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 (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. © 2017. American Geophysical Union

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

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

Predictive modelling of global solar radiation with artificial intelligence approaches using MODIS satellites and atmospheric reanalysis data for Australia

Global solar radiation (GSR) prediction is a prerequisite task for agricultural management and agronomic decisions, including photovoltaic (PV) power generation, biofuel exploration and several other bio-physical applications. Since short-term variabilities in the GSR incorporate stochastic and intermittent behaviours (such as periodic fluctuations, jumps and trends) due to the dynamicity of atmospheric variables, GSR predictions, as required for solar energy generation, is a challenging endeavour to satisfactorily predict the solar generated electricity in a PV system. Additionally, the solar radiation data, as required for solar energy monitoring purposes, are not available in all geographic locations due to the absence of meteorological stations and this is especially true for remote and regional solar powered sites. To surmount these challenges, the universally (and freely available) atmospheric gridded datasets (e.g., reanalysis and satellite variables) integrated into solar radiation predictive models to generate reliable GSR predictions can be considered as a viable medium for future solar energy exploration, utilisation and management. Hence, this doctoral thesis aims to design and evaluate novel Artificial Intelligence (AI; Machine Learning and Deep Learning) based predictive models for GSR predictions, using the European Centre for Medium Range Weather Forecasting (ECMWF) Interim-ERA reanalysis and Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite variables enriched with ground-based weather station datasets for the prediction of both long-term (i.e., monthly averaged daily) as well as the short-term (i.e., daily and half-hourly) GSR. The focus of the study region is Queensland, the sunshine state, as well as a number of major solar cities in Australia where solar energy utilisation is actively being promoted by the Australian State and Federal Government agencies. Firstly, the Artificial Neural Networks (ANN), a widely used Machine Learning model is implemented to predict daily GSR at five different cities in Australia using ECMWF Reanalysis fields obtained from the European Centre for Medium Range Weather Forecasting repository. Secondly, the Self-Adaptive Differential Evolutionary Extreme Learning Machine (i.e., SaDE-ELM) is also proposed for monthly averaged daily GSR prediction trained with ECMWF reanalysis and MODIS satellite data from the Moderate Resolution Imaging Spectroradiometer. Thirdly, a three-phase Support Vector Regression (SVR; Machine Learning) model is developed to predict monthly averaged daily GSR prediction where the MODIS data are used to train and evaluate the model and the Particle Swarm Algorithm (PSO) is used as an input selection algorithm. The PSO selected inputs are further transformed into wavelet subseries via non-decimated Discrete Wavelet Transform to unveil the embedded features leading to a hybrid PSO-W-SVR model, seen to outperform the comparative hybrid models. Fourthly, to improve the accuracy of conventional techniques adopted for GSR prediction, Deep Learning (DL) approach based on Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms are developed to predict the monthly averaged daily GSR prediction using MODIS-based dataset. Finally, the Convolutional Neural Network (CNN) integrated with a Long Short-Term Memory Network (LSTM) model is used to construct a hybrid CLSTM model which is tested to predict the half-hourly GSR values over multiple time-step horizons (i.e., 1-Day, 1-Week, 2-Week, and 1-Month periods). Here, several statistical, Machine Learning and Deep Learning models are adopted to benchmark the proposed DNN and CLSTM models against conventional models (ANN, SaDE-ELM, SVR, DBN). In this doctoral research thesis, a Global Sensitivity Analysis method that attempts to utilise the Gaussian Emulation Machine (GEM-SA) algorithm is employed for a sensitivity analysis of the model predictors. Sensitivity analysis of selected predictors ascertains that the variables: aerosol, cloud, and water vapour parameters used as input parameters for GSR prediction play a significant role and the most important predictors are seen to vary with the geographic location of the tested study site. A suite of alternative models are also developed to evaluate the input datasets classified into El Niño, La Niña and the positive and negative phases of the Indian Ocean Dipole moment. This considers the impact of synoptic-scale climate phenomenon on long-term GSR predictions. A seasonal analysis of models applied at the tested study sites showed that proposed predictive models are an ideal tool over several other comparative models used for GSR prediction. This study also ascertains that an Artificial Intelligence based predictive model integrated with ECMWF reanalysis and MODIS satellite data incorporating physical interactions of the GSR (and its variability) with the other important atmospheric variables can be considered to be an efficient method to predict GSR. In terms of their practical use, the models developed can be used to assist with solar energy modelling and monitoring in solar-rich sites that have diverse climatic conditions, to further support cleaner energy utilization. The outcomes of this doctoral research program are expected to lead to new applications of Artificial Intelligence based predictive tools for GSR prediction, as these tools are able to capture the non-linear relationships between the predictor and the target variable (GSR). The Artificial Intelligence models can therefore assist climate adaptation and energy policymakers to devise new energy management devices not only for Australia but also globally, to enable optimal management of solar energy resources and promote renewable energy to combat current issues of climate change. Additionally, the proposed predictive models may also be applied to other renewable energy areas such as wind, drought, streamflow, flood and electricity demand for prediction

