91 research outputs found
Pangeo: A Community and a Framework for Flexible, Scalable Open-Source Geoprocessing
No full textPangeo is a community promoting open, reproducible, and scalable science and the development of tools, infrastructure, and educational materials to make science more efficient. This talk will describe Pangeo\u27s open-source Python-based framework for highly scalable data-proximate analysis and visualization. The framework can be used on local machines, on high-performance computing systems and on any commercial Cloud (e.g., AWS, Google, Azure, OpenStack) using a web browser as the interface. Through an interactive demo, attendees will hopefully see how these tools could be used to solve their science problems, take advantage of cloud computing, and optimize analysis ready data for the Cloud.
Presenter Bio
Rich Signell is a research oceanographer at the US Geological Survey in Woods Hole. He graduated from the University of Michigan School of Engineering with a B.S. in Atmospheric and Oceanic Science in 1983, obtained a MS in Physical Oceanography from MIT in 1987, and a Ph.D. from the WHOI/MIT Joint Program in Physical Oceanography in 1989.
Rich\u27s early work at the USGS focused on dispersion and transport in coastal waters, and included the hydrodynamic simulations for the relocation of Boston’s sewage outfall to clean up of Boston Harbor. He has worked on a number of environmental sediment issues, including Massachusetts Bay, Lake Pontchartrain, and Long Island Sound. He also worked for the NATO Undersea Research Center in La Spezia, Italy from 2001-2004. Rich has a long-standing interest in data management, analysis and visualization, promoting standards and standards-based modeling tools for the last 20 years. He has been active on the Pangeo Project for the last three years and is a member of the Pangeo Steering Committee
Tide- and wind-forced currents in Buzzards Bay, Massachusetts
No full textThesis (M.S.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1987.Microfiche copy available in Archives and Science.Bibliography: leaves 83-86.by Richard Peter Signell.M.S
Tidal dynamics and dispersion around coastal headlands
No full textSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 1989The dynamics of shallow tidal currents and tide-induced dispersion are investigated around coastal headlands that have alongshore length scales that are comparable to or less than the tidal excursion. Depth-averaged shallow water equations forced by oscillatory flow are solved numerically for Gaussian headlands. The tidal flows around these headlands are shown to be characterized by flow separation and. transient eddy formation. Idealized models of flow separation and the transport and damping of vorticity away from the headland explain much of the observed behavior. The characteristics of the separated wake are compared with known results from the study of viscous flow around bluff bodies. The kinematics of particle dispersion in the numerical solutions is described and analyzed.This work was supported by NSF grant OCE-87-11031 and the National Center for
Atmospheric Research. Additional funding was provided by the Woods Hole Oceanographic
Institution's Ocean Venture Fund, Coastal Research Center, and Education Program
Selected Papers from the 15th Estuarine and Coastal Modeling Conference
No full textThe 15th Estuarine and Coastal Modeling Conference provides a venue for commercial, academic, and government scientists and engineers from around the world to present and discuss the latest results and techniques in applied estuarine and coastal modeling. Prospective authors are invited to submit papers on a wide range of topic areas, including:• Pollutant Transport and Water Quality Prediction• Coastal Response to Climate Change• Modeling Techniques and Sensitivity Studies• Model Assessment• Modeling Specific Estuarine and Coastal Systems• Visualization and Analysis• Wave and Sediment Transport Modeling• Modeling of Chemicals and Floatables• Oil Spill Transport and Fate Modeling• Inverse Methods• Circulation Modeling• Facility Siting and CSO Studies• Data Assimilation• Nowcast/Forecast Modeling Systems• Modeling Systems with Strong Buoyancy Forcing• Modeling of Coupled Systems• Risk Analysis (Nuclear Reactors, Flood Forecasting
Spatial distribution of water level impacting back-barrier bays
No full text© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Aretxabaleta, A. L., Ganju, N. K., Defne, Z., & Signell, R. P. Spatial distribution of water level impacting back-barrier bays. Natural Hazards and Earth System Sciences, 19(8), (2019): 1823-1838, doi: 10.5194/nhess-19-1823-2019.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 complementary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Bay area and 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 a 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 50 %–100 %) and tidal frequencies (10 %–55 %). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The wind setup effect can be comparable in magnitude to the offshore transfer forcing during intense storms. The approach provides transfer estimates for locations inside the bay where observations were not available, resulting in a complete spatial characterization. An extension of the methodology that takes advantage of the ADCIRC tidal database for the east coast of the United States allows for the expansion of the approach to other bay systems. 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.This work was supported by the US Geological Survey, Coastal and Marine Hazards/Resources Program
Dynamic Reusable Workflