Cloud-free moderate-resolution imaging spectroradiometer (MODIS) images of the Zhujiang (Pearl) River Estuary (ZRE) taken between 2002 and 2012 are retrieved and used to study the spatial and temporal patterns of suspended sediment concentrations (SSCs) across the estuary under runoff, wind, and tropical storm conditions. Five typical dispersal patterns of suspended sediments in the estuary are defined:Case I shows generally low SSCs under low dynamics; Case Ⅱ shows a river-dominant dispersal pattern of suspended sediments from the outlets, particularly from Modaomen, Jiaomen, Hengmen, and others; Case Ⅲ shows wind-dominant dispersal of high SSCs derived from the west shoal and southwesterly transport under a strong NE wind; Case IV is the combination of relatively large runoff and wind; and Case V is caused by a strong tropical storm with high river discharge and wind, which is characterized by the high SSCs across the entire estuary that are transported eastward by wind-driven and buoyancy currents outside the estuary. Runoff is a dominant factor that controls seasonal and annual SSC variations in the ZRE, with the area of high SSCs being largest in the summer and smallest in the spring. The correlation coefficients between the monthly averaged river-suspended sediment discharge and the area of the high SSCs are approximately 0.6. The wind power over the west shoal increases with a wind speed, which induces more sediment resuspension and shows a close relationship between the wind speed and high SSC area.

The first Chinese microwave ocean environment satellite HY-2A, carrying a Ku-band scatteromenter (SCAT), was successfully launched in August 2011. The first quality assessment of HY-2A SCAT wind products is presented through the comparison of the first 6 months operationally released SCAT products with in situ data. The in situ winds fromtheNationalData Buoy Center (NDBC) buoys, R/V Polarstern, Aurora Australis, Roger Revelle and PY30-1 oil platform, were converted to the 10 m equivalent neutral winds. The temporal and spatial differences between the HY-2A SCAT and the in situ observations were limited to less than 5 min and 12.5 km. For HY-2A SCAT wind speed products, the comparison and analysis using the NDBC buoys yield a bias of -0.49 m/s, a root mean square error (RMSE) of 1.3 m/s and an increase negative bias with increasing wind speed observation above 3m/s. Although less accurate of HY-2A SCAT wind direction at low winds, the RMSE of 19.19° with a bias of 0.92° is found for wind speeds higher than 3 m/s. These results are found consistent with those fromR/Vs and oil platformcomparisons. Moreover, the NDBC buoy comparison results also suggest that the accuracy of HY-2A SCAT winds is consistent over the first half year of 2012. The encouraging assessment results over the first 6 months show that wind products from HY-2A SCAT will be useful for scientific community.

Sea-water-level (SWL) prediction significantly impacts human lives and maritime activities in coastal regions, particularly at offshore locations with shallow water levels. Long-term SWL forecasts, which are conventionally obtained via harmonic analysis, become ineffective when nonperiodic meteorological events predominate. Artificial intelligence combined with other data-processing methods can effectively forecast highly nonlinear and nonstationary inflow patterns by recognizing historical relationships between input and output. These techniques are considerably useful in time-series data predictions. This paper reports the development of a hybrid model to realize accurate multihour SWL forecasting by combining an adaptive neuro-fuzzy inference system (ANFIS) with wavelet decomposition while using sea-level anomaly (SLA) and wind-shear-velocity components as inputs. Numerous wavelet-ANFIS (WANFIS) models have been tested using different inputs to assess their applicability as alternatives to the artificial neural network (ANN), wavelet ANN (WANN), and ANFIS models. Different error definitions have been used to evaluate results, which indicate that integrated wavelet-decomposition and ANFIS models improve the accuracy of SWL prediction and that the inputs of SLA and wind-shear velocity exhibit superior prediction capability compared to conventional SWL-only models.

