oceans, the gradients of temperature and salinity are not sharp and changepoint analysis is found to be a useful complementary technique to the existing front detecting methods when combined with contextual median filters.

Indian Scientific Expeditions to the Southern Ocean (ISESO) have been implemented since 2004 to explore the unknown areas of the Indian Sector of the SO (ISSO) with the National Centre for Polar and Ocean Research (NCPOR) as the nodal center for the Ministry of Earth Sciences, Government of India. The studies conducted in these expeditions have prime importance to understand the role of the SO on regional and global climatic variability. Surveys have been designed to explore the interconnections between physical, chemical and biological components across the Antarctic Circumpolar Current (ACC). Further aims are to understand the ocean-ice-atmosphere interaction, aerosols, other trace gases, thermohaline variations, carbon sequestration and biogeochemical processes. Valuable sediment cores have also been recovered in some critical locations for the reconstruction of the paleo-environmental conditions for selected time slices during the last glacial-interglacial cycles. This special issue presents multidisciplinary data collected during ISESO from 2010 to 2018 that shed light on the present understanding of one of the sparsely explored regions in the SO.

This work reports a new methodology for deriving monthly averages of temperature (T) and salinity (S) fields for the Indian Ocean based on the use of an artificial neural network (ANN). Investigation and analysis were performed for this region with two distinct datasets: (1) monthly climatological data for T and S fields (in 1° × 1° grid boxes) at standard depth levels of the World Ocean Atlas 1994 (WOA94), and; (2) heterogeneous randomly distributed in situ ARGO, ocean station data (OSD) and profiling (PFL) floats. A further numerical experiment was conducted with these two distinct datasets to train the neural network model. Nonlinear regression mapping utilizing a multilayer perceptron (MLP) is employed to tackle nonlinearity in the data. This study reveals that a feed-forward type of network with a resilient backpropagation algorithm is best suited for deriving T and S fields; this is demonstrated by independently using WOA94 and in situ data, which thus tests the robustness of the ANN model. The suppleness of the T and S fields derived from the ANN model provides the freedom to generate a new grid at any desired level with a high degree of accuracy. Comprehensive training, testing and validation exercises were performed to demonstrate the robustness of the model and the consistency of the derived fields. The study points out that the parameters derived from the ANN model using scattered, inhomogeneous in situ data show very good agreement with state-of-the-art WOA climatological data. Using this approach, improvements in ocean climatology can be expected to occur in a synergistic manner with in situ observations. Our investigation of the Indian Ocean reveals that this approach can be extended to model global oceans.

ISSN: 1042-8275; International audience; In only 10 years, the Argo Program has grown from an idea into a functioning global observing system for the subsurface ocean. More than 3000 Argo floats now cover the world ocean. With these instruments operating on 10-day cycles, the array provides 9000 temperature/salinity/depth profiles every month that are quickly available via the Global Telecommunications System and the Internet. Argo is recognized as a major advance for oceanography, and a success for Argo's parent programs, the Global Ocean Data Assimilation Experiment and Climate Variability and Predictability, and for the Global Earth Observation System of Systems. The value of Argo data in ocean data assimilation (ODA) and other applications is being demonstrated, and will grow as the data set is extended in time and as experience in using the data set leads to new applications. The spatial coverage and quality of the Argo data set are improving, with consideration being given to sampling under seasonal ice at higher latitudes, in additional marginal seas, and to greater depths. Argo data products of value in ODA modeling are under development, and Argo data are being tested to confirm their consistency with related satellite and in situ data. Maintenance of the Argo Program for the next decade and longer is needed for a broad range of climate and oceanographic research and for many operational applications in ocean state estimation and prediction.