Variabilité interannuelle d'un indice d'intensité des remontées d'eau dans le secteur du cap Blanc (Mauritanie)

1985 ◽  
Vol 42 (12) ◽  
pp. 1969-1978 ◽  
Author(s):  
Robert Arfi
Keyword(s):  
Time Series ◽  
Deep Water ◽  
Coastal Waters ◽  
Water Mass ◽  
Initial Conditions ◽  
Trade Winds ◽  
Strong Activity ◽  
Northwest Africa ◽  
Progressive Return ◽  
Coastal Upwellings

The coastal waters along the Atlantic Sahara are characterized by intense hydrodynamic phenomena (upwellings, water mass alternations) throughout the year that deeply influence their productivity. Deep-water arrivals at surface levels are closely related to the local prevailing winds dominating all year long, the Northeast Trade winds. Working on a time series of 28 yr shows that far from being regular and constant during that period, the Trade winds were very variable. The coastal upwellings induced by these winds showed important intensity variations: after years of strong activity (1955–60) there was a period of minimal activity (1961–69), which was followed by a period of great irregularity (1970–82), showing a progressive return to the initial conditions. These important variations inducing deep hydrological consequences and modifications of water trophic potentialities can be related to the biological anomalies observed during the same time in the Northwest Africa fisheries.

ANALYSIS OF THE FUNCTIONING OF MARINE ECOSYSTEMS ON CHANGING THE PARAMETERS OF THE BIOLUMINESCENCE FIELD ON THE CRIMEAN BLACK SEA SHELF

2017 ◽  
Author(s):  
Svetlana Rubtsova ◽  
Svetlana Rubtsova ◽  
Natalya Lyamina ◽  
Natalya Lyamina ◽  
Aleksey Lyamin ◽  
...  
Keyword(s):  
Black Sea ◽  
Field Intensity ◽  
Deep Water ◽  
Coastal Waters ◽  
Integrated Management ◽  
Abiotic Factors ◽  
Environmental Safety ◽  
The Black Sea ◽  
Intensity Increase ◽  
First Time

The concept of a new approach to environmental assessment is offered in the system of integrated management of the resource and environmental safety of the coastal area of the Black Sea. The studies of the season and daily changeability in the bioluminescence field in the Sevastopol coastal waters has been conducted. For the first time considerable differences in the bioluminescence field seasonal changes in the surface and deep water layers and the reasons conditioning this phenomenon have been shown, using a method of multidimensional statistical analysis. The bioluminescence field vertical profile change in the Black sea coastal waters in the autumn period at night has been studied. It has been shown that according to the character of bioluminescence parameters dynamics a water column can be divided into layers: upper (0 – 35 m) and deep water (36 – 60 m). It has been revealed that life rhythms of the plankton community are the main reason for the bioluminescence field intensity variability. It has been revealed that 14-hour periodicity of the bioluminescence field is related to the changes in light and its variations with 2,5…4,5 hours are conditioned by planktonts endogenous daily rhythms. And here biotic factors effect mostly periodicity of the bioluminescence field intensity increase and fall down at the dark time of the day. Abiotic factors are of less importance in circadian rhythmic of the bioluminescence field in the neritic zone.

scholarly journals On the generation of internal waves by river plumes in subcritical initial conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
R. Mendes ◽  
J. C. B. da Silva ◽  
J. M. Magalhaes ◽  
B. St-Denis ◽  
D. Bourgault ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
Internal Waves ◽  
Coastal Waters ◽  
Initial Conditions ◽  
Spatial Scales ◽  
River Plumes ◽  
Wide Range ◽  
Near Shore ◽  
Generation Mechanisms ◽  
Douro River

AbstractInternal waves (IWs) in the ocean span across a wide range of time and spatial scales and are now acknowledged as important sources of turbulence and mixing, with the largest observations having 200 m in amplitude and vertical velocities close to 0.5 m s−1. Their origin is mostly tidal, but an increasing number of non-tidal generation mechanisms have also been observed. For instance, river plumes provide horizontally propagating density fronts, which were observed to generate IWs when transitioning from supercritical to subcritical flow. In this study, satellite imagery and autonomous underwater measurements are combined with numerical modeling to investigate IW generation from an initial subcritical density front originating at the Douro River plume (western Iberian coast). These unprecedented results may have important implications in near-shore dynamics since that suggest that rivers of moderate flow may play an important role in IW generation between fresh riverine and coastal waters.