Spilled oil floats and travels across the water’s surface under the influence of wind, currents, and wave action. Wave-induced Stokes drift is an important physical process that can affect surface water particles but that is currently absent from oil spill analyses. In this study, two methods are applied to determine the velocity of Stokes drift, the first calculates velocity from the wind-related formula based upon a one-dimensional frequency spectrum, while the second determines velocity directly from the wave model that was based on a two-dimensional spectrum. The experimental results of numerous models indicated that: (1) oil simulations that include the influence of Stokes drift are more accurate than that those do not; (2) for medium and long-term simulations longer than two days or more, Stokes drift is a significant factor that should not be ignored, and its magnitude can reach about 2% of the wind speed; (3) the velocity of Stokes drift is related to the wind but is not linear. Therefore, Stokes drift cannot simply be replaced or substituted by simply increasing the wind drift factor, which can cause errors in oil spill projections; (4) the Stokes drift velocity obtained from the two-dimensional wave spectrum makes the oil spill simulation more accurate.

By using iterative method to solve the vector radiative transfer equation of discrete scatterers with randomly rough under-boundary, the back-scattering coefficient is derived, and is applied to the two-scale model of sea surface with foam scatterers driven by strong wind.By employing the modified probability density function of Cox and Munk's, and Pierson's sea spectrum, numerical results of polarized back-scattering are calculated.The functional dependence on wind speed and direction, observation angle, polarization and other parameters are discussed, and theoretical results are favorably matched with experimental data.

Based on the analysis of sea level, air temperature, sea surface temperature (SST), air pressure and wind data during 1980-2013, the causes of seasonal sea level anomalies in the coastal region of the East China Sea (ECS) are investigated. The research results show:(1) sea level along the coastal region of the ECS takes on strong seasonal variation. The annual range is 30-45 cm, larger in the north than in the south. From north to south, the phase of sea level changes from 140° to 231°, with a difference of nearly 3 months. (2) Monthly mean sea level (MSL) anomalies often occur from August to next February along the coast region of the ECS. The number of sea level anomalies is at most from January to February and from August to October, showing a growing trend in recent years. (3) Anomalous wind field is an important factor to affect the sea level variation in the coastal region of the ECS. Monthly MSL anomaly is closely related to wind field anomaly and air pressure field anomaly. Wind-driven current is essentially consistent with sea surface height. In August 2012, the sea surface heights at the coastal stations driven by wind field have contributed 50%-80% of MSL anomalies. (4) The annual variations for sea level, SST and air temperature along the coastal region of the ECS are mainly caused by solar radiation with a period of 12 months. But the correlation coefficients of sea level anomalies with SST anomalies and air temperature anomalies are all less than 0.1. (5) Seasonal sea level variations contain the long-term trends and all kinds of periodic changes. Sea level oscillations vary in different seasons in the coastal region of the ECS. In winter and spring, the oscillation of 4-7 a related to El Ni.o is stronger and its amplitude exceeds 2 cm. In summer and autumn, the oscillations of 2-3 a and quasi 9 a are most significant, and their amplitudes also exceed 2 cm. The height of sea level is lifted up when the different oscillations superposed. On the other hand, the height of sea level is fallen down.

Based on sea level, air temperature, sea surface temperature (SST), air pressure and wind data during 1980-2014, this paper uses Morlet wavelet transform, Estuarine Coastal Ocean Model (ECOM) and so on to investigate the characteristics and possible causes of seasonal sea level anomalies along the South China Sea (SCS) coast. The research results show that:(1) Seasonal sea level anomalies often occur from January to February and from June to October. The frequency of sea level anomalies is the most in August, showing a growing trend in recent years. In addition, the occurring frequency of negative sea level anomaly accounts for 50% of the total abnormal number. (2) The seasonal sea level anomalies are closely related to ENSO events. The negative anomalies always occurred during the El Niño events, while the positive anomalies occurred during the La Ni.a (late El Niño) events. In addition, the seasonal sea level oscillation periods of 4-7 a associated with ENSO are the strongest in winter, with the amplitude over 2 cm. (3) Abnormal wind is an important factor to affect the seasonal sea level anomalies in the coastal region of the SCS. Wind-driven sea level height (SSH) is basically consistent with the seasonal sea level anomalies. Moreover, the influence of the tropical cyclone in the coastal region of the SCS is concentrated in summer and autumn, contributing to the seasonal sea level anomalies. (4) Seasonal variations of sea level, SST and air temperature are basically consistent along the coast of the SCS, but the seasonal sea level anomalies have no much correlation with the SST and air temperature.

Based on the analysis of wind, ocean currents, sea surface temperature (SST) and remote sensing satellite altimeter data, the characteristics and possible causes of sea level anomalies in the Xisha sea area are investigated. The main results are shown as follows:(1) Since 1993, the sea level in the Xisha sea area was obviously higher than normal in 1998, 2001, 2008, 2010 and 2013. Especially, the sea level in 1998 and 2010 was abnormally high, and the sea level in 2010 was 13.2 cm higher than the muti-year mean, which was the highest in the history. In 2010, the sea level in the Xisha sea area had risen 43 cm from June to August, with the strength twice the annual variation range. (2) The sea level in the Xisha sea area was not only affected by the tidal force of the celestial bodies, but also closely related to the quasi 2 a periodic oscillation of tropical western Pacific monsoon and ENSO events. (3) There was a significant negative correlation between sea level in the Xisha sea area and ENSO events. The high sea level anomaly all happened during the developing phase of La Ni.a. They also show significant negative correlations with Niño 4 and Niño 3.4 indices, and the lag correlation coefficients for 2 months and 3 months are -0.46 and -0.45, respectively. (4) During the early La Ni.a event form June to November in 2010, the anomalous wind field was cyclonic. A strong clockwise vortex was formed for the current in 25 m layer in the Xisha sea area, and the velocity of the current is close to the speed of the Kuroshio near the Luzon Strait. In normal years, there is a "cool eddy". While in 2010, from July to August, the SST in the area was 2-3℃ higher than that of the same period in the history.

To understand the response of marine ecosystem to environmental factors, the oceanographic (physical and biochemical) data are analyzed to examine the spatio-temporal distributions of chlorophyll a (Chl a) associated with surface temperature, winds and height anomaly for long periods (1997-2008) in the western South China Sea (SCS). The results indicate that seasonal and spatial distributions of Chl a are primarily influenced by monsoon winds and hydrography. A preliminary Empirical Orthogonal Function (EOF) analysis of remotely sensed data is used to assess basic characteristics of the response process of Chl a to physical changes, which reveals interannual variability of anomalous low Chl a values corresponding to strong El Niño (1997-1998), high values corresponding to strong La Niña (1999-2000), low Chl a corresponding to moderate El Niño (2001-2003), upward Chl a after warm event in 2005 off the east coast of Vietnam. The variability of Chl a in nearshore and the Mekong River Estuary (MER) waters also suggests its response to these warm or cold processes. Considering the evidence for covariabilities between Chl a and sea surface temperature, winds, height anomaly (upwelling or downwelling), cold waters input and strong winds mixing may play important roles in the spatial and temporal variability of high Chl a. Such research activities could be very important to gain a mechanistic understanding of ecosystem responses to the climate change in the SCS.

The daily and monthly-mean characteristics of cold water patches (CWPs) off the Jiangsu coast in 35 a of 1982-2016 are examined based on advanced very high resolution radiometer (AVHRR) data. Most of the CWPs are found to occur in the warm and hot months (May-September), with some CWPs in the cool and cold months (October-April). The average radius and intensity of the monthly-mean CWPs are about 81 km and 0.6℃, respectively. The average difference in the sea surface temperature (SST) between the centers of the CWPs and the nearshore is about 2.0℃. The correlation analysis between the CWPs, winds and tides indicates that most of the CWPs occurred during the southerly winds, with some CWPs occurring during the northerly winds. The average intensity of the CWPs during spring tides is slightly stronger than that during neap tides in the warm and hot months, and the difference is very small in the cool and cold months.