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Richard P. Signell

Publications and source records attributed to Richard P. Signell.

At least 19 recordsLinked to original sources

Science storms the cloud

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte data sets. Data sets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?

AGU Advances

Cloud-native repositories for big scientific data

Scientific data have traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow toward the petabyte scale. A “cloud-native data repository,” as defined in this article, offers several advantages over traditional data repositories—performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access and inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing’s full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.

Computing in Science and Engineering

Spatial distribution of water level impact to back-barrier bays

Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a secondary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the Bay in the storm frequency band (transfers ranging from 70-100%) and tidal frequencies (10-55%). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The approach provides transfer estimates for locations inside the Bay where observations were not available resulting in a complete spatial characterization. The approach allows for the study of the Bay response to alternative forcing scenarios (landscape changes, future storms, and rising sea level). Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.

Natural Hazards and Earth System Sciences

From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows

Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.

Frontiers in Marine Science

Analysis and visualization of coastal ocean model data in the cloud

The traditional flow of coastal ocean model data is from High Performance Computing (HPC) centers to the local desktop, or to a file server where just the data needed can be extracted via services such as OPeNDAP. Analysis and visualization is then conducted using local hardware and software. This requires moving large amounts of data across the internet as well as acquiring and maintaining local hardware, software and support personnel. Further, as data sets increase in size, the traditional workflow may not be scalable. Alternatively, recent advances make it possible to move data from HPC to the Cloud and perform interactive, scalable, data-proximate analysis and visualization, with simply a web browser user interface. We use the framework advanced by the NSF-funded Pangeo project, a free, open-source Python system which provides multi-user login via JupyterHub and parallel analysis via Dask, both running in Docker containers orchestrated by Kubernetes. Data is stored in the Zarr format, a Cloud-friendly ndarray format that allows performant extraction of data by anyone without relying on data services like OPeNDAP. Interactive visual exploration of data on massive model grids is made possible by new tools in the Python PyViz ecosystem, which can render maps at screen resolution, dynamically updating on pan and zoom operations. Two example are given: (1) calculating the maximum water level at each grid cell from a 53GB, 720 time step, 9 million node triangular mesh ADCIRC simulation of Hurricane Ike; (2) creating a dashboard for visualizing data from the curvilinear orthogonal COAWST/ROMS forecast model.

Journal of Marine Science and Engineering

Community for Data Integration fiscal year 2017 funded project report

The U.S. Geological Survey Community for Data Integration annually funds small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 11 projects funded in fiscal year 2017, outlining their goals, activities, and outputs.

Open-File Report

Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System

This paper outlines strategies that would advance coastal ocean modelling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the US Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of US-based researchers with a cross-section of coastal oceanography and ocean modelling expertise and community representation drawn from Regional and US Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation, and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modelling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3–8-year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, US Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal–academic partnerships benefiting IOOS stakeholders and end users.

Journal of Operational Oceanography

Community for Data Integration 2016 annual report

The Community for Data Integration (CDI) represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the U.S. Geological Survey and the CDI community. This annual report describes the various presentations, activities, and outcomes of the CDI monthly forums, working groups, virtual training series, and other CDI-sponsored events in fiscal year 2016. The report also describes the objectives and accomplishments of the 13 CDI-funded projects in fiscal year 2016.

Open-File Report

Observations and a linear model of water level in an interconnected inlet-bay system

