Oceanic Phenomena Research Articles

Symmetry Reductions, Cte Method and Interaction Solutions for Sharma-Tasso-Olver-Burgers Equation

In this paper, the Sharma-Tasso-Olver-Burgers (STOB) system is analyzed by the Lie point symmetry method. The hypergeometric wave solution of the STOB equation is derived by symmetry reductions. In the meantime, the consistent tanh expansion (CTE) method is applied to the STOB equation. An nonauto-Bäcklund (BT) theorem that includes the over-determined equations and the consistent condition is obtained by the CTE method. By using the nonauto-BT theorem, the interactions between one-soliton and the cnoidal wave, and between one-soliton and the multiple resonant soliton solutions, are constructed. The dynamics of these novel interaction solutions are shown both in analytical and graphical forms. The results are potentially useful for explaining ocean phenomena.

Extreme anomalies under El Niño and La Niña developing tendencies restrain subsequent severe typhoons in the western North Pacific

Forecasting typhoon features over a few upcoming months is necessary for planning disaster prevention and predicting the drought/flood situation. However, the current methods are more suitable for forecasting the interannual to interdecadal variation trends or demanding huge computing resources and are unsuitable for the operation of short-term climate forecasts. In order to devise other effective approaches, the correlation between the Oceanic Niño Index (ONI) in January and the frequency of severe typhoons (STYs) during 1959–2018 was analyzed for the two scenarios when the ONI trend during the previous year was higher and lower than zero. The reason for this classification is that the air‒sea conditions were different when the oceanic phenomena developed toward an El Niño or La Niña phase during the previous year, and the results of these trends affect the subsequent features of atmospheric dynamic factors favorable for TC development (relative vorticity and vertical velocity). The extreme anomalies under both the increasing and decreasing tendencies of ONI in January typically retrained the growth of STYs, as the subsequent decreasing ocean heat anomaly of the previous year was accompanied by a negative vorticity anomaly and downward vertical velocity anomaly in the lower atmosphere and vice versa. Results from the forecast test show that the accuracy of the new approach was acceptable and somewhat higher than that of the current methods. Thus, these results can assist in developing a practical approach for typhoon forecasts.

A Novel Investigation of Non-Periodic Snap BVP in the G-Caputo Sense

In the present paper, we consider a nonlinear fractional snap model with respect to a G-Caputo derivative and subject to non-periodic boundary conditions. Some qualitative analysis of the solution, such as existence and uniqueness, are investigated in view of fixed-point theorems. Moreover, the stabilities of Ulam–Hyers and Ulam–Hyers–Rassias criterions are considered and investigated. Some numerical simulations were performed using MATLAB for understanding the theoretical results. All results in this work play an important role in understanding ocean engineering phenomena due to the huge applicability of jerk and snap in seakeeping, ride comfort, and shock response spectrum.

Numerical study on advective fog formation and its characteristic associated with cold water upwelling.

Recent rapid industrial development in the Korean Peninsula has increased the impacts of meteorological disasters on marine and coastal environments. In particular, marine fog driven by summer cold water masses can inhibit transport and aviation; yet a lack of observational data hinders our understanding of this phenomena. The present study aimed to analyze the differences in cold water mass formation according to sea surface temperature (SST) resolution and its effects on the occurrence and distribution of sea fog over the Korean Peninsula from June 23-July 1, 2016, according to the Weather Research and Forecasting model. Data from the Final Operational Model Global Tropospheric Analyses were provided at 1° and 0.25° resolutions and NOAA real-time global SST (RTG-SST) data were provided at 0.083°. While conventional analyses have used initial SST distributions throughout the entire simulation period, small-scale, rapidly developing oceanic phenomena (e.g., cold water masses) lasting for several days act as an important mediating factor between the lower atmosphere and sea. RTG-SST was successful at identifying fog presence and maintained the most extensive horizontal distribution of cold water masses. In addition, it was confirmed that the difference in SST resolution led to varying sizes and strengths of the warm pools that provided water vapor from the open sea area to the atmosphere. On examining the horizontal water vapor transport and the vertical structure of the generated sea fog using the RTG-SST, water vapors were found to be continuously introduced by the southwesterly winds from June 29 to 30, creating a fog event throughout June 30. Accordingly, high-resolution SST data must be input into numerical models whenever possible. It is expected that the findings of this study can contribute to the reduction of ship accidents via the accurate simulation of sea fog.

