Field Of Oceanography Research Articles

Sea Surface Temperature Prediction Using ConvLSTM-Based Model with Deformable Attention

Sea surface temperature (SST) prediction has received increasing attention in recent years due to its paramount importance in the various fields of oceanography. Existing studies have shown that neural networks are particularly effective in making accurate SST predictions by efficiently capturing spatiotemporal dependencies in SST data. Among various models, the ConvLSTM framework is notably prominent. This model skillfully combines convolutional neural networks (CNNs) with recurrent neural networks (RNNs), enabling it to simultaneously capture spatiotemporal dependencies within a single computational framework. To overcome the limitation that CNNs primarily capture local spatial information, in this paper we propose a novel model named DatLSTM that integrates a deformable attention transformer (DAT) module into the ConvLSTM framework, thereby enhancing its ability to process more complex spatial relationships effectively. Specifically, the DAT module adaptively focuses on salient features in space, while ConvLSTM further captures the temporal dependencies of spatial correlations in the SST data. In this way, DatLSTM can adaptively capture complex spatiotemporal dependencies between the preceding and current states within ConvLSTM. To evaluate the performance of the DatLSTM model, we conducted short-term SST forecasts in the Bohai Sea region with forecast lead times ranging from 1 to 10 days and compared its efficacy against several benchmark models, including ConvLSTM, PredRNN, TCTN, and SwinLSTM. Our experimental results show that the proposed model outperforms all of these models in terms of multiple evaluation metrics short-term SST prediction. The proposed model offers a new predictive learning method for improving the accuracy of spatiotemporal predictions in various domains, including meteorology, oceanography, and climate science.

Alfredo Magalhães Ramalho’s scientific correspondence: historical connections between oceanography and marine biology from Portugal (1919–1949)

This article presents Alfredo Magalhães Ramalho’s scientific correspondence between 1919 and 1949, both nationally and internationally. It consists of 181 unpublished letters, exchanged with 30 relevant figures belonging to 11 countries, including France, the UK, Croatia, Japan, Germany, Canada, Sweden, Norway, Morocco, Belgium and Spain. This correspondence shows a network among different research institutions devoted to the marine sciences. This includes biological stations, such as the Istituto Italo-Germanico di Biologia Marina, laboratories and museums, e. g. the British Museum of Natural History. At the national level, the correspondence between Ramalho and Augusto Nobre represents a historical axis between Lisbon and Porto, involving two of the most relevant scientists in the fields of oceanography and marine biology. At the international level, Ramalho corresponded with personalities such as Henry Gascoyne Maurice and other members of the International Council for the Exploration of the Sea, including for example the oceanographer Vagn Walfrid Ekman. This correspondence represents an archival discovery of the Portuguese Institute for Sea and Atmosphere in Lisbon, marking a decisive historical scientific moment for both Portuguese and international contexts in the field of marine sciences. This article aims to reconstruct Portugal’s internal and external networks.

Wave-Former: Lag removing univariate long time series forecasting transformer for ocean waves

Long-Sequence Time Series Forecasting (LSTSF) of ocean waves using univariate modelling has always been a challenging task in the field of oceanography as it demands a high prediction capacity model capable to capture precise long-range dependency between input and output variables. Inherently, it exhibits the problem of “time lag or phase shift” in predictions which is due to high autocorrelation in between time dependent sequential values. Very few researchers had tried to remove this “phase shift” problem in the univariate modelling including the author of present work wherein preprocessing of time series data was done by different methods. Many failed in gaining high prediction accuracy at longer forecasting intervals. Overcoming these limitations, present work aims for LSTSF of ocean waves ranging from 24 to 144 hours (hrs) at the three locations of Gulf of Mexico using “WaveFormer” architecture based on the Informer. Probabilistic self-attention mechanism has proved to be efficient to remove the “time lag or phase shift” in ocean waves as well as in predicting the waves at large time intervals (144 hrs). Low Root Mean Squared Errors (RMSEs) (0.045–0.100) and high correlation coefficients (0.90–0.99), low values of test loss (0.002–0.012) of all the developed models demonstrated the competency of WaveFormer.