A system of barrier islands and back-barrier bays occurs along southern Long Island, New York, and in many coastal areas worldwide. Characterizing the bay physical response to water level fluctuations is needed to understand flooding during extreme events and evaluate their relation to geomorphological changes. Offshore sea level is one of the main drivers of water level fluctuations in semienclosed back-barrier bays. We analyzed observed water levels (October 2007 to November 2015) and developed analytical models to better understand bay water level along southern Long Island. An increase (∼0.02 m change in 0.17 m amplitude) in the dominant M 2 tidal amplitude (containing the largest fraction of the variability) was observed in Great South Bay during mid-2014. The observed changes in both tidal amplitude and bay water level transfer from offshore were related to the dredging of nearby inlets and possibly the changing size of a breach across Fire Island caused by Hurricane Sandy (after December 2012). The bay response was independent of the magnitude of the fluctuations (e.g., storms) at a specific frequency. An analytical model that incorporates bay and inlet dimensions reproduced the observed transfer function in Great South Bay and surrounding areas. The model predicts the transfer function in Moriches and Shinnecock bays where long-term observations were not available. The model is a simplified tool to investigate changes in bay water level and enables the evaluation of future conditions and alternative geomorphological settings.

Journal of Geophysical Research C: Oceans

Dynamic reusable workflows for ocean science

Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic notebooks across the geoscience domains.

Journal of Marine Science and Engineering

Technical Note: Harmonizing met-ocean model data via standard web services within small research groups

Work over the last decade has resulted in standardised web services and tools that can significantly improve the efficiency and effectiveness of working with meteorological and ocean model data. While many operational modelling centres have enabled query and access to data via common web services, most small research groups have not. The penetration of this approach into the research community, where IT resources are limited, can be dramatically improved by (1) making it simple for providers to enable web service access to existing output files; (2) using free technologies that are easy to deploy and configure; and (3) providing standardised, service-based tools that work in existing research environments. We present a simple, local brokering approach that lets modellers continue to use their existing files and tools, while serving virtual data sets that can be used with standardised tools. The goal of this paper is to convince modellers that a standardised framework is not only useful but can be implemented with modest effort using free software components. We use NetCDF Markup language for data aggregation and standardisation, the THREDDS Data Server for data delivery, pycsw for data search, NCTOOLBOX (MATLAB®) and Iris (Python) for data access, and Open Geospatial Consortium Web Map Service for data preview. We illustrate the effectiveness of this approach with two use cases involving small research modelling groups at NATO and USGS.

Ocean science and engineering

Progress and challenges in coupled hydrodynamic-ecological estuarine modeling

Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review, we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a “theory of everything” for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.

Estuaries and Coasts

Advances in a distributed approach for ocean model data interoperability

An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.

Journal of Marine Science and Engineering

Investigating the importance of sediment resuspension in Alexandrium fundyense cyst population dynamics in the Gulf of Maine

Cysts of Alexandrium fundyense , a dinoflagellate that causes toxic algal blooms in the Gulf of Maine, spend the winter as dormant cells in the upper layer of bottom sediment or the bottom nepheloid layer and germinate in spring to initiate new blooms. Erosion measurements were made on sediment cores collected at seven stations in the Gulf of Maine in the autumn of 2011 to explore if resuspension (by waves and currents) could change the distribution of over-wintering cysts from patterns observed in the previous autumn; or if resuspension could contribute cysts to the water column during spring when cysts are viable. The mass of sediment eroded from the core surface at 0.4 Pa ranged from 0.05 kg m −2 near Grand Manan Island, to 0.35 kg m −2 in northern Wilkinson Basin. The depth of sediment eroded ranged from about 0.05 mm at a station with sandy sediment at 70 m water depth on the western Maine shelf, to about 1.2 mm in clayey–silt sediment at 250 m water depth in northern Wilkinson Basin. The sediment erodibility measurements were used in a sediment-transport model forced with modeled waves and currents for the period October 1, 2010 to May 31, 2011 to predict resuspension and bed erosion. The simulated spatial distribution and variation of bottom shear stress was controlled by the strength of the semi-diurnal tidal currents, which decrease from east to west along the Maine coast, and oscillatory wave-induced currents, which are strongest in shallow water. Simulations showed occasional sediment resuspension along the central and western Maine coast associated with storms, steady resuspension on the eastern Maine shelf and in the Bay of Fundy associated with tidal currents, no resuspension in northern Wilkinson Basin, and very small resuspension in western Jordan Basin. The sediment response in the model depended primarily on the profile of sediment erodibility, strength and time history of bottom stress, consolidation time scale, and the current in the water column. Based on analysis of wave data from offshore buoys from 1996 to 2012, the number of wave events inducing a bottom shear stress large enough to resuspend sediment at 80 m ranged from 0 to 2 in spring (April and May) and 0 to 10 in winter (October through March). Wave-induced resuspension is unlikely in water greater than about 100 m deep. The observations and model results suggest that a millimeter or so of sediment and associated cysts may be mobilized in both winter and spring, and that the frequency of resuspension will vary interannually. Depending on cyst concentration in the sediment and the vertical distribution in the water column, these events could result in a concentration in the water column of at least 10 4 cysts m −3 . In some years, resuspension events could episodically introduce cysts into the water column in spring, where germination is likely to be facilitated at the time of bloom formation. An assessment of the quantitative effects of cyst resuspension on bloom dynamics in any particular year requires more detailed investigation.

