CHARACTERISTICS OF REFRACTIVITY AND SEA STATE IN THE MARINE ATMOSPHERIC SURFACE LAYER AND THEIR INFLUENCE ON X-BAND PROPAGATION

Predictions of environmental conditions within the marine atmospheric surface layer (MASL) are important to X-band radar system performance. Anomalous propagation occurs in conditions of non-standard atmospheric refractivity, driven by the virtually permanent presence of evaporation ducts (ED) in marine environments. Evaporation ducts are commonly characterized by the evaporation duct height (EDH), evaporation duct strength, and the gradients below the EDH, known as the evaporation duct curvature. Refractivity, and subsequent features, are estimated in the MASL primarily using four methods: in-situ measurements, numerical weather and surface layer modeling, boundary layer theory, and inversion methods. The existing refractivity estimation techniques often assume steady homogeneous conditions, and discrepancies between measured and simulated propagation predictions exist. These discrepancies could be attributed to the exclusion of turbulent fluctuations of the refractive index, exclusion of spatially heterogeneous refractive environments, and inaccurate characterization of the sea surface in propagation simulations. Due to the associated complexity and modeling challenges, unsteady inhomogeneous refractivity and rough sea surfaces are often omitted from simulations. This dissertation first investigates techniques for steady homogeneous refractivity and characterizes refractivity predictions using EDH and profile curvature, examining their effects on X-band propagation. Observed differences between techniques are explored with respect to prevailing meteorological conditions. Significant characteristics are then utilized in refractivity inversions for mean refractivity based-on point-to-point EM measurements. The inversions are compared to the other previously examined techniques. Differences between refractivity estimation methods are generally observed in relation to EDH, resulting in the largest variations in propagation, where most significant EDH discrepancies occur in stable conditions. Further, discrepancies among the refractivity estimation methods (in-situ, numerical models, theory, and inversion) when conditions are unstable and the mean EDH are similar, could be attributed to the neglect of spatial heterogeneity of EDH and turbulent fluctuations in the refractive index. To address this, a spectral-based turbulent refractive index fluctuation model (TRIF) is applied to emulate refractive index fluctuations. TRIF is verified against in-situ meteorological measurements and integrated with a heterogenous EDH model to estimate a comprehensive propagation environment. Lastly, a global sensitivity analysis is applied to evaluate the leading-order effects and non-linear interactions between the parameters of the comprehensive refractivity model and the sea surface in a parabolic wave equation propagation simulation under different atmospheric stability regimes (stable, neutral, and unstable). In neutral and stable regimes, mean evaporation duct characteristics (EDH and refractive gradients below the EDH) have the greatest impact on propagation, particularly beyond the geometric horizon. In unstable conditions, turbulence also plays a significant role. Regardless of atmospheric stability, forward scattering from the rough sea surface has a substantial effect on propagation predictions, especially within the lowest 10 m of the atmosphere

An Artificial Neural Network for Solar Energy Prediction and Control Using Jaya-SMC

In recent years, researchers have focused on improving the efficiency of photovoltaic systems, as they have an extremely low efficiency compared to fossil fuels. An obvious issue associated with photovoltaic systems (PVS) is the interruption of power generation caused by changes in solar radiation and temperature. As a means of improving the energy efficiency performance of such a system, it is necessary to predict the meteorological conditions that affect PV modules. As part of the proposed research, artificial neural networks (ANNs) will be used for the purpose of predicting the PV system’s current and voltage by predicting the PV system’s operating temperature and radiation, as well as using JAYA-SMC hybrid control in the search for the MPP and duty cycle single-ended primary-inductor converter (SEPIC) that supplies a DC motor. Data sets of size 60538 were used to predict temperature and solar radiation. The data set had been collected from the Department of Systems Engineering and Automation at the Vitoria School of Engineering of the University of the Basque Country. Analyses and numerical simulations showed that the technique was highly effective. In combination with JAYA-SMC hybrid control, the proposed method enabled an accurate estimation of maximum power and robustness with reasonable generality and accuracy (regression (R) = 0.971, mean squared error (MSE) = 0.003). Consequently, this study provides support for energy monitoring and control