Advancing Sea Surface Height Retrieval through Global Navigation Satellite System Reflectometry: A Model Interaction Approach with Cyclone Global Navigation Satellite System and FengYun-3E Measurements

The measurement of sea surface height (SSH), which is of great importance in the field of oceanography, can be obtained through the innovative technique of GNSS-R for remote sensing. This research utilizes the dataset from spaceborne GNSS-R platforms, the Cyclone Global Navigation Satellite System (CYGNSS) and FengYun-3E (FY-3E), as the primary source of data for retrieving sea surface height (SSH). The utilization of artificial neural networks (ANNs) allows for the accurate estimation of ocean surface height with a precision of meter-level accuracy throughout the period of 1–17 August 2022. As a traditional machine learning method, an ANN is employed to extract pertinent data features, facilitating the acquisition of precise sea surface height estimations. Additionally, separate models are devised for both GNSS-R platforms, one based on constant velocity (CV) and the other on constant acceleration (CA). The Interactive Multiple Model (IMM) is utilized as the main method to combine the four models and convert the likelihood of each model. The transition between the models allows the filters to effectively adapt to dynamic changes and complex environments. This approach relies on the fundamental notion of the Kalman filter (KF), which showcases robust noise handling capabilities in predicting the SSH, separately. The results demonstrate that the model interaction technology is capable of efficiently filtering and integrating SSH data, yielding a Root Mean Square Error (RMSE) of 1.03 m. This corresponds to a 9.84% enhancement compared to the retrieved height from CYGNSS and a 37.19% enhancement compared to the retrieved height from FY-3E. The model proposed in this paper provides a potential scheme for the GNSS-R data fusion of multiple platforms and multiple models. In the future, more data sources and more models can be added to achieve more accurate adaptive fusion.

A model for community-driven development of best practices: the Ocean Observatories Initiative Biogeochemical Sensor Data Best Practices and User Guide

The field of oceanography is transitioning from data-poor to data-rich, thanks in part to increased deployment of in-situ platforms and sensors, such as those that instrument the US-funded Ocean Observatories Initiative (OOI). However, generating science-ready data products from these sensors, particularly those making biogeochemical measurements, often requires extensive end-user calibration and validation procedures, which can present a significant barrier. Openly available community-developed and -vetted Best Practices contribute to overcoming such barriers, but collaboratively developing user-friendly Best Practices can be challenging. Here we describe the process undertaken by the NSF-funded OOI Biogeochemical Sensor Data Working Group to develop Best Practices for creating science-ready biogeochemical data products from OOI data, culminating in the publication of the GOOS-endorsed OOI Biogeochemical Sensor Data Best Practices and User Guide. For Best Practices related to ocean observatories, engaging observatory staff is crucial, but having a “user-defined” process ensures the final product addresses user needs. Our process prioritized bringing together a diverse team and creating an inclusive environment where all participants could effectively contribute. Incorporating the perspectives of a wide range of experts and prospective end users through an iterative review process that included “Beta Testers’’ enabled us to produce a final product that combines technical information with a user-friendly structure that illustrates data analysis pipelines via flowcharts and worked examples accompanied by pseudo-code. Our process and its impact on improving the accessibility and utility of the end product provides a roadmap for other groups undertaking similar community-driven activities to develop and disseminate new Ocean Best Practices.