Bay Of Fundy;Grand Manan Island;Gulf Of Maine;Jord

Near-bottom circulation and dispersion of sediment containing Alexandrium fundyense cysts in the Gulf of Maine during 2010-2011

The life cycle of Alexandrium fundyense in the Gulf of Maine includes a dormant cyst stage that spends the winter predominantly in the bottom sediment. Wave-current bottom stress caused by storms and tides induces resuspension of cyst-containing sediment during winter and spring. Resuspended sediment could be transported by water flow to different locations in the Gulf and the redistribution of sediment containing A. fundyense cysts could alter the spatial and temporal manifestation of its spring bloom. The present study evaluates model near-bottom flow during storms, when sediment resuspension and redistribution are most likely to occur, between October and May when A. fundyense cells are predominantly in cyst form. Simulated water column sediment (mud) concentrations from representative locations of the Gulf are used to initialize particle tracking simulations for the period October 2010–May 2011. Particles are tracked in full three-dimensional model solutions including a sinking velocity characteristic of cyst and aggregated mud settling (0.1 mm s − 1). Although most of the material was redeposited near the source areas, small percentages of total resuspended sediment from some locations in the western (~4%) and eastern (2%) Maine shelf and the Bay of Fundy (1%) traveled distances longer than 100 km before resettling. The redistribution changed seasonally and was sensitive to the prescribed sinking rate. Estimates of the amount of cysts redistributed with the sediment were small compared to the inventory of cysts in the upper few centimeters of sediment but could potentially have more relevance immediately after deposition.

Maine

U.S. Geological Survey core science systems strategy: Characterizing, synthesizing, and understanding the critical zone through a modular science framework

