A Leeway-Trace model was established for the traceability analysis of drifting objects at sea. The model was based on the Leeway model which is a Monte Carlo-based ensemble trajectory model, and a method of realistic traceability analysis was proposed in this study by using virtual spatiotemporal drift trajectory prediction. Here, measured data from a drifting buoy observation experiment in the northern South China Sea in April 2019, combined with surface current data obtained from the finite volume community ocean model (FVCOM), were used for the traceability analysis of humanoid buoys. The results were basically consistent with the observations, and the assimilation of measured current data can significantly improve the accuracy of the traceability analysis. Several sensitive experiments were designed to discuss the effects of wind and tide on the traceability analysis, and their results showed that the wind-driven current and the wind-induced leeway drift are both important to the traceability analysis. The effect of tidal currents on traceability could not be ignored even though they were much weaker than the residual currents in the experimental area of the northern South China Sea.

We aim to directly invert wave parameters by using the data of a compact polarimetric synthetic aperture radar (CP SAR) and validate the effectiveness of ocean wave parameter retrieval from the circular transmit/linear receive mode and π/4 compact polarimetric mode. Relevant data from the RADARSAT-2 fully polarimetric SAR on the C-band were used to obtain the compact polarimetric SAR images, and a polarimetric SAR wave retrieval algorithm was used to verify the sea surface wave measurements. Using the data and algorithm, there is no need to estimate complex hydrodynamic modulation transfer functions, even at large radar incidence angles. First, the radar backscattering cross-sections and backscattering cross-section of the radar linearly polarized with any polarization orientation angle were calculated in the two compact polarimetric SAR modes. Then, the wave slopes along the azimuth direction and the range direction were calculated directly using CP SAR data. Finally, we obtained the slope spectrum of the wave from the estimated wave slopes along azimuth and range directions. The wave parameters extracted from the synthetic wave slope spectrum were compared with those obtained from buoy observations of the National Data Buoy Center, verifying a suitable agreement.

The unbalanced submesoscale motions and their seasonality in the northern Bay of Bengal (BoB) are investigated using outputs of the high resolution regional oceanic modeling system. Submesoscale motions in the forms of filaments and eddies are present in the upper mixed layer during the whole annual cycle. Submesoscale motions show an obvious seasonality, in which they are active during the winter and spring but weak during the summer and fall. Their seasonality is associated with the mixed layer instability that depends on the mixed layer depth (MLD). During the winter, the MLD provides a much greater reservoir of the available potential energy, which promotes mixed layer instability to develop active submesoscale motions. The variations of MLD are likely modulated by the larger scale motions and the influxes of freshwater. Further investigations imply that the MLD and the stratified barrier layer are combined to determine the vertical structure of the submesoscale motions. The shallow MLD and strong stratification below during the summer and fall seem to prevent the downward extension of submesoscale motions. But in spring when the weak stratification exists, the penetration depth exceeds the base of the barrier layer.

The horizontally variable density stratification and background currents are taken into the variable-coefficent extended Korteweg-de Vries (evKdV) theory to obtain the geographical and seasonal distribution of kinematic parameters of internal solitary waves in the Andaman Sea (AS). The kinematic parameters include phase speed, dispersion parameter, quadratic and cubic nonlinear parameters. It shows that the phase speed and dispersion parameter are mainly determined by the topographic feature and have limited seasonal variation. The maximum phase speed is 2.6 m/s, which occurs in the cool season (November) in the middle of the AS, while the phase speed in the cool season is slightly larger than those in other seasons, up to 11.4% larger than that in the rainy season (July) in the southern AS. The dispersion parameter in the cool season can be 22.3% larger than that in the hot season. The nonlinear parameters have significant seasonal variation, and they can even change their signs at the continental slope in the north of the AS, from season to season. Meanwhile, the algebraic solitons dominate in the AS with minimum amplitudes (a_al) ranging from 0.1 m to 102 m, and the maximum a_al occurs in the cool season in the southern AS. The effect of the background flow on the parameters is also studied. The background flow has a great influence on the nonlinear parameters, e.g., the value of cubic nonlinear parameter can be reduced by 1/3 when the background flow is not considered.

Seasonal location and intensity changes in the western Pacific subtropical high (WPSH) are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East Asia. Therefore, this article delves into the forecast of the western Pacific subtropical high index during typhoon activity by adopting a hybrid deep learning model. Firstly, the predictors, which are the inputs of the model, are analysed based on three characteristics: the first is the statistical discipline of the WPSH index anomalies corresponding to the three types of typhoon paths; the second is the correspondence of distributions between sea surface temperature, 850 hPa zonal wind (u), meridional wind (v), and 500 hPa potential height field; and the third is the numerical sensitivity experiment, which reflects the evident impact of variations in the physical field around the typhoon to the WPSH index. Secondly, the model is repeatedly trained through the backward propagation algorithm to predict the WPSH index using 2011–2018 atmospheric variables as the input of the training set. The model predicts the WPSH index after 6 h, 24 h, 48 h, and 72 h. The validation set using independent data in 2019 is utilized to illustrate the performance. Finally, the model is improved by changing the CNN2D module to the DeCNN module to enhance its ability to predict images. Taking the 2019 typhoon “Lekima” as an example, it shows the promising performance of this model to predict the 500 hPa potential height field.