Spatiotemporal estimation of nutrient data from the northwest pacific and east asian seas

Nutrient data obtained from field observations have the potential to enhance our understanding of oceanic biogeochemical cycling and productivity changes. In particular, long-term nutrient data can provide valuable information on the links between climate change and biogeochemical changes. However, unlike other observational variables such as sea surface temperature, nutrient data are limited in terms of their broad-scale observations and automated sensor-based measurements. In this study, we analyzed nitrate and phosphate data obtained from coastal regions in Northeast Asia and the northwest Pacific from 1980 to 2019 using the spatiotemporal kriging technique and provide results in a spatiotemporal grid format. The data are available at monthly intervals and may be attractive to researchers in the fields of oceanography, marine ecology, and marine biogeochemistry at the climate change scale. Furthermore, sharing the source code of the data production process can contribute to better long-term data reproduction in the future.

Comparison of Publicly Available Bathymetric Data with Real Measurements in the Southeastern Black Sea

Being able to access accurate and reliable depth information has uncountable benefits for not only fields of oceanography, geophysics, geology, natural resources but also navigation & logistics. There is an ever-increasing demand for high-resolution bathymetric data for those fields since only a small portion of the world seas and oceans have been explored, observed, and charted so far. There are some sources which provide publicly available global bathymetric data to its stakeholders and users such as widely used European Marine Observation and Data Network (EMODnet) and General Bathymetric Chart of the Oceans (GEBCO). In general, it is challenging for researchers to select the best fit for their studies among available datasets, due to the fact that their sole reliability is not well-assessed. The purpose of our study, thus, is to compare those publicly available bathymetric data with field measurements obtained from the surveys carried out by the Office of Navigation, Hydrography and Oceanography for an area of interest in the southeastern Black Sea comprising various characteristics as to the bottom topography (i.e. homogeneous elevation, steep slope, mild slope, etc.). Validation is conducted focusing on these distinct features by means of visual assessment and quantitative comparison. Results reveal that, even though there is an overall agreement, local discrepancies are also present. Nonetheless, GEBCO and EMODnet datasets are proved to be great assets for any hydrospatial application that does not necessarily require high spatial resolution.

A Multivariable Convolutional Neural Network for Forecasting Synoptic-Scale Sea Surface Temperature Anomalies in the South China Sea

Abstract The sea surface temperature anomaly (SSTA) plays a key role in climate change and extreme weather processes. Usually, SSTA forecast methods consist of numerical and conventional statistical models, and the former can be seriously influenced by the uncertainty of physical parameterization schemes, the nonlinearity of ocean dynamic processes, and the nonrobustness of numerical discretization algorithms. Recently, deep learning has been explored to address forecast issues in the field of oceanography. However, existing deep learning models for ocean forecasting are mainly site specific, which were designed for forecasting on a single point or for an independent variable. Moreover, few special deep learning networks have been developed to deal with SSTA field forecasts under typhoon conditions. In this study, a multivariable convolutional neural network (MCNN) is proposed, which can be applied for synoptic-scale SSTA forecasting in the South China Sea. In addition to the SSTA itself, the surface wind speed and the surface current velocity are regarded as input variables for the prediction networks, effectively reflecting the influences of both local atmospheric dynamic forcing and nonlocal oceanic thermal advection. Experimental results demonstrate that MCNN exhibits better performance than a single-variable convolutional neural network (SCNN), especially for the SSTA forecast during the typhoon passage. While forecast results deteriorate rapidly in the SCNN during the passage of a typhoon, forecast errors in the MCNN can be effectively restrained to slowly increase over the forecast time due to the introduction of the surface wind speed in this network.

High-performance computation toward large-scale underwater acoustics modelling

The complex nature of the ocean has long presented a challenge for researchers in the field of oceanography, including in the area of underwater acoustics. As a result, significant efforts have been made to develop accurate numerical models to better understand and study the ocean. Established models such as Acoustic Toolbox and Range-dependent Acoustic Models have proven to be effective for modelling sound propagation. However, these models were designed to run on single-core computers, and there is potential to optimise their performance on modern systems. The use of General-purpose graphics processing units (GPGPUs) and Single Instruction/Multiple Data (SIMD) computing units is one way to achieve this optimization. In addition to improving the computational speed of established models, these optimized models can also contribute to implementing more sophisticated modelling approaches, such as broad-band modelling. In this study, we will discuss the optimized performance of established codes, particularly those based on parabolic equation methods (PE), and also discuss the results of advanced modelling techniques.

