Qing Xu

SAR observation and model tracking of an oil spill event in coastal waters.

Oil spills are a major contributor to marine pollution. The objective of this work is to simulate the oil spill trajectory of oil released from a pipeline leaking in the Gulf of Mexico with the GNOME (General NOAA Operational Modeling Environment) model. The model was developed by NOAA (National Oceanic and Atmospheric Administration) to investigate the effects of different pollutants and environmental conditions on trajectory results. Also, a Texture-Classifying Neural Network Algorithm (TCNNA) was used to delineate ocean oil slicks from synthetic aperture radar (SAR) observations. During the simulation, ocean currents from NCOM (Navy Coastal Ocean Model) outputs and surface wind data measured by an NDBC (National Data Buoy Center) buoy are used to drive the GNOME model. The results show good agreement between the simulated trajectory of the oil spill and synchronous observations from the European ENVISAT ASAR (Advanced Synthetic Aperture Radar) and the Japanese ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array L-band Synthetic Aperture Radar) images. Based on experience with past marine oil spills, about 63.0% of the oil will float and 18.5% of the oil will evaporate and disperse. In addition, the effects from uncertainty of ocean currents and the diffusion coefficient on the trajectory results are also studied.

Satellite observations and modeling of oil spill trajectories in the Bohai Sea

On June 4 and 17, 2011, separate oil spill accidents occurred at two oil platforms in the Bohai Sea, China. The oil spills were subsequently observed on different types of satellite images including SAR (Synthetic Aperture Radar), Chinese HJ-1-B CCD and NASA MODIS. To illustrate the fate of the oil spills, we performed two numerical simulations to simulate the trajectories of the oil spills with the GNOME (General NOAA Operational Modeling Environment) model. For the first time, we drive the GNOME with currents obtained from an operational ocean model (NCOM, Navy Coastal Ocean Model) and surface winds from operational scatterometer measurements (ASCAT, the Advanced Scatterometer). Both data sets are freely and openly available. The initial oil spill location inputs to the model are based on the detected oil spill locations from the SAR images acquired on June 11 and 14. Three oil slicks are tracked simultaneously and our results show good agreement between model simulations and subsequent satellite observations in the semi-enclosed shallow sea. Moreover, GNOME simulation shows that the number of splots, which denotes the extent of spilled oil, is a vital factor for GNOME running stability when the number is less than 500. Therefore, oil spill area information obtained from satellite sensors, especially SAR, is an important factor for setting up the initial model conditions.

Sea Surface Manifestation of Along-Tidal-Channel Underwater Ridges Imaged by SAR

A group of submerged ocean bottom sand ridges in the Bohai Sea, China, are shown in RADARSAT-1 and ENVISAT synthetic aperture radar (SAR) images. The sand ridges appear as fingerlike quasi-linear features in the SAR images. Examining the detailed local bathymetry chart, we find that these features coincide with the satellite images. The heights of the sand ridges are less than 10 m, and the water depth is between 10 and 30 m. The spacing of the sand ridges is about 10 km, and the length of the sand ridges is about 20 km. The same sand ridges are also visible on a Moderate Resolution Imaging Spectroradiometer (MODIS) true-color image. The semidiurnal and diurnal tidal currents in this area are almost parallel to the major axis of these sand ridges. These observations cannot be explained using the existing 1-D SAR imaging model, which is not applicable to sand ridges parallel to the tidal current. In this paper, we consider the shallow-water current bathymetry in a 2-D space. An analytical ocean model was applied to demonstrate the temporal variations of the current divergence and convergence that are induced by the along-sand-ridge-direction current and ridge interaction. A radar simulation model is used to simulate the variation of normalized radar cross section (NRCS) induced by the ocean surface current. The simulated NRCS variation is similar to that extracted from the calibrated SAR image. Simulation results also show that the NRCS variation becomes negligible when the ocean current is set to about half of the maximum tidal current.

Assessment of an analytical model for sea surface wind speed retrieval from spaceborne SAR

An analytical model based on radar backscatter theory was utilized to retrieve sea surface wind speeds from C-band satellite synthetic aperture radar (SAR) data at either vertical (VV) or horizontal (HH) polarization in transmission and reception. The wind speeds were estimated from several ENVISAT Advanced SAR (ASAR) images in Hong Kong coastal waters and from Radarsat-1 SAR images along the west coast of North America. To evaluate the accuracy of the analytical model, the estimated wind speeds were compared to coincident buoy measurements, as well as winds retrieved by C-band empirical algorithms (CMOD4, CMOD_IRF2 and CMOD5). The comparison shows that the accuracy of the analytical model is comparable to that of the C-band empirical algorithms. The results indicate the capability of the analytical model for sea surface wind speed retrieval from SAR images at both VV and HH polarization.

Monitoring of Oil Spill Trajectories With COSMO-SkyMed X-Band SAR Images and Model Simulation

The Shell North Sea Gannet Alpha platform oil spill accident occurred on August 10, 2011. This was the largest oil spill accident in United Kingdom waters in the last decade. The spills were observed on four COSMO-SkyMed (CSK) X-band synthetic aperture radar (SAR) images acquired between August 17 and 22, 2011, with revisit time from 11 h to 3 days between the SAR acquisitions. The areas of oil slicks were extracted from SAR images using an existing image classification and segmentation algorithm. It was found that the oil slicks moved toward the southwest with slick size enlarging from 3.69 to 62.01 km2 in the first 24 h between the first and second SAR acquisitions. We tracked the oil spill trajectories using the General NOAA Operational Modeling Environment (GNOME) oil-drifting model. The 6-hourly surface wind fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA) Interim products and the 3-hourly ocean surface current fields from the Navy Coastal Ocean Model (NCOM) global operational model were used to drive the GNOME model. The simulated oil slick movement was in good agreement with that observed by the CSK SAR images. Moreover, the simulation showed that the movement of oil spills was dominated by the surface winds in the North Sea.