The spring–neap variability and mechanisms of long-term variations in the upwelling at the tip of Liaodong Peninsula, China

Upwelling is one of the important phenomena in oceans which plays a significant role in marine hydrology and marine ecological systems. During the summer stratified season, there are usually cold patches around the tip of Peninsula. Using the satellite sea surface temperature (SST) data from 1999 to 2018, this study showed that there was a pod-like cold water patch with two cold centers around the tip of Liaodong Peninsula, China. The range and the intensity of upwelling indicated an increasing trend during 1999–2018. We used a two-dimensional hydrodynamic model Mike21 to study the impact of the tidal current to the intra-month variations of upwelling (DHI 2009). The variability of tidal currents brought the significant spring–neap variations of the upwelling, showing that the SST of upwelling during the spring tide was lower about 1°C and the range of the cold water extended farther than that during the neap tide. The variations of upwelling area lagged behind the tide range about 4 days. The mechanisms of long-term variations of the upwelling were analyzed using the remote sensing wind data and the reanalysis ocean current data. Summer surface wind in the Bohai Sea suppressed the upwelling through the inshore Ekman transports and the effect of the wind curls could be ignored. The dispersion of geostrophic current around the Laotieshan Channel due to the bottom friction could drive the upward vertical velocities. Both of wind and current contributed to the multi-year variations of the upwelling around the tip of Liaodong Peninsula, China.

NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

Abstract We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusing on environmental science applications. Our institute focuses on developing trustworthy AI methods for weather, climate, and coastal hazards. The AI methods will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and will be utilized by diverse, professional user groups to reduce risks to society. In addition, we are creating novel educational paths, including a new degree program at a community college serving underrepresented minorities, to improve workforce diversity for both AI and environmental science.

Global Mesoscale Ocean Variability from Multiyear Altimetry: An Analysis of the Influencing Factors

Abstract Sea surface slope (SSS) responds to oceanic processes and other environmental parameters. This study aims to identify the parameters that influence SSS variability. We use SSS calculated from multiyear satellite altimeter observations and focus on small resolvable scales in the 30–100-km wavelength band. First, we revisit the correlation of mesoscale ocean variability with seafloor roughness as a function of depth, as proposed by Gille et al. Our results confirm that in shallow water there is statistically significant positive correlation between rough bathymetry and surface variability, whereas the opposite is true in the deep ocean. In the next step, we assemble 27 features as input variables to fit the SSS with a linear regression model and a boosted trees regression model, and then we make predictions. Model performance metrics for the linear regression model are R2 = 0.381 and mean square error = 0.010 μrad2. For the boosted trees model, R2 = 0.563 and mean square error = 0.007 μrad2. Using the hold-out data, we identify the most important influencing factors to be the distance to the nearest thermocline boundary, significant wave height, mean dynamic topography gradient, and M2 tidal speed. However, there are individual regions, that is, the Amazon outflow, that cannot be predicted by our model, suggesting that these regions are governed by processes that are not represented in our input features. The results highlight both the value of machine learning and its shortcomings in identifying mechanisms governing oceanic phenomena.

Internal Variability of All‐Sky and Clear‐Sky Surface Solar Radiation on Decadal Timescales

AbstractInternal variability comprises all processes that occur within the climate system without any natural or anthropogenic forcing. Climate‐driving variables like the surface solar radiation (SSR) are shown to exhibit unforced trends (i.e., trends due to internal variability) of magnitudes comparable to the magnitude of the forced signal even on decadal timescales. We use annual mean data from 50 models participating in the preindustrial control experiment (piControl) of the Coupled Model Intercomparison Project‐Phase 6 (CMIP6) to give quantitative grid‐box specific estimates of the magnitudes of unforced trends. To characterize a trend distribution, symmetrical around 0, we use the 75th percentile of all possible values, which corresponds to a positive trend with 25% chance of occurrence. For 30‐year periods and depending on geographical location, this trend has a magnitude between 0.15 and 2.1 W m−2/decade for all‐sky and between 0.04 and 0.38 W m−2/decade for clear‐sky SSR. The corresponding area‐weighted medians are 0.69 W m−2/decade for all‐sky trends and 0.17 W m−2/decade for clear‐sky trends. The influence of internal variability is on average six times smaller in clear‐sky, compared to all‐sky SSR. The relative uncertainties in the physical representation, derived from the CMIP6 inter‐model spread, are ±32% for all‐sky and ±43% for clear‐sky SSR trends. Reasons for differences between models like horizontal resolution, aerosol handling, and the representation of atmospheric and oceanic phenomena are investigated. The results can be used in the analysis of observational time series by attributing a probability for a trend to be caused by internal variability, given its magnitude, length, and location.