Showcasing model performance across space and time using single diagrams

Model states are often validated against observations to establish the veracity of the model. However, when a model state is compared with multiple in-situ observations scattered in space, no single diagram exists that can showcase the statistical measure of correlation, root mean square error and the standard deviations of the concerned variables across all the locations. We present a simple but efficient representation of correlation, root mean square error and standard deviation of the model and the observation across multiple in-situ locations in a single diagram and name it as the “Performance Across Space (PAS)” diagram. We also present a diagram that showcases the comparison of model state and observation at a single location across multiple time windows in a single diagram and call it “Performance Across Time (PAT)” diagram. We highlight the effectiveness of these diagrams through realistic ocean models and realistic in-situ observations. We show using a PAT diagram that the ocean model NEMO fares better in simulating ocean sea surface temperature during boreal winters compared to boreal summers. We also illustrate how the PAS diagrams can be used in assessing the efficacy of data assimilation. Though the examples shown here are from the field of oceanography, the display mechanism can be extended to any field.

Accuracy Assessment of Recent Global Ocean Tide Models Using Tide Gauge Measurements from the East Sea of Korea

Lee, J.-C. and Lee, D-H., 2023. Accuracy assessment of recent global ocean tide models using tide gauge measurements from the East Sea of Korea. Journal of Coastal Research, 39(2), 354–359. Charlotte (North Carolina), ISSN 0749-0208. The continued rise in average sea level owing to climate change has intensified coastal damage worldwide. Therefore, various studies in the field of oceanography and hydrography including the development of ocean tidal prediction models are in progress. In this study, the accuracy of the recent ocean tide models using the long and short-term tide gauge measurements from seven tidal gauging stations and 10 tidal benchmarks at the coastal area in the East Sea of Korea was assessed. For this purpose, a total of five ocean tide models, DTU10, FES2014, OSU12, NAO99, and TPXO9 were used. The accuracy of the four major tidal components (M2, S2, K1, and O1) were assessed based on the same components determined from direct tide gauge measurements in east coastal area of Korea. The tide gauge measurements used in this study consisted of long-term observation data of over 1.7 years on average from seven permanent tide gauge stations and short-term observation data of about 6.6 months on average from 10 tidal benchmarks. Observation and determination of tidal components were performed by the Korea Hydrographic and Oceanographic Agency. The accuracy of each ocean tide model was assessed by comparing the amplitude of four major components with those from tide gauge measurements for each location of tide gauge. The lowest accuracy of modelled component values was detected in the M2, S2, K1, and O1 tidal components of NAO99 model. On the contrary, FES2014 was the most accurate in M2, S2 tidal components and TPXO9 was most accurate in the K1, O1 tidal components in the East Sea of Korea.

SAR Object Detection Using a Novel Convolutional Neural Network Architecture

In recent years, the importance of SAR (synthetic aperture radar) image analysis is growing day by day due to the vast applications in the field of oceanography, military war, land observation, agriculture, disaster management and geography. These applications required accurate classification of highresolution SAR images. In the present study, two different machine learning techniques are applied on the SAR dataset to analyze the classification accuracy. The first classification technique is SVM (support vector machine) along with principal component analysis (PCA) for feature reduction. While, in the second technique, a novel CNN (convolutional neural network) has been proposed to perform the classification on SAR images. Essential experimental analysis has been carried out based on the MSTAR dataset. Experimental results of the current study showed that the proposed CNN model outperformed the SVM classifier with an accuracy of 98.69%

