Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

Forecasting of ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based on computationally demanding forecasts of three-dimensional ocean currents. Herein, we investigate a complementary approach for shorter time scales by using the recently proposed two-stage implicit equal-weights particle filter applied to a simplified ocean model. To achieve this, we present a new algorithmic design for a data-assimilation system in which all components – including the model, model errors, and particle filter – take advantage of massively parallel compute architectures, such as graphical processing units. Faster computations can enable in-situ and ad-hoc model runs for emergency management, and larger ensembles for better uncertainty quantification. Using a challenging test case with near-realistic chaotic instabilities, we run data-assimilation experiments based on synthetic observations from drifting and moored buoys, and analyze the trajectory forecasts for the drifters. Our results show that even sparse drifter observations are sufficient to significantly improve short-term drift forecasts up to twelve hours. With equidistant moored buoys observing only 0.1% of the state space, the ensemble gives an accurate description of the true state after data assimilation followed by a high-quality probabilistic forecast

Abundance, composition, distribution and fate of floating marine litter in the south-east Bay of Biscay

151 p.This PhD thesis presents a first overview of floating marine litter pollution in the south-east Bay of Biscay through a combination of harmonized observations, sampling methods, and numerical modelling techniques. Abundance and composition of floating marine litter (FML) were assessed combining net tows and visual observations in coastal and open waters of the Bay of Biscay. Floating riverine litter was also collected to explore the floating fraction of marine litter transported via rivers to the south-east Bay of Biscay. Simulations performed at regional (Bay of Biscay) and sub-regional scale (south-east Bay of Biscay) provided insights into the seasonal distribution patterns and fate of fishing-related and riverine litter items according to their observed buoyancy. The model was previously calibrated with data obtained from drifters released in the south-east Bay of Biscay and forced with hourly estimated and measured winds and currents. Data collection in the coastal waters of the south-east Bay of Biscay highlights the occurrence of submesoscale convergence zones for FML (¿litter windrows¿) during Spring and Summer. Fishing, shipping, and aquaculture sectors were the main source of macrolitter (size>2.5 cm) for litter windrows. Abundances derived from sampling the south-east Bay of Biscay revealed that the area is a hotspot for microplastics (size<5 mm). Most modelled particles released both in coastal andopen waters did not abandon the Bay of Biscay, reinforcing that the basin acts as accumulation region for FML. Results also demonstrated the impact of buoyancy and wind effect on FML behaviour, mainly in summer, when highly buoyant items strongly affected French Marine Protected Areas and Gipuzkoa and Pyrénées-Atlantiques regions. This thesis represents a milestone for supporting future science and policy actions in the south-east Bay of Biscay oriented to prevent and mitigate FML at local, sub-regional and regional scale

Assessing the Real-Time Lagrangian Predictability of the Operational Navy Coastal Ocean Model in the Gulf of Mexico

This study quantitatively assesses the drift predictive skill of Fleet Numerical Meteorology and Oceanography Center’s (FNMOC’s) operational ocean models which are used to support a wide range of military and civilian applications. Overall, the findings of this work support the recommendation of spatial filtering for regional-scale ocean model velocity fields used in deep-water drift applications. In conjunction with filtering, the use of a pure particle drift algorithm is suggested for short-term forecasts and a drift algorithm including a sub-grid scale, random flight, parameterization for predictions requiring extended forecast predictions. Drift prediction skill is quantified through metrics of in-cloud percentage, distance error, and cloud size, which are used to assess the impact of different drift algorithms and underlying ocean models on the drift prediction capability. Through an exploration of parameterization additions to the drift algorithm, spatial filtering of model velocity fields, and increases in model horizontal resolution, drift prediction skill is shown to be counter-balanced on the accuracy of the model\u27s dispersive characteristics along with the accuracy of the underlying model velocity field (i.e. data-constrained, predictable features). A regional scale model at a horizontal resolution typically employed by FNMOC (3-kilometers) is found to be grossly under dispersive, and derived drift predictions using a pure particle algorithm are not skillful in terms of in-cloud percentage beyond a 24-hour forecast. Parameterization additions (i.e. sub-grid scale velocity and Leeway), which enhance model dispersion, are shown to greatly improve the regional scale model\u27s ability to predict a drift cloud that encompasses an object of interest at longer forecast lengths (\u3e 24-hours) by increasing cloud size. Increasing the model’s horizontal resolution (500-meters) is likewise shown to improve in-cloud prediction performance at all forecast lengths, due to its more accurate representation of dispersion which results in much larger cloud size predictions compared to those from a regional scale model. Spatial filtering of regional scale velocity fields using a Gaussian filter removes uncertain, unpredictable features (i.e. submesocale) leaving behind a data-constrained velocity field. Even though spatial filtering suppresses dispersion further for an already under-dispersion regional scale model, filtering is shown to significantly improve drift prediction performance extending in-cloud skill farther into the forecast, reducing distance errors by 15-20%, and reducing cloud size predictions by 20-30%