SENTINEL-1 SAR FOR OPERATIONAL MONITORING OF SOUTHERN AFRICA OCEANS

Abstract. The introduction of Synthetic Aperture Radar (SAR) through the Oceans and Coasts Monitoring Information system (OCIMS) has therefore become a critical tool for operational coastal and ocean monitoring in South Africa. Natural/man-made ocean phenomena can be observed using satellite Synthetic Aperture Radar (SAR) techniques, making it an integral part of large ocean area monitoring for maritime domain awareness. SAR is an active instrument on-board a satellite platform that transmits electromagnetic waves and measures the returning signal to infer what is happening on the ocean surface. The objective of this paper was to provide information and present options to the decision-makers around sustainability of commercial SAR data acquisition in support of OCIMS IVT. The OCIMS IVT tool identifies ships from multiple types of SAR data, which can provide locations of vessels that have switched off their AIS and oil slicks caused by vessels. Although open-data offerings, such as Sentinel-1, can be considered alternatives to commercial sensors, they present severe limitations for an operational service. To meet OCIMS requirements, and address the imperative to reduce costs, a hybrid of commercial acquisition being used in conjunction with an open data source such as Sentinel-1 is recommended. This would ensure that an operational service with the desired latency as well as increasing the revisit time over the EEZ.

Numerical Simulation of the Force Acting on the Riser by Two Internal Solitary Waves

An internal wave is a typical dynamic process. As an internal wave, an internal solitary wave usually occurs between two layers of fluids with different densities. Compared with general internal waves, internal solitary waves have large amplitudes, fast propagation speeds, short-wave periods, and often have tremendous energy. The propagation causes strong convergence and divergence of seawater and generates a sudden strong current. Due to its various characteristics, the propagation of internal solitary waves can cause serious harm to offshore engineering structures. Therefore, studying the effects of internal solitary waves on risers is vital in preventing environmental pollution caused by riser damage. Although the research on internal solitary waves has achieved very fruitful results, the research on structures is mostly focused on a single condition, and the occurrence of internal solitary wave, as a complex ocean phenomenon, is often accompanied by many situations. Therefore, this paper constructs a numerical simulation of the interaction between two columns of internal solitary waves and risers. This study explores the force and flow field changes of the riser under the condition of multiple internal solitary waves using the Star-CCM+ software in the simulation. The improved K-epsilon turbulence model was adopted to close the three-dimensional incompressible Navier–Stokes equation, and the solitary wave solution of the eKdV equation was used as the initial and boundary conditions. The interaction between single and double internal solitary waves and a riser was calculated, compared, and analyzed using numerical analysis. The experiment results indicate that the conditions of two internal solitary waves differ from those of a single internal solitary wave. After colliding at the riser, the waves gradually merge into a single wave, and the flow field reaches its minimum velocity. Under the two-wave condition, the horizontal force on the riser as a whole is less than the single-wave condition. As the amplitude difference between the two internal solitary waves gradually decreases, the horizontal opposing force received by the riser first increases and then decreases, while the horizontal positive force gradually decreases.

A feedback control strategy for improving the motion accuracy of underwater gliders in currents: Performance analysis and parameter optimization

As a promising autonomous observation platform for ocean phenomena, underwater glider can achieve space motion by adjusting its net buoyancy and attitude. In some high accuracy exploration missions, underwater glider needs to move on a specific trajectory, and the position that the glider resurfaces should be accurate enough. In fact, many uncertain factors will affect the glider motion and reduce the motion accuracy, especially the current. In this paper, we present a feedback control strategy for improving the glider motion accuracy in currents. The feedback control is achieved by intermittently adjusting the movable mass block rotation amount, which is energy-saving. Especially, the feedback control strategy considers the force condition change of the glider in different water depths and the different motion states of the glider in currents. Besides, we introduce the surrogate models to calculate the adjustment time length of movable mass block rotation amount, which can ensure that the feedback control has a higher reaction speed. To verify the effectiveness of the above strategy, the dynamic model of underwater glider is established, and the model is validated by using experimental data. The simulation results under specific working conditions illustrate that the feedback control strategy can effectively improve the glider motion accuracy in currents. To achieve the optimal control, we further propose a method to optimize the relevant parameters of feedback control strategy, considering the multiple working conditions and the energy consumption metric. The simulation results under specific working conditions verify the effectiveness of proposed optimization method. The research works can provide certain guidance for the achievement of the glider precision operation.

A Novel Method of Trajectory Optimization for Underwater Gliders Based on Dynamic Identification

For underwater gliders (UGs), high trajectory accuracy is an important factor in improving the observation of ocean phenomena. In this paper, a novel method of trajectory optimization is proposed to increase the trajectory accuracy of UGs, which is approximately based on the nonlinear dynamic model, rather than the linearization model. Firstly, a dynamic model of UGs is established to analyze the effect of the input parameters on the trajectory error, based on some approximate models that replaced the dynamic model due to its strong nonlinearity. Then, an identification strategy for the trajectory error is proposed, and the trajectory optimization strategy is analyzed while considering gliding range loss and observation distance loss. Finally, the identification strategy and trajectory optimization strategy proposed in this paper are verified by a sea trial of Petrel-L. The dynamic model, identification strategy, and optimization strategy are appropriate for other UGs.

High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things.

Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and temporal convolutional network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.

Spatial asymmetry of nonlinear internal waves in the Lombok Strait

Nonlinear internal waves (NLIWs) are an energetic oceanic phenomenon near the Lombok Strait; however, their generation and three-dimensional characteristics remain poorly understood. To identify the generation and evolution processes of local NLIWs, we implement high-resolution (Δx=100 m) three-dimensional numerical simulations with realistic topography and tidal forcing, and a set of two-dimensional idealized numerical simulations. Barotropic semidiurnal tides (D2 in short) are verified to predominantly determine the local NLIW generation, but barotropic diurnal tides (D1 in short) can significantly modulate it. Particularly, D1 would enhance (weaken) the amplitudes of NLIWs during the propagation processes when they are in phase (out of phase) with D2. In addition, the three-dimensional model results also suggest that internal wave energy fluxes attenuate dramatically when NLIWs are radiating away from the generation site, due to the radial spreading effects of wave energy along the crest line. However, as NLIWs propagate over the complex bottom topography, large energy fluxes mainly occur in the east-central area of the crest line, resulting in a west-east asymmetry of internal wave energy field. The three-dimensional model results indicate that the northward NLIWs are more numerous and better identified than the southward ones, which are in accordance with the satellite image (MODIS). By a series of two-dimensional idealized numerical simulations, we conclude that the northward NLIWs is 43% larger than the southward ones, due to the joint effect of north–south asymmetric slope steepness on two flanks of the sill in the Lombok Strait and a southward deeper Indian Ocean, between them, the role of asymmetric water depth is more significant (38%) than the asymmetric slope steepness (13%).

Investigation of Turbulent Tidal Flow in a Coral Reef Channel Using Multi-Look WorldView-2 Satellite Imagery

The general topic here is the application of high-resolution satellite imagery to the study of ocean phenomena having horizontal length scales of several meters to a few kilometers. The present study investigates whether multiple images acquired quite closely in time can be used to derive a spatial map of the surface current in situations where the near-surface hydrodynamics are dominated by bed-generated turbulence and associated wave–current interaction. The approach is illustrated using imagery of turbulent tidal flow in a channel through the outer part of the Great Barrier Reef. The main result is that currents derived from the imagery are found to reach speeds of nearly 4 m/s during a flooding tide—three times larger than published values for other parts of the Reef. These new findings may have some impact on our understanding of the transport of tracers and particles over the shelf.

High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension

Sea SurfaceTemperature (SST) is a critical parameter for monitoring the marine environment and understanding various ocean phenomena. While SST can be regularly retrieved from satellite data, it often suffers from missing data due to various reasons including cloud contamination. In this study, we proposed a novel two-step data fusion framework for generating high-resolution seamless daily SST from multi-satellite data sources. The proposed approach consists of (1) SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using the SSTs derived from two satellite sensors (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer 2(AMSR2)), and (2) SST improvement through data fusion using random forest for consistency with in situ measurements with two schemes (i.e., scheme 1 using the reconstructed MODIS SST variables and scheme 2 using both MODIS and AMSR2 SST variables). The proposed approach was evaluated over the Kuroshio Extension in the Northwest Pacific, where a highly dynamic SST pattern can be found, from 2015 to 2019. The results showed that the reconstructed MODIS and AMSR2 SSTs through DINCAE yielded very good performance with Root Mean Square Errors (RMSEs) of 0.85 and 0.60 °C and Mean Absolute Errors (MAEs) of 0.59 and 0.45 °C, respectively. The results from the second step showed that scheme 2 and scheme 1 produced RMSEs of 0.75 and 0.98 °C and MAEs of 0.53 and 0.68 °C, respectively, compared to the in situ measurements, which proved the superiority of scheme 2 using multi-satellite data sources. Scheme 2 also showed comparable or even better performance than two operational SST products with similar spatial resolution. In particular, scheme 2 was good at simulating features with fine resolution (~50 km). The proposed approach yielded promising results over the study area, producing seamless daily SST products with high quality and high feature resolution.