Observation of a giant nonlinear wave-packet on the surface of the ocean

In many physical systems such as ocean waves, nonlinear optics, plasma physics etc., extreme events and rare fluctuations of a wave field have been widely observed and discussed. In the field of oceanography and naval architecture, their understanding is fundamental for a correct design of platforms and ships, and for performing safe operations at sea. Here, we report a measurement of an impressive and unique wave packet recorded in the Bay of Biscay in the North-East of the Atlantic Ocean. An analysis of the spatial extension of the packet that includes three large waves reveals that it extents for more than 1 km, with individual crests moving faster than 100 km/h. The central and largest wave in the packet was 27.8 m high in a sea with significant wave height of 11 m. A detailed analysis of the data using the nonlinear Fourier analysis reveals that the wave packet is characterized by a non trivial nonlinear content. This observation opens a new paradigm which requires new understanding of the dynamics of ocean waves and, more in general, of nonlinear and dispersive waves.

Evolutionary dynamics of solitary wave profiles and abundant analytical solutions to a (3+1)-dimensional burgers system in ocean physics and hydrodynamics

In the fields of oceanography, hydrodynamics, and marine engineering, many mathematicians and physicists are interested in Burgers-type equations to show the different dynamics of nonlinear wave phenomena, one of which is a (3+1)-dimensional Burgers system that is currently being studied. In this paper, we apply two different analytical methods, namely the generalized Kudryashov (GK) method, and the generalized exponential rational function method, to derive abundant novel analytic exact solitary wave solutions, including multi-wave solitons, multi-wave peakon solitons, kink-wave profiles, stripe solitons, wave-wave interaction profiles, and periodic oscillating wave profiles for a (3+1)-dimensional Burgers system with the assistance of symbolic computation. By employing the generalized Kudryashov method, we obtain some new families of exact solitary wave solutions for the Burgers system. Further, we applied the generalized exponential rational function method to obtain a large number of soliton solutions in the forms of trigonometric and hyperbolic function solutions, exponential rational function solutions, periodic breather-wave soliton solutions, dark and bright solitons, singular periodic oscillating wave soliton solutions, and complex multi-wave solutions under various family cases. Based on soft computing via Wolfram Mathematica, all the newly established solutions are verified by back substituting them into the considered Burgers system. Eventually, the dynamical behaviors of some established results are exhibited graphically through three - and two-dimensional wave profiles via numerical simulation.

Interference identification and removal of seismic precursor observation data based on singular spectrum analysis method

The singular spectrum analysis (SSA) method has the characteristics that it is not affected by the noise spectrum distribution, and is superior to the traditional denoising method. Moreover, it has been commonly used in signal analysis in the fields of oceanography, machinery, and electronic technology. This paper systematically introduces the basic principle and implementation process of singular spectrum analysis method. Then the singular spectrum analysis method is applied to the interference identification and removal of seismic precursor observation data. Taking deformation data and geoelectric field observation data as an example, the results show that the singular spectrum analysis method can effectively separate the rainfall interference in deformation observation data, and has important guiding significance for the subsequent removal of rainfall interference. In addition, the singular spectrum analysis method can be used to extract and remove the interference of HVDC and subway from the geoelectric field observation data, and the effect is obvious, which can effectively improve the data quality and better serve the earthquake analysis and prediction.

Construction on Morphology of Aquatic Animals via Moving Least Squares Method

Unmanned underwater vehicle (UUV) is an effective instrument to allow human to execute hazardous underwater mission without being exposed to life risk. Recently, UUV has gained increased popularity in the field of oceanography, underwater archaeology, underwater cable examination, military usage and hydrodynamic survey operation. Since aquatic animals have great capability to circumvent the drag underwater for efficient swimming compared with the torpedo-like man-made submarine, the morphology of aquatic animals shall be investigated in order to improve the efficiency of UUV. However, the complexity and non-linearity of their body shapes has hindered the formation of its morphological equations. Hence, this research aims to apply the mathematical model of moving least squares method to construct continuous equations which describe the bionic body shape of batoid and dolphin. The work will render geometrical support for computer-aided design and fluid dynamics analysis in the future.