Global path planning and waypoint following for heterogeneous unmanned surface vehicles assisting inland water monitoring

The idea of dispatching multiple unmanned surface vehicles (USVs) to undertake marine missions has ignited a burgeoning enthusiasm on a global scale. Embarking on a quest to facilitate inland water monitoring, this paper presents a systematical approach concerning global path planning and path following for heterogeneous USVs. Specifically, by capturing the heterogeneous nature, an extended multiple travelling salesman problem (EMTSP) model, which seamlessly bridges the gap between various disparate constraints and optimization objectives, is formulated for the first time. Then, a novel Greedy Partheno Genetic Algorithm (GPGA) is devised to consistently address the problem from two aspects: (1) Incorporating the greedy randomized initialization and local exploration strategy, GPGA merits strong global and local searching ability, providing high-quality solutions for EMTSP. (2) A novel mutation strategy which not only inherits all advantages of PGA but also maintains the best individual in the offspring is devised, contributing to the local escaping efficiently. Finally, to track the waypoint permutations generated by GPGA, control input is generated by the nonlinear model predictive controller (NMPC), ensuring the USV corresponds with the reference path and smoothen the motion under constrained dynamics. Simulations and comparisons in various scenarios demonstrated the effectiveness and superiority of the proposed scheme

Time series data mining: preprocessing, analysis, segmentation and prediction. Applications

Currently, the amount of data which is produced for any information system is increasing exponentially. This motivates the development of automatic techniques to process and mine these data correctly. Specifically, in this Thesis, we tackled these problems for time series data, that is, temporal data which is collected chronologically. This kind of data can be found in many fields of science, such as palaeoclimatology, hydrology, financial problems, etc. TSDM consists of several tasks which try to achieve different objectives, such as, classification, segmentation, clustering, prediction, analysis, etc. However, in this Thesis, we focus on time series preprocessing, segmentation and prediction. Time series preprocessing is a prerequisite for other posterior tasks: for example, the reconstruction of missing values in incomplete parts of time series can be essential for clustering them. In this Thesis, we tackled the problem of massive missing data reconstruction in SWH time series from the Gulf of Alaska. It is very common that buoys stop working for different periods, what it is usually related to malfunctioning or bad weather conditions. The relation of the time series of each buoy is analysed and exploited to reconstruct the whole missing time series. In this context, EANNs with PUs are trained, showing that the resulting models are simple and able to recover these values with high precision. In the case of time series segmentation, the procedure consists in dividing the time series into different subsequences to achieve different purposes. This segmentation can be done trying to find useful patterns in the time series. In this Thesis, we have developed novel bioinspired algorithms in this context. For instance, for paleoclimate data, an initial genetic algorithm was proposed to discover early warning signals of TPs, whose detection was supported by expert opinions. However, given that the expert had to individually evaluate every solution given by the algorithm, the evaluation of the results was very tedious. This led to an improvement in the body of the GA to evaluate the procedure automatically. For significant wave height time series, the objective was the detection of groups which contains extreme waves, i.e. those which are relatively large with respect other waves close in time. The main motivation is to design alert systems. This was done using an HA, where an LS process was included by using a likelihood-based segmentation, assuming that the points follow a beta distribution. Finally, the analysis of similarities in different periods of European stock markets was also tackled with the aim of evaluating the influence of different markets in Europe. When segmenting time series with the aim of reducing the number of points, different techniques have been proposed. However, it is an open challenge given the difficulty to operate with large amounts of data in different applications. In this work, we propose a novel statistically-driven CRO algorithm (SCRO), which automatically adapts its parameters during the evolution, taking into account the statistical distribution of the population fitness. This algorithm improves the state-of-the-art with respect to accuracy and robustness. Also, this problem has been tackled using an improvement of the BBPSO algorithm, which includes a dynamical update of the cognitive and social components in the evolution, combined with mathematical tricks to obtain the fitness of the solutions, which significantly reduces the computational cost of previously proposed coral reef methods. Also, the optimisation of both objectives (clustering quality and approximation quality), which are in conflict, could be an interesting open challenge, which will be tackled in this Thesis. For that, an MOEA for time series segmentation is developed, improving the clustering quality of the solutions and their approximation. The prediction in time series is the estimation of future values by observing and studying the previous ones. In this context, we solve this task by applying prediction over high-order representations of the elements of the time series, i.e. the segments obtained by time series segmentation. This is applied to two challenging problems, i.e. the prediction of extreme wave height and fog prediction. On the one hand, the number of extreme values in SWH time series is less with respect to the number of standard values. In this way, the prediction of these values cannot be done using standard algorithms without taking into account the imbalanced ratio of the dataset. For that, an algorithm that automatically finds the set of segments and then applies EANNs is developed, showing the high ability of the algorithm to detect and predict these special events. On the other hand, fog prediction is affected by the same problem, that is, the number of fog events is much lower tan that of non-fog events, requiring a special treatment too. A preprocessing of different data coming from sensors situated in different parts of the Valladolid airport are used for making a simple ANN model, which is physically corroborated and discussed. The last challenge which opens new horizons is the estimation of the statistical distribution of time series to guide different methodologies. For this, the estimation of a mixed distribution for SWH time series is then used for fixing the threshold of POT approaches. Also, the determination of the fittest distribution for the time series is used for discretising it and making a prediction which treats the problem as ordinal classification. The work developed in this Thesis is supported by twelve papers in international journals, seven papers in international conferences, and four papers in national conferences