Initial AtlantOS Requirements Report

Initial description from ongoing work of the societal imperatives for sustained Atlantic Ocean observations, the phenomena to observe, EOVs, and contributing observing network

Towards a 3D hydrodynamic characterization from the joint analysis and blending of multiplatform observations for potential marine applications in the southeastern Bay of Biscay

277 p.La necesidad de un mayor conocimiento y una gestión sostenible de las áreas costeras ha suscitado la instalación de observatorios que monitorizan su estado. A pesar de que la información aportada por estos observatorios es esencial la compleja hidrodinámica de estas áreas dificulta una completa caracterización de las mismas. Además, la cobertura espacial de las observaciones es, en general, relativamente escasa especialmente en la columna de agua. Por tanto, el objetivo de esta tesis es combinar los datos disponibles de diferentes plataformas de observación en el sureste del Golfo de Bizkaia proporcionados por el sistema de oceanografía operacional de la costa vasca (EuskOOS) y también por fuentes externas para caracterizar en 3D la hidrodinámica de la zona. Para ello se han analizado conjuntamente las diferentes observaciones disponibles y se han utilizado métodos de reconstrucción de datos que permiten expandir dichas observaciones en 3D. Las observaciones conjuntas permiten detectar los principales procesos hidrodinámicos como los remolinos o la corriente de talud. Por otro lado, se observa que el usode los métodos de reconstrucción evaluados es factible en el área, especialmente el de la interpolación óptima de orden reducido (ROOI). Las observaciones y las corrientes reconstruidas por el ROOI han permitido caracterizar un remolino en 3D en el área de estudio por primera vez. Además, los campos de corrientes reconstruidos han posibilitado simular la advección superficial y subsuperficial de huevos y larvas de anchoa en la zona, mostrando el potencial del ROOI para aplicaciones marinas

Lagrangian connectivity of the upper limb of the overturning circulation studied with high-resolution ocean models

Lagrangian connectivity studies with ocean models comprise the analysis of sets of virtual fluid particle trajectories to identify connecting pathways, as well as associated timescales and transports between distinct oceanic regions. They constitute a powerful tool in physical oceanography and a unique means to coherently study seawater pathways associated with the global overturning circulation (GOC). However, there are several open questions related to the (partially unresolved) impact of small-scale flow variability on large-scale Lagrangian connectivity measures. This doctoral dissertation addresses different aspects of the question how high-resolution ocean models can help improving our understanding of the spreading of water masses associated with the global overturning circulation, by: (i) reviewing the theoretical background of Lagrangian connectivity studies with ocean models, thereby highlighting the importance to distinguish Lagrangian analyzes of simulated purely advective volume transport trajectories from Lagrangian modeling approaches to estimate advective-diffusive tracer trajectories; (ii) investigating Lagrangian volume transport pathways and along-track tracer changes of the GOC’s upper limb in the South Atlantic (study 1) and Indian Ocean (study 2) with high-resolution models; and (iii) assessing the performance of near-surface particle dispersal simulations in the extended Agulhas Current system by means of lateral eddy diffusivity estimates (study 3).Lagrangesche Konnektivitätsstudien mit Hilfe von Ozeanmodellen stellen ein wichtiges Analysetool in der physikalischen Ozeanographie dar. Sie beruhen auf der Simulation von Trajektorien virtueller Fluidpartikel, und ihrer Auswertung hinsichtlich dominanter Ausbreitungspfade und -zeiskalen, sowie Volumen- oder Tracertransporte zwischen ausgewählten Ozeangebieten. Dennoch bestehen einige offene Fragestellungen bezüglich des Einflusses von (nicht vollständig aufgelösten) relativ kleinskaligen Prozessen auf großskalige Langrangesche Konnektivität. In dieser Doktorarbeit wurden verschiedene Aspekte der Frage untersucht, wie Lagrangesche Konnektivitätsstudien mit hochauflösenden Ozeanmodellen zu einem verbesserten Verständnis der Wassermassenausbreitung der globalen Umwälzbewegung beitragen können. Es wurden (i) Theorie und bisherige Anwendungen Lagrangescher Konnektivitätsstudien mit Ozeanmodellen zusammengefasst, und auf die Wichtigkeit hingewiesen Lagrangesche Analysen von rein advektiven Volumentransportpfaden und Lagrangesche Modellierung advektiv-diffusiver Tracerausbreitung zu unterscheiden; (ii) Lagrangesche Volumentransportpfade der globalen Umwälzbewegung im Südatlantik (Studie 1) und Indischen Ozean (Studie 2) in hochauflösenden Ozeanmodellen bestimmt und die damit verbundenen Netto-Wassermassentransformationen analysiert; und (iii) simulierte oberflächennahe Lagrange Ausbreitungspfade im Agulhasstromsystem mit beobachteten Driftertrajektorien hinsichtlich abgeleiteter Wirbel-Diffusivitäten verglichen (Studie 3)

