Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"Data assimilative"”.
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Quantifying the SST biases in data assimilative ocean simulations of the Benguela Upwelling System
… to evaluate the predictive skill and impact of data assimilation, three experiments with HYCOMEnOI are evaluated: (1) with no assimilation (HYCOMFREE), (2) only assimilating along-track SLA (HYCOMSLA) and (3) assimilating both SLA and SST (HYCOMSLA+SST). Using MODIS Terra SST as reference, the …
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Physical Control of Biological Processes in the Central Equatorial Pacific: A Data Assimilative Modeling Study
<p>A five-component data assimilative ecosystem model is developed in order to investigate the effects of physical processes encompassing a wide range of space and time scales, on the lower trophic levels of the highly dynamic central equatorial Pacific (140°W). Many of the biological processes …
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Neural Operator Models as Applied to Fluid Flow Systems and Real Ocean Dynamics
Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the …
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Upwelling dynamics off Monterey Bay : heat flux and temperature variability, and their sensitivities
… Bay 06 (MB06) at-sea experiment, for which MSEAS data-assimilative baseline simulations already existed. Using the thermal energy (temperature), salinity and momentum (velocity) conservation equations, full ocean fields in the region as well as both control volume (flux) balances and local …
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Comparing Different Methods for Estimating Total Open Heliospheric Magnetic Flux
… lines are not easily distinguished in spacecraft data. Another possibility is that a portion of the open flux measured by in situ spacecraft originates from the time-dependent evolution of solar magnetic fields that is not captured by static or steady state coronal model solutions. In this …
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Fishery Interaction Modeling of Cetacean Bycatch in the California Drift Gillnet Fishery to Inform a Dynamic Ocean Management Tool
… Atmospheric Administration fisheries’ observer data from the California drift gillnet fishery, we model the relative probability of bycatch (presence–absence) of four cetacean species in the California Current System (short-beaked common dolphin Delphinus delphis, northern right whale dolphins …
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Characterising Spatial and Temporal Ionospheric Variability with a Network of Oblique Angle-of-arrival and Doppler Ionosondes
… variability metrics derived from the CSF data. The analysis of large quantities of F2 peak data shows persistent diurnal patterns in the oblique AoA observables that are also well-captured by a conventional data-assimilative ionospheric model, even without the benefit of AoA and Doppler …
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Adaptive Stochastic Reduced-Order Modeling for Autonomous Ocean Platforms
Onboard forecasting and data assimilation are challenging but essential for unmanned autonomous ocean platforms. Due to the numerous operational constraints for these platforms, efficient adaptive reduced-order models (ROMs) are needed. In this thesis, we first review existing approaches and then …
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Path planning and adaptive sampling in the coastal ocean
… realistic multi-scale current predictions from a data-assimilative ocean modeling system. In the second part of the thesis, we derive a theory for adaptive sampling that exploits the governing nonlinear dynamics of the system and captures the non-Gaussian structure of the random state fields. …
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High-Dimensional Optimal Path Planning and Multi-Timescale Lagrangian Data Assimilation in Stochastic Dynamical Ocean Environments
… planning and generalized Lagrangian Bayesian data assimilation enable the sustained and optimal operation of autonomous vehicles over a long time duration in realistic uncertain ocean settings. With this vision, the vehicles autonomously make decisions to optimally achieve their mission …
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Probabilistic regional ocean predictions : stochastic fields and optimal planning
… For the second objective, we integrate data-driven ocean modeling with our stochastic DO level-set optimization to compute and study energy-optimal paths, speeds, and headings for ocean vehicles in the Middle Atlantic Bight region. We compute the energy-optimal paths from among exact …
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High-order Discontinuous Galerkin Methods and Deep Reinforcement Learning with Application to Multiscale Ocean Modeling
… in tandem with the integration of observational data and adaptive methods. As scientists strive to better understand multiscale ocean processes, the thirst for comprehensive simulations has proceeded apace with concomitant increases in computing power, and submesoscale resolutions where …