Executive Summary Core Science Systems is a new mission of the U.S. Geological Survey (USGS) that resulted from the 2007 Science Strategy, “Facing Tomorrow’s Challenges: U.S. Geological Survey Science in the Decade 2007–2017.” This report describes the Core Science Systems vision and outlines a strategy to facilitate integrated characterization and understanding of the complex Earth system. The vision and suggested actions are bold and far-reaching, describing a conceptual model and framework to enhance the ability of the USGS to bring its core strengths to bear on pressing societal problems through data integration and scientific synthesis across the breadth of science. The context of this report is inspired by a direction set forth in the 2007 Science Strategy. Specifically, ecosystem-based approaches provide the underpinnings for essentially all science themes that define the USGS. Every point on Earth falls within a specific ecosystem where data, other information assets, and the expertise of USGS and its many partners can be employed to quantitatively understand how that ecosystem functions and how it responds to natural and anthropogenic disturbances. Every benefit society obtains from the planet— food, water, raw materials to build infrastructure, homes and automobiles, fuel to heat homes and cities, and many others— are derived from or affect ecosystems. The vision for Core Science Systems builds on core strengths of the USGS in characterizing and understanding complex Earth and biological systems through research, modeling, mapping, and the production of high quality data on the Nation’s natural resource infrastructure. Together, these research activities provide a foundation for ecosystem-based approaches through geologic mapping, topographic mapping, and biodiversity mapping. The vision describes a framework founded on these core mapping strengths that makes it easier for USGS scientists to discover critical information, share and publish results, and identify potential collaborations that transcend all USGS missions. The framework is designed to improve the efficiency of scientific work within USGS by establishing a means to preserve and recall data for future applications, organizing existing scientific knowledge and data to facilitate new use of older information, and establishing a future workflow that naturally integrates new data, applications, and other science products to make interdisciplinary research easier and more efficient. Given the increasing need for integrated data and interdisciplinary approaches to solve modern problems, leadership by the Core Science Systems mission will facilitate problem solving by all USGS missions in ways not formerly possible. The report lays out a strategy to achieve this vision through three goals with accompanying objectives and actions. The first goal builds on and enhances the strengths of the Core Science Systems mission in characterizing and understanding the Earth system from the geologic framework to the topographic characteristics of the land surface and biodiversity across the Nation. The second goal enhances and develops new strengths in computer and information science to make it easier for USGS scientists to discover data and models, share and publish results, and discover connections between scientific information and knowledge. The third goal brings additional focus to research and development methods to address complex issues affecting society that require integration of knowledge and new methods for synthesizing scientific information. Collectively, the report lays out a strategy to create a seamless connection between all USGS activities to accelerate and make USGS science more efficient by fully integrating disciplinary expertise within a new and evolving science paradigm for a changing world in the 21st century.

Circular

Characterizing wave- and current- induced bottom shear stress: U.S. middle Atlantic continental shelf

Waves and currents create bottom shear stress, a force at the seabed that influences sediment texture distribution, micro-topography, habitat, and anthropogenic use. This paper presents a methodology for assessing the magnitude, variability, and driving mechanisms of bottom stress and resultant sediment mobility on regional scales using numerical model output. The analysis was applied to the Middle Atlantic Bight (MAB), off the U.S. East Coast, and identified a tidally-dominated shallow region with relatively high stress southeast of Massachusetts over Nantucket Shoals, where sediment mobility thresholds are exceeded over 50% of the time; a coastal band extending offshore to about 30 m water depth dominated by waves, where mobility occurs more than 20% of the time; and a quiescent low stress region southeast of Long Island, approximately coincident with an area of fine-grained sediments called the “Mud Patch”. The regional high in stress and mobility over Nantucket Shoals supports the hypothesis that fine grain sediment winnowed away in this region maintains the Mud Patch to the southwest. The analysis identified waves as the driving mechanism for stress throughout most of the MAB, excluding Nantucket Shoals and sheltered coastal bays where tides dominate; however, the relative dominance of low-frequency events varied regionally, and increased southward toward Cape Hatteras. The correlation between wave stress and local wind stress was lowest in the central MAB, indicating a relatively high contribution of swell to bottom stress in this area, rather than locally generated waves. Accurate prediction of the wave energy spectrum was critical to produce good estimates of bottom shear stress, which was sensitive to energy in the long period waves.

Continental Shelf Research

Construction of a 3-arcsecond digital elevation model for the Gulf of Maine

A system-wide description of the seafloor topography is a basic requirement for most coastal oceanographic studies. The necessary detail of the topography obviously varies with application, but for many uses, a nominal resolution of roughly 100 m is sufficient. Creating a digital bathymetric grid with this level of resolution can be a complex procedure due to a multiplicity of data sources, data coverages, datums and interpolation procedures. This report documents the procedures used to construct a 3-arcsecond (approximately 90-meter grid cell size) digital elevation model for the Gulf of Maine (71°30' to 63° W, 39°30' to 46° N). We obtained elevation and bathymetric data from a variety of American and Canadian sources, converted all data to the North American Datum of 1983 for horizontal coordinates and the North American Vertical Datum of 1988 for vertical coordinates, used a combination of automatic and manual techniques for quality control, and interpolated gaps using a surface-fitting routine.

Maine