Drainage of a deep magma reservoir near Mayotte inferred from seismicity and deformation

The dynamics of magma deep in the Earth’s crust are difficult to capture by geophysical monitoring. Since May 2018, a seismically quiet area offshore of Mayotte in the western Indian Ocean has been affected by complex seismic activity, including long-duration, very-long-period signals detected globally. Global Navigation Satellite System stations on Mayotte have also recorded a large surface deflation offshore. Here we analyse regional and global seismic and deformation data to provide a one-year-long detailed picture of a deep, rare magmatic process. We identify about 7,000 volcano-tectonic earthquakes and 407 very-long-period seismic signals. Early earthquakes migrated upward in response to a magmatic dyke propagating from Moho depth to the surface, whereas later events marked the progressive failure of the roof of a magma reservoir, triggering its resonance. An analysis of the very-long-period seismicity and deformation suggests that at least 1.3 km3 of magma drained from a reservoir of 10 to 15 km diameter at 25 to 35 km depth. We demonstrate that such deep offshore magmatic activity can be captured without any on-site monitoring

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches

Surface Wave Tomography Across Europe-Mediterranean and Middle EastBased on Automated Inter-station Phase Velocity Measurements

Seismic tomography is an imaging tool which allows to construct 3-D models of the Earth’s internal structure from observables of seismic waves. Surface wave tomography can be performed using earthquakes and ambient noise data and is sensitive to isotropic as well as anisotropic 3-D shear-wave velocity structure in broad depth ranges sampling the crust and the lithosphere-asthenosphere structure. In this study, surface wave tomography is performed to characterize the structure of the lithosphere-asthenosphere underneath the Mediterranean and the adjacent regions. We utilize a large database consists of 3800 teleseismic earthquakes recorded by 4500 broadband stations provided by IRIS and EIDA in a combination, for the first time, with waveform data from the Egyptian National Seismological Network (ENSN). An automated algorithm for inter-station phase velocities is applied to obtain fundamental mode phase velocities from this database (3.5 millions of waveforms). Path average dispersion curves are obtained by averaging the smooth parts of single-event dispersion curves. We calculated new high resolution Rayleigh and Love wave phase velocity maps using an unprecedentedly large number (200.000) of measurements in the period range from 8s-350s. In order to relate the local dispersion curves to 1-D velocity models as function of depth, the Particle Swarm Optimization (PSO) algorithm has been developed and implemented. The 3-D model has been constructed based on the obtained 1-D shear velocity model

Impact of Data Selection on the Accuracy of Atmospheric Refractivity Inversions Performed over Marine Surfaces