Antarctic Bottom Water (AABW) plays an important role in the meridional overturning circulation and contributes significantly to global heat transport and sea level rise (SLR). Based on the Global Ocean (1/12)° Physical Reanalysis (GLORYS12V1) products and conductivity-temperature-depth instrument data from the World Ocean Circulation Experiment hydrographic program, we analyzed the trends in the thickness, volume, temperature, salinity, and neutral density of the AABW in the Amundsen Sea from 1993 to 2017. Over the past 25 years, the volume has decreased by 3.45×10¹² m³/a, thinning at a rate of 5 m/a. In the vertical direction, the contraction of the AABW is compensated by the volume expansion of the Circumpolar Deep Water. As the volume of AABW decreases, the temperature of the AABW increases by about 0.002°C/a. This warming is equivalent to a heat flux of 0.27 W/m². A local SLR is produced due to thermal expansion of 0.35 mm/a. During the study period, the neutral density decreased by 0.000 3 kg/(m³∙a) due to warming. In the horizontal direction, the volume of AABW flowing from the Ross Sea into the Amundsen Sea gradually decreases and the temperature of the AABW increases continuously. The horizontal transport loss of the AABW volume is 4.07×10¹⁴ m³ and the horizontal heat transport results in a 0.03°C increase in the temperature of the AABW.

With the accelerated warming of the world, the safety and use of Arctic passages is receiving more attention. Predicting visibility in the Arctic has been a hot topic in recent years because of navigation risks and opening of ice-free northern passages. Numerical weather prediction and statistical prediction are two methods for predicting visibility. As microphysical parameterization schemes for visibility are so sophisticated, visibility prediction using numerical weather prediction models includes large uncertainties. With the development of artificial intelligence, statistical prediction methods have received increasing attention. In this study, we constructed a statistical model with a physical basis, to predict visibility in the Arctic based on a dynamic Bayesian network, and tested visibility prediction over a 1°×1° grid area averaged daily. The results show that the mean relative error of the predicted visibility from the dynamic Bayesian network is approximately 14.6% compared with the inferred visibility from the artificial neural network. However, dynamic Bayesian network can predict visibility for only 3 days. Moreover, with an increase in predicted area and period, the uncertainty of the predicted visibility becomes larger. At the same time, the accuracy of the predicted visibility is positively correlated with the time period of the input evidence data. It is concluded that using a dynamic Bayesian network to predict visibility can be useful over Arctic regions for projected climatic changes.

Piezocone penetration test (CPTu), the preferred in-situ tool for submarine investigation, is significant for soil classification and soil depth profile prediction, which can be used to predict soil types and states. However, the accuracy of these methods needs to be validated for local conditions. To distinguish and evaluate the properties of the shallow surface sediments in Chengdao area of the Yellow River Delta, seabed CPTu tests were carried out at ten stations in this area. Nine soil classification methods based on CPTu data are applied for soil classification. The results of classification are compared with the in-situ sampling to determine whether the method can provide sufficient resolution. The methods presented by Robertson (based on soil behavior type index I_c), Olsen and Mitchell are the more consistent and compatible ones compared with other methods. Considering that silt soils have potential to liquefy under storm tide or other adverse conditions, this paper is able to screen soil classification methods suitable for the Chengdao area and help identify the areas where liquefaction or submarine landslide may occur through CPTu investigation.

Satellite altimetry observations, including the upcoming Surface Water and Ocean Topography mission, provide snapshots of the global sea surface high anomaly field. The common practice in analyzing these surface elevation data is to convert them into surface velocity based on the geostrophic approximation. With increasing horizontal resolution in satellite observations, sea surface elevation data will contain many dynamical signals other than the geostrophic velocity. A new physical quantity, the available surface potential energy, is conceptually introduced in this study defined as the density multiplied by half of the squared deviation from the local mean reference surface elevation. This gravitational potential energy is an intrinsic property of the sea surface height field and it is an important component of ocean circulation energetics, especially near the sea surface. In connection with other energetic terms, this new variable may help us better understand the dynamics of oceanic circulation, in particular the processes in connection with the free surface data collected through satellite altimetry. The preliminary application of this concept to the numerically generated monthly mean Global Ocean Data Assimilation System data and Archiving, Validation, and Interpretation of Satellite Oceanographic altimeter data shows that the available surface potential energy is potentially linked to other dynamic variables, such as the total kinetic energy, eddy kinetic energy and available potential energy.

In this study a novel synthetic aperture radar (SAR) scattering model for sea surface with breaking waves is proposed. Compared with existing models, the proposed model considers an empirical relationship between wind speed and wave breaking scattering to present the contribution of wave breaking. Moreover, the scattering weight factor p, and wave breaking rate q, are performed to present the contribution of the quasi-specular scattering term, Bragg scattering term, and wave breaking scattering term to the total scattering from the sea surface. To explore the modeling accuracy of sea-surface scattering, a simulated normalized radar cross-section (NRCS) and measured NRCS are compared. The proposed model generated the simulated NRCS and a matching GF-3 dataset was used for the measured NRCS. It was revealed that the performance of the VV polarization of our model was much better than that of HH polarization, with a correlation of 0.91, bias of −0.14 dB, root mean square error (RMSE) of 1.26 dB, and scattering index (SI) of −0.11. In addition, the novel model is explored and compared with the geophysical model of CMODs and satellite-measured NRCS from GF-3 SAR wave mode imagery. For an incidence angle 40°–41°, the relationship between the NRCS and wind speed, relative wind direction is proposed. As with the SAR-measured NRCS, the performance of VV polarization was much better than HH polarization, with a correlation of 0.99, bias of −0.25 dB, RMSE of 0.64 dB, and SI of −0.04.