Acoustical ocean ecology in the era of the robot revolution

Over the past decade, the field of oceanography has experienced a robotic revolution. A proliferation of autonomous surface and underwater platforms is providing new ways to obtain environmental, chemical, and biological data. Bioacoustical oceanographic sensors are increasingly being added to the payload of these platforms to provide increased spatial and temporal coverage as well as access to unexplored areas. Integrating relatively large, high power draw sensors like scientific echosounders is a challenge, however, and each platform type and size have specific tradeoffs with respect to these payloads. I will present case studies employing echsounders from autonomous surface vessels to study the deep scattering layer, a deep-diving, short endurance autonomous underwater vehicle for examining bathypelagic squid, and a moderate endurance underwater glider for examining the relationship between forage species and their environment. These studies illustrate the challenges of integration of echsosounder payloads into platforms as well as the opportunities of robotic approaches for bioacoustical oceanography. Finally, a recent experiment integrating nine robotic platforms and a cabled observatory will highlight how the integration of various sensor technologies and platforms can be used to study dynamic biological processes like diel vertical migration in the ocean.

Observations, modeling, and inter-comparison of waves from deep to intermediate waters in the East Mediterranean basin

Wind waves are of great importance in various fields of oceanography and engineering. Although they propagate along entire seas and oceans, due to the complexity and high costs in setting deep water measurements, they are mostly measured only in nearshore regions. The connection between deep sea wave conditions and nearshore ones is commonly done using wave propagation models. Nevertheless, due to the scarcity of deep water measurements, the validity of this process is seldomly tested. This work presents newly acquired data for investigating wave evolution from the deep sea far beyond the continental shelf to intermediate coastal waters. The deep water wave measurements were obtained using an upward-looking Acoustic Doppler Current Profiler (ADCP) mounted on a submerged buoy (35 m depth) in the East Mediterranean 50 km offshore Israel (bottom depth of 1500 m). To the best of our knowledge, it is the first time that such a deep sea measurement is carried out in the region. Another ADCP was deployed on the sea bed (26 m depth) 2 km offshore. During this campaign, a substantial storm (Hm0=6 m) was recorded simultaneously by both ADCPs. A wave evolution model accounting for triad and quartet interactions was employed for connecting the two measurements, in order to explain the differences between the two measurements with numerical estimations. A new methodology was developed for resolving spatial variability in the deep sea conditions by assimilating the deep water data into the output of an operational wave model for the Mediterranean Sea. This allowed forming appropriate boundary conditions for propagation of the wave field from deep to coastal waters. Model results show an importance of accounting for inhomogeneity of the wave field in deep water. Nevertheless, the measurements indicate that one cannot expect to fully resolve the exact wave spectra only by applying wave modeling. This emphasizes the importance of conducting such deep wave measurements.

How (Not) to Run an Oceanography Field Course

How (Not) to Run an Oceanography Field Course

A survey on visual analysis of ocean data

A major challenge in analysis of huge amounts of ocean data is the complexity of the data and the inherent complexity of ocean dynamic processes. Interactive visual analysis serves as an efficient complementary approach for the detection of various phenomena or patterns, and correlation exploring or comparing multiple variables in researchers daily work. Firstly, this paper presents a basic concept of the ocean data produced from numerous measurement devices or computer simulations. The characteristics of ocean data and the related data processing techniques are also described. Secondly, the main tasks of ocean data analysis are introduced. Based on the main analysis tasks in the field of oceanography, the survey emphasizes related interactive visualization techniques and tools from four aspects: visualization of multiple ocean environmental elements and multivariate analysis, ocean phenomena identification and tracking, patterns or correlation discovery, ensembles and uncertainties exploration. Finally, the opportunities are discussed for future studies.