Within the Earth’s atmosphere there is a planetary boundary layer that extends from the surface to roughly 1 km above the surface. Within this planetary boundary layer exists the marine atmospheric boundary layer, which is a complex turbulent surface layer that extends from the sea surface to roughly 100 m in altitude. The turbulent nature of this layer combined with the interactions across the air-sea interface cause ever changing environmental conditions within it, including atmospheric properties that affect the index of refraction, or atmospheric refractivity. Variations in atmospheric refractivity lead to many types of anomalous propagation phenomena of electromagnetic (EM) signals; thus, improving performance of these EM systems requires in-situ knowledge of the refractivity. Efforts to inversely obtain refractivity from radar power returns have done so using both reflected sea clutter and bi-static radar approaches. These types of inversion methods are driven by radar measurements. This study applies a bi-static radar data inversion process to estimate atmospheric refractivity parameters in evaporative ducting conditions and examines the impacts of radar propagation loss data quantity and source location on the accuracy of refractivity inversions. Genetic algorithms and the Variable Terrain Radio Parabolic Equation radar propagation model are used to perform the inversions for three refractivity parameters. Numerical experiments are performed to test various randomly distributed amounts of synthetic data from a 100 m altitude by 60 km range domain. To compare the impact of location of data on the inverse solutions, three domains were examined from which data was sourced, including the whole domain (0 m to 100 m altitude and 0 km to 60 km range), a lower domain (0 m to 60 m altitude and 0 km to 60 km range), and a long-range domain (0 m to 100 m altitude and 30 km to 60 km range). Comparisons of inversion performance across experiments involved evaluation of several metrics: fitness scores, fitness-distance-correlations, the root-mean-square-errors of refractivity profiles, and percent errors of each individual refractivity parameter. The results of the data quantity experiments show that propagation loss measurement coverage of approximately 1% of the prediction domain yields the most accurate refractivity estimates. It is concluded that this amount of data is needed to sufficiently eliminate non-unique solutions that were observed using smaller data quantities. The results of the regional study indicate that the long-range domain produced slightly more accurate results with less data compared to the other regions. From the results of these experiments and prior studies, four specific sampling patterns were developed that were hypothesized to generate accurate inversion results. It was shown that the pattern containing the most data cells with the widest spread over the domain generated inversion results with the highest parameter and refractivity accuracy; although, a second pattern that sourced data concentrated in a short range low altitude region performed similarly with significantly less data. The results from this study enable advancement of refractivity inversion techniques by providing insight into where and how many EM measurements are needed for successful refractivity inversions. Improvements in refractivity inversion techniques enable performance improvements of EM sensing and communication technologies

Adequate model complexity and data resolution for effective constraint of simulation models by 4D seismic data

4D seismic data bears valuable spatial information about production-related changes in the reservoir. It is a challenging task though to make simulation models honour it. Strict spatial tie of seismic data requires adequate model complexity in order to assimilate details of seismic signature. On the other hand, not all the details in the seismic signal are critical or even relevant to the flow characteristics of the simulation model so that fitting them may compromise the predictive capability of models. So, how complex should be a model to take advantage of information from seismic data and what details should be matched? This work aims to show how choices of parameterisation affect the efficiency of assimilating spatial information from the seismic data. Also, the level of details at which the seismic signal carries useful information for the simulation model is demonstrated in light of the limited detectability of events on the seismic map and modelling errors. The problem of the optimal model complexity is investigated in the context of choosing model parameterisation which allows effective assimilation of spatial information in the seismic map. In this study, a model parameterisation scheme based on deterministic objects derived from seismic interpretation creates bias for model predictions which results in poor fit of historic data. The key to rectifying the bias was found to be increasing the flexibility of parameterisation by either increasing the number of parameters or using a scheme that does not impose prior information incompatible with data such as pilot points in this case. Using the history matching experiments with a combined dataset of production and seismic data, a level of match of the seismic maps is identified which results in an optimal constraint of the simulation models. Better constrained models were identified by quality of their forecasts and closeness of the pressure and saturation state to the truth case. The results indicate that a significant amount of details in the seismic maps is not contributing to the constructive constraint by the seismic data which is caused by two factors. First is that smaller details are a specific response of the system-source of observed data, and as such are not relevant to flow characteristics of the model, and second is that the resolution of the seismic map itself is limited by the seismic bandwidth and noise. The results suggest that the notion of a good match for 4D seismic maps commonly equated to the visually close match is not universally applicable