Deep Learning Based Classification of Time Series of Chaotic Systems over Graphic Images

2021 ◽  
Author(s):  
Süleyman UZUN ◽  
Sezgin KAÇAR ◽  
Burak ARICIOĞLU
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Transfer Learning ◽  
Chaotic Systems ◽  
Initial Conditions ◽  
Step Size ◽  
Learning Methods ◽  
Data Set ◽  
Graphic Images ◽  
Different Chaotic Systems

Abstract In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

scholarly journals ESTIMATION OF AUTOREGRESSIVE ROOTS NEAR UNITY USING PANEL DATA

2000 ◽  
Vol 16 (6) ◽  
pp. 927-997 ◽  
Author(s):  
Hyungsik R. Moon ◽  
Peter C.B. Phillips
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Time Series Data ◽  
Initial Conditions ◽  
Series Data ◽  
Test Statistics ◽  
Rate Condition ◽  
Panel Models ◽  
Stochastic Trends ◽  
Efficient Extraction

Time series data are often well modeled by using the device of an autoregressive root that is local to unity. Unfortunately, the localizing parameter (c) is not consistently estimable using existing time series econometric techniques and the lack of a consistent estimator complicates inference. This paper develops procedures for the estimation of a common localizing parameter using panel data. Pooling information across individuals in a panel aids the identification and estimation of the localizing parameter and leads to consistent estimation in simple panel models. However, in the important case of models with concomitant deterministic trends, it is shown that pooled panel estimators of the localizing parameter are asymptotically biased. Some techniques are developed to overcome this difficulty, and consistent estimators of c in the region c < 0 are developed for panel models with deterministic and stochastic trends. A limit distribution theory is also established, and test statistics are constructed for exploring interesting hypotheses, such as the equivalence of local to unity parameters across subgroups of the population. The methods are applied to the empirically important problem of the efficient extraction of deterministic trends. They are also shown to deliver consistent estimates of distancing parameters in nonstationary panel models where the initial conditions are in the distant past. In the development of the asymptotic theory this paper makes use of both sequential and joint limit approaches. An important limitation in the operation of the joint asymptotics that is sometimes needed in our development is the rate condition n/T → 0. So the results in the paper are likely to be most relevant in panels where T is large and n is moderately large.

Upper ocean salinity variabilities in the Southern Ocean responding to the recent surface salting in perspective of climate changes

2021 ◽  
Author(s):  
Ling Du ◽  
Xubin Ni
Keyword(s):  
Southern Ocean ◽  
Deep Water ◽  
Water Mass ◽  
Water Cycle ◽  
Upper Ocean ◽  
Tropical Ocean ◽  
Circumpolar Deep Water ◽  
Salinity Changes ◽  
Ocean Salinity ◽  
The Antarctic

&lt;p&gt;Water cycle have prevailed on upper ocean salinity acting as the climate change fingerprint in the numerous observation and simulation works. Water mass in the Southern Ocean accounted for the increasing importance associated with the heat and salt exchanges between Subantarctic basins and tropical oceans. The circumpolar deep water (CDW), the most extensive water mass in the Southern Ocean, plays an indispensable role in the formation of Antarctic Bottom Water. In our study, the observed CTDs and reanalysis datasets are examined to figure out the recent salinity changes in the three basins around the Antarctica. Significant surface salinity anomalies occurred in the South Indian/Pacific sectors south of 60&amp;#186;S since 2008, which are connected with the enhanced CDW incursion onto the Antarctic continental shelf. Saltier shelf water was found to expand northward from the Antarctica coast. Meanwhile, the freshening of Upper Circumpolar Deep Water(UCDW), salting and submergence of Subantarctic Mode Water(SAMW) were also clearly observed. The modified vertical salinity structures contributed to the deepen mixed layer and enhanced intermediate stratification between SAMW and UCDW. Their transport of salinity flux attributed to the upper ocean processes responding to the recent atmospheric circulation anomalies, such as the Antarctic Oscillation and Indian Ocean Dipole. The phenomena of SAMW and UCDW salinity anomalies illustrated the contemporaneous changes of the subtropical and polar oceans, which reflected the meridional circulation fluctuation. Salinity changes in upper southern ocean (&lt; 2000m) revealed the influence of global water cycle changes, from the Antarctic to the tropical ocean, by delivering anomalies from high- and middle-latitudes to low-latitudes oceans.&lt;/p&gt;

scholarly journals Remote detection of water property changes from a time series of oceanographic data

