Yongcun Cheng

Accuracy assessment of global barotropic ocean tide models

The accuracy of state-of-the-art global barotropic tide models is assessed using bottom pressure data, coastal tide gauges, satellite altimetry, various geodetic data on Antarctic ice shelves, and independent tracked satellite orbit perturbations. Tide models under review include empirical, purely hydrodynamic (“forward”), and assimilative dynamical, i.e., constrained by observations. Ten dominant tidal constituents in the diurnal, semidiurnal, and quarter-diurnal bands are considered. Since the last major model comparison project in 1997, models have improved markedly, especially in shallow-water regions and also in the deep ocean. The root-sum-square differences between tide observations and the best models for eight major constituents are approximately 0.9, 5.0, and 6.5 cm for pelagic, shelf, and coastal conditions, respectively. Large intermodel discrepancies occur in high latitudes, but testing in those regions is impeded by the paucity of high-quality in situ tide records. Long-wavelength components of models tested by analyzing satellite laser ranging measurements suggest that several models are comparably accurate for use in precise orbit determination, but analyses of GRACE intersatellite ranging data show that all models are still imperfect on basin and subbasin scales, especially near Antarctica. For the M2 constituent, errors in purely hydrodynamic models are now almost comparable to the 1980-era Schwiderski empirical solution, indicating marked advancement in dynamical modeling. Assessing model accuracy using tidal currents remains problematic owing to uncertainties in in situ current meter estimates and the inability to isolate the barotropic mode. Velocity tests against both acoustic tomography and current meters do confirm that assimilative models perform better than purely hydrodynamic models.

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.

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.

Impacts of altimeter corrections on local linear sea level trends around Taiwan

Multi-mission altimeter measurements from TOPEX, Jason-1, and Jason-2 satellite altimetry over the 1993–2009 time span are used to characterize the local linear sea level trend (LSLT) around Taiwan. The results show that the long-term changes of default geophysical and range corrections, i.e. the inverted barometer correction, wet tropospheric correction, and sea state bias correction, have significant impacts on the determination of local LSLT. The trend of default corrections contribute more than 1.4 mm year−1 along the coastline of China mainland and 2.1 mm year−1 to local LSLT in the Taiwan Strait. The default-corrected altimetric data exhibit highest and lowest local LSLTs in the southeast and northwest of Taiwan, respectively. The regional LSLTs of 3.8 mm year−1 and 4.6 mm year−1 are estimated from the default-corrected and uncorrected altimetric data in the study area, respectively.

Sea-level trend in the South China Sea observed from 20 years of along-track satellite altimetric data

The sea-level trend in the South China Sea (SCS) is investigated based on 20 years of along-track data from TOPEX and Jason–1/2 satellite altimetry. The average sea-level rise over all the regions in the study area is observed to have a rate of 5.1 ± 0.8 mm year−1 for the period from 1993 to 2012. The steric sea level contributes 45% to the observed sea-level trend. These results are consistent with previous studies. In addition, the results demonstrate that the maximum sea-level rise rate of 8.4 mm year–1 is occurring off the east coast of Vietnam and eastern part of SCS. During 2010–2011, the La Niña event was highly correlated with the dramatic sea-level rise in the SCS; La Niña events were also associated with the maximum rate of sea rise off the east coast of Vietnam, which occurred during 1993 and 2012. We also evaluated the trends in the geophysical (e.g. dynamical atmospheric correction (DAC)) and range corrections (e.g. wet tropospheric correction, dry tropospheric correction, and ionosphere correction), which can leak into the observed sea-level record and be interpreted as part of the sea-level trend. The mean DAC trend within the SCS is found to be 0.4 ± 0.1 mm year–1 with >0.7 mm year–1 exhibited in the northern portion of the SCS. This is validated by comparing the altimetric data with the DAC-corrected tide gauge data at Xisha. In the southern SCS, the trend in wet troposphere correction, which is based on radiometer measurements on board the satellite, should be considered for local sea-level trend estimation.

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.

Multimission satellite altimetric data validation in the Baltic Sea

The assessment of altimetric data is crucial for investigating the regional sea level variability. Few works has been performed to validate the altimetric data [1, 2] in the Baltic Sea. The exploring of multi-mission altimetric data in the Baltic Sea has yet to be published. The number of available altimetric measurements increases of 96% by replacing the radiometer wet troposphere correction with model based correction. The results indicate the high quality of the along-track altimetry measurements in the semi-closed sea, which shows good agreement with tide gauge data except in the shallow waters and ice-covered regions, such as Danish Straits and the Gulf of Bothnian.

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.