Mechanisms of SAR Imaging of Shallow Water Topography of the Subei Bank

In this study, the C-band radar backscatter features of the shallow water topography of Subei Bank in the Southern Yellow Sea are statistically investigated using 25 ENVISAT (Environmental Satellite) ASAR (advanced synthetic aperture radar) and ERS-2 (European Remote-Sensing Satellite-2) SAR images acquired between 2006 and 2010. Different bathymetric features are found on SAR imagery under different sea states. Under low to moderate wind speeds (3.1~6.3 m/s), the wide bright patterns with an average width of 6 km are shown and correspond to sea surface imprints of tidal channels formed by two adjacent sand ridges, while the sand ridges appear as narrower (only 1 km wide), fingerlike, quasi-linear features on SAR imagery in high winds (5.4~13.9 m/s). Two possible SAR imaging mechanisms of coastal bathymetry are proposed in the case where the flow is parallel to the major axes of tidal channels or sand ridges. When the surface Ekman current is opposite to the mean tidal flow, two vortexes will converge at the central line of the tidal channel in the upper layer and form a convergent zone over the sea surface. Thus, the tidal channels are shown as wide and bright stripes on SAR imagery. For the SAR imaging of sand ridges, all the SAR images were acquired at low tidal levels. In this case, the ocean surface waves are possibly broken up under strong winds when propagating from deep water to the shallower water, which leads to an increase of surface roughness over the sand ridges.

Impacts of oil spills on altimeter waveforms and radar backscatter cross‐section

Ocean surface films can damp short capillary-gravity waves, reduce the surface mean square slope, and induce “sigma0 blooms” in satellite altimeter data. No study has ascertained the effect of such film on altimeter measurements due to lack of film data. The availability of Environmental Response Management Application (ERMA) oil cover, daily oil spill extent and thickness data acquired during the Deepwater Horizon (DWH) oil spill accident provides a unique opportunity to evaluate the impact of surface film on altimeter data. In this study, the Jason-1/2 passes nearest to the DWH platform are analyzed to understand the waveform distortion caused by the spill as well as the variation of σ0 as a function of oil thickness, wind speed and radar band. Jason-1/2 Ku-band σ0 increased by 10 dB at low wind speed ( 3 m.s-1) in the oil-covered area. The mean σ0 in Ku and C bands increased by 1.0 - 3.5 dB for thick oil and 0.9 - 2.9 dB for thin oil while the waveforms are strongly distorted. As the wind increases up to 6 m.s-1, the mean σ0 bloom and waveform distortion in both Ku and C bands weakened for both thick and thin oil. When wind exceeds 6 m.s-1, only does the σ0 in Ku band slightly increase by 0.2 - 0.5 dB for thick oil. The study shows that high-resolution altimeter data can certainly help better evaluate the thickness of oil spill, particularly at low wind speeds.

Bathymetric features of Subei Bank on ENVISAT ASAR images

In this study, we investigated the bathymetric features of Subei Bank in the Southern Yellow Sea on ENVISAT (Environmental Satellite) ASAR (advanced synthetic aperture radar) images. There are some fingerlike features in numerous ASAR images from 2005 to 2010. Examining the detailed local bathymetry chart, we found that these features are indeed the sea surface imprints of tidal channels and sand ridges. Analysis of quasi-synchronous sea surface wind and tide data demonstrates that the wind and ocean current play a significant role in the capability of SAR imaging of shallow water topography in the study area.

Observation and simulation of 2010 ULVA prolifera bloom in the Yellow Sea

In this paper, the Ulva prolifera bloom event in the Yellow Sea in summer 2010 is investigated by MODIS (Moderate Resolution Imaging Spectroradiometer) images. We use the FAI (Floating Algae Index) method to detect the distribution of the floating macroalgae from the images. Then we apply the GNOME (General NOAA Operational Modeling Environment) model to simulate the trajectories of the Ulva prolifera in the Yellow Sea. The model results agree well with satellite observations, indicating that the occurrence and movement of the floating macroalgae can be investigated with the combination of GNOME model and satellite data.

Spatio-Temporal Variability of Annual Sea Level Cycle in the Baltic Sea

In coastal and semi-enclosed seas, the mean local sea level can significantly influence the magnitude of flooding in inundation areas. Using the cyclostationary empirical orthogonal function (CSEOF) method, we examine the spatial patterns and temporal variations of annual sea level cycle in the Baltic Sea based on satellite altimetry data, tide gauge data, and regional model reanalysis during 1993 and 2014. All datasets demonstrate coherent spatial and temporal annual sea level variability, although the model reanalysis shows a smaller interannual variation of annual sea level amplitude than other datasets. A large annual sea level cycle is observed in the Baltic Sea, except in the Danish straits from December to February. Compared with altimetry data, tide gauge data exhibit a stronger annual sea level cycle in the Baltic Sea (e.g., along the coasts and in the Gulf of Finland and the Gulf of Bothnia), particularly in the winter. Moreover, the maps of the maximum and minimum annual sea level amplitude imply that all datasets underestimate the maximum annual sea level amplitude. Analysis of the atmospheric forcing factors (e.g., sea level pressure, North Atlantic Oscillation (NAO), winds and air temperature), which may contribute to the interannual variation of the annual sea level cycle shows that both the zonal wind and winter NAO (e.g., from December to March) are highly correlated with the annual cycle variations in the tide gauge data in 1900–2012. In the altimetry era (1993–2014), all the atmospheric forcing factors are linked to the annual sea level cycle variations, particularly in 1996, 2010 and 2012, when a significant increase and drop of annual sea level amplitude are observed from all datasets, respectively.