Tracer and Timescale Methods for Passive and Reactive Transport in Fluid Flows

Geophysical, environmental, and urban fluid flows (i.e., flows developing in oceans, seas, estuaries, rivers, aquifers, reservoirs, etc.) exhibit a wide range of reactive and transport processes. Therefore, identifying key phenomena, understanding their relative importance, and establishing causal relationships between them is no trivial task. Analysis of primitive variables (e.g., velocity components, pressure, temperature, concentration) is not always conducive to the most fruitful interpretations. Examining auxiliary variables introduced for diagnostic purposes is an option worth considering. In this respect, tracer and timescale methods are proving to be very effective. Such methods can help address questions such as, "where does a fluid-born dissolved or particulate substance come from and where will it go?" or, "how fast are the transport and reaction phenomena controlling the appearance and disappearance such substances?" These issues have been dealt with since the 19th century, essentially by means of ad hoc approaches. However, over the past three decades, methods resting on solid theoretical foundations have been developed, which permit the evaluation of tracer concentrations and diagnostic timescales (age, residence/exposure time, etc.) across space and time and using numerical models and field data. This book comprises research and review articles, introducing state-of-the-art diagnostic theories and their applications to domains ranging from shallow human-made reservoirs to lakes, river networks, marine domains, and subsurface flow

北極海での氷中航行支援のための海氷変動予測を目指した海氷/海洋連成計算

学位の種別:課程博士University of Tokyo(東京大学

Stochastic modelling and inference of ocean transport

Inference of ocean dynamical properties from observations requires a suite of sta- tistical tools. In this thesis we assemble and develop a selection of useful methods for oceanographic inference problems. Our work is centred around the modelling of ocean transport. We consider Lagrangian observations, including those obtained from surface drifters. We adopt a Bayesian approach which offers a coherent frame- work for diagnosing and predicting ocean transport and enables principled uncer- tainty quantification. We also emphasise the role of stochastic models. We begin with the problem of comparing stochastic models on the basis of ob- servations. We apply Bayesian model comparison to classical stochastic differential equation models of turbulent dispersion given trajectory data generated by simula- tion of particles in an idealised forced–dissipative model of two-dimensional turbu- lence. We discuss how model preference is quantifiably sensitive to the timescale on which the models are applied. The method is widely applicable and accounts for uncertainty in model parameters. We then consider purely data-driven models for particle dynamics. In particular we build a probabilistic neural network model of the single-particle transition density given observations from the Global Drifter Program. The transition density model can be used either to emulate surface transport, by modelling trajectories as a discrete- time Markov process, or to estimate spatially-varying dynamical statistics including diffusivity. As is standard for probabilistic neural networks we train our model to maximise the likelihood of data. The model outperforms existing stochastic models, as assessed by skill scores for probabilistic forecasts, and is better able to deal with non-uniform data than standard methods. A weakness of our transition density model is that, since it is trained by maximum likelihood rather than Bayesian inference, its predictions come without uncertainty quantification. This is especially concerning in regions where little data is available and point estimates of statistics such as diffusivity cannot be trusted. With this mo- tivation we discuss state-of-the-art methods in approximate Bayesian inference and their effectiveness in building Bayesian neural networks. We highlight deficiencies in current methods and identify the key challenges in providing uncertainty quan- tification with neural network models. We illustrate these issues both in a simple one-dimensional problem and in a Bayesian version of our transition density model