한반도 주변해 연안 해양현상에 대한 합성개구레이더 활용

학위논문 (박사)-- 서울대학교 대학원 : 과학교육과 (지구과학전공), 2016. 8. 박경애.In this thesis, the applicability of synthetic aperture radar (SAR) to interpretation of oceanic phenomena at the coastal regions around Korea peninsula is presented. For that, the spatial and temporal variations of SAR-derived coastal wind fields and evolution of disastrous oil spills on SAR images were analyzed in relation to atmospheric and oceanic environmental factors using in-situ measurement and satellite observations. The SAR wind fields retrieved from the east coast of Korea in August 2007 during the upwelling period revealed a spatial distinction between near and offshore regions. Low wind speeds were associated with cold water regions with dominant coastal upwelling. Time series of in-situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. SAR data at the present upwelling region showed a relatively large backscattering attenuation to SST ratio of 1.2 dB ºC−1 compared the known dependence of the water viscosity on the radar backscattering. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature. It implies that the low wind field from SAR was mainly induced by changes in atmospheric stability due to air-sea temperature differences. Oil spills at the Hebei Spirit accident off the coast of Korea in the Yellow Sea were identified using SAR data and their evolution was investigated. To quantitatively analyze the spatial and temporal variations of oil spills, objective detection methods based on adaptive thresholding and a neural network were applied. Prior to applying, the results from two methods were compared for verification. It showed good agreement enough for the estimation of the extent of oil patches and their trajectories, with the exception of negligible errors at the boundaries. Quantitative analyses presented that the detected oil slicks moved southeastward, corresponding to the prevailing wind and tidal currents, and gradually dissipated during the spill, except for an extraordinary rapid decrease in onshore regions at the initial stage. It was identified that the initial dissipation of the spilt oil was induced by strong tidal mixing in the tidal front zone from comparison with the tidal mixing index. The spatial and temporal variations of the oil slicks confirmed the influence of atmospheric and oceanic environmental factors. The overall horizontal migration of the oil spills detected from consecutive SAR images was mainly driven by Ekman drift during the winter monsoon rather than the tidal residual current.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Objectives of the Thesis 14 Chapter 2. Data Description 15 2.1. SAR Data 15 2.2. Other Satellite Data 21 2.2.1. Wind Data 21 2.2.2. Sea Surface Temperature Data 21 2.2.3. Ocean Color Data 22 2.3. Reanalysis Data 23 2.4. In-situ Measurements 23 2.5. Land Masking Data 26 2.6. Tidal Current Data 28 Chapter 3. Methods 29 3.1. SAR Wind Retrieval 29 3.2. Noise Reduction of ScanSAR Images 37 3.3. Conversion of Wind Speed to Neutral Wind 41 3.4. Estimation of Index of the Tidal Front 43 3.5. Estimation of Ekman Drift and Tidal Residual Current 45 3.6. Feature Detection Methods 46 3.6.1. Adaptive Threshold Method 47 3.6.2. Bimodal Histogram Method 50 3.6.3. Neural Network Method 54 Chapter 4. Coastal Wind Fields and Upwelling Response 58 4.1. Variations of Wind Fields during Coastal Upwelling 58 4.2. Stability Effect on Wind Speed 65 4.3. Biological Impact of Upwelling 70 Chapter 5. Characteristics of Objective Feature Detection 74 5.1. Comparison of Thresholding Methods 74 5.2. Oil Spill of the Hebei Spirit by Thresholding Method 81 5.3. Oil Spill by the Hebei Spirit by Neural Network Method 85 5.4. Differences by Detection Methods 88 Chapter 6. Evolution of Oil Spill at the Coastal Region 90 6.1. Temporal Evolution of the Hebei Spirit Oil Spill 90 6.2. Effect of Artificial Factor on the Evolution 96 Chapter 7. Effect of Environmental Factors on the Oil Spill 98 7.1. Effect of Tidal Mixing 98 7.2. Effect of Wind and Tidal Current 103 Chapter 8. Summary and Conclusion 110 Reference 114 Abstract in Korean 142Docto