Long Term Indian Ocean Dipole (IOD) Index Prediction Used Deep Learning by convLSTM

Indian Ocean Dipole (IOD) is a large-scale physical ocean phenomenon in the Indian Ocean that plays an important role in predicting the El Nino Southern Oscillation in the tropical Pacific. Predicting the occurrence of IOD is of great significance to the study of climate change and other marine phenomena. Generally, the IOD index is calculated to judge whether the IOD occurs. In this paper, a convolutional LSTM (convLSTM) neural network is used to build the deep learning model to predict the sea surface temperature in the next seven months and calculate the IOD index. Through the analysis of marine atmospheric data with complex temporal and spatial relationships, the wind field signal knowledge of the physical ocean is introduced to predict IOD phenomenon by combining the prior knowledge of the physical ocean and deep learning. The experimental results show that the average correlation of IOD index time series to the true IOD index time series is 82.87% from 2015 to 2018, seven months ahead for IOD prediction. IOD manifests as sea surface temperature (SST) anomaly changes, and this thesis verifies that the wind field signal information has a positive impact on the prediction of IOD changes. Moreover, the convLSTM can predict this anomaly better. The IOD index line graph can generally fit the real IOD index variation trend, which has a profound impact on the study of the IOD phenomenon.

The Sandwich Structure of the Southern Yellow Sea Cold Water Mass and Yellow Sea Warm Current

The southern Yellow Sea Cold Water Mass (YSCWM) and the Yellow Sea Warm Current (YSWC) are prominent features in the southern Yellow Sea. The traditional viewpoint is that the southern YSCWM exists in the warm half of the year and the YSWC exists in the cold half of the year; the most existing studies have regarded these features as independent ocean phenomenon. Through marine investigations conducted in the recent years in the southern Yellow Sea, the southern YSCWM was found to exist in the early winter in 2010, and the YSWC was observed in the early summer in 2013, 2014. For approximately half the year, the southern YSCWM and YSWC existed simultaneously, and the YSWC was located in a slightly high-temperature region in the middle of the southern YSCWM. A sandwich-shaped temperature structure formed in the middle of the southern Yellow Sea from spring to autumn. This article studies the seasonal variation characteristics of the three-dimensional southern YSCWM and YSWC structures using ocean survey data collected in the recent years. The new perspective of the seasonal variations in the southern YSCWM and YSWC obtained herein will promote the recognition of southern Yellow Sea water exchanges and support biogeochemical cycle research in the Yellow Sea.

Site selection for the validation of wide-swath interferometric altimeter in the South China Sea

ABSTRACT Altimetry is widely used in the research of oceanic phenomena. Particularly, wide-swath interferometric altimetry (WIA) can be used to measure sea surface height (SSH) with sufficiently high resolution. Although it has provided that the steric height can be used to validate the SSH by the theoretical and the observation data, validating sea-level variability in the two dimensions of WIA remains a difficult task. Site selection and influencing factor analysis are important in the validation process. In this paper, we analyze the validation process at three sites (Panyu site, DJxxxx site, and Wanshan site) in the South China Sea and discuss the effects of different sites on the validation of SSH variability. On this basis, we analyze the influencing factors, which include significant wave heights, wind, and currents, at three sites. Based on the results, we discuss how site selection for the validation process of WIA can be improved. At DJxxxx site, greater water depth and more equipment layout schemes have emerged with better performance in wavenumber. At the same time, the performance under the tidal, wave, wind and current is different for the other two sites. However, further analysis is necessary for the site selection.

Recent Developments in Artificial Intelligence in Oceanography

With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surges is discussed. This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics. Moreover, physics-informed deep learning and neural networks are discussed within an oceanographic context, and further applications with ocean digital twins and physics-constrained AI algorithms are described. This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.