Ocean Science ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 11-18 ◽  
Author(s):  
A. Henry-Edwards ◽  
M. Tomczak
Keyword(s):  
Time Series ◽  
Water Mass ◽  
Synthetic Data ◽  
Data Sets ◽  
The North ◽  
Water Property ◽  
Water Mass Analysis ◽  
Minimization Technique ◽  
Definition Of ◽  
Property Changes

Abstract. A water mass analysis method based on a constrained minimization technique is developed to derive water property changes in water mass formation regions from oceanographic station data taken at significant distance from the formation regions. The method is tested with two synthetic data sets, designed to mirror conditions in the North Atlantic at the Bermuda BATS time series station. The method requires careful definition of constraints before it produces reliable results. It is shown that an analysis of the error fields under different constraint assumptions can identify which properties vary most over the period of the observations. The method reproduces the synthetic data sets extremely well if all properties other than those that are identified as undergoing significant variations are held constant during the minimization.

Nonlinear Techniques for Signals Characterization

2011 ◽  
pp. 1266-1272
Author(s):  
Jesús Bernardino Alonso Hernández ◽  
Patricia Henríquez Rodríguez
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Order Statistics ◽  
Chaos Theory ◽  
Initial Conditions ◽  
Digital Signal ◽  
Higher Order ◽  
Higher Order Statistics ◽  
Three Body ◽  
Nonlinear Signal

The field of nonlinear signal characterization and nonlinear signal processing has attracted a growing number of researchers in the past three decades. This comes from the fact that linear techniques have some limitations in certain areas of signal processing. Numerous nonlinear techniques have been introduced to complement the classical linear methods and as an alternative when the assumption of linearity is inappropriate. Two of these techniques are higher order statistics (HOS) and nonlinear dynamics theory (chaos). They have been widely applied to time series characterization and analysis in several fields, especially in biomedical signals. Both HOS and chaos techniques have had a similar evolution. They were first studied around 1900: the method of moments (related to HOS) was developed by Pearson and in 1890 Henri Poincaré found sensitive dependence on initial conditions (a symptom of chaos) in a particular case of the three-body problem. Both approaches were replaced by linear techniques until around 1960, when Lorenz rediscovered by coincidence a chaotic system while he was studying the behaviour of air masses. Meanwhile, a group of statisticians at the University of California began to explore the use of HOS techniques again. However, these techniques were ignored until 1980 when Mendel (Mendel, 1991) developed system identification techniques based on HOS and Ruelle (Ruelle, 1979), Packard (Packard, 1980), Takens (Takens, 1981) and Casdagli (Casdagli, 1989) set the methods to model nonlinear time series through chaos theory. But it is only recently that the application of HOS and chaos in time series has been feasible thanks to higher computation capacity of computers and Digital Signal Processing (DSP) technology. The present article presents the state of the art of two nonlinear techniques applied to time series analysis: higher order statistics and chaos theory. Some measurements based on HOS and chaos techniques will be described and the way in which these measurements characterize different behaviours of a signal will be analized. The application of nonlinear measurements permits more realistic characterization of signals and therefore it is an advance in automatic systems development.

scholarly journals A Depth-Bistatic Bottom Reverberation Model and Comparison with Data from an Active Triplet Towed Array Experiment

2020 ◽  
Vol 10 (9) ◽  
pp. 3080
Author(s):  
Youngcheol Jung ◽  
Woojae Seong ◽  
Keunhwa Lee ◽  
Seongil Kim
Keyword(s):  
Time Series ◽  
Deep Water ◽  
Water Environment ◽  
Ray Theory ◽  
Reverberation Time ◽  
Environment Model ◽  
Line Array ◽  
Model Predictions ◽  
Towed Array ◽  
Good Agreement

In this paper, a depth-bistatic bottom reverberation model that employs the ray theory is presented. The model can be applied to an active towed array in the ocean. The reverberation time series are modeled under the depth-bistatic assumption and their Doppler shift is calculated based on the actual source–receiver geometry. This model can handle N × 2D range-dependent bathymetry, the geometry of a triplet array, and the Doppler motion of the source, targets, and receiver. The model predictions are compared with the mid-frequency reverberation data measured by an active triplet towed array during August 2015 in the East Sea, Korea. These data are collected with a variable depth source at mid-frequency and the triplet line array in a deep-water environment. Model predictions of the beam time series and its spectrogram are in good agreement with the measurement. In particular, we discuss the effects of the source and receiver depths on the reverberation in deep water observed in both the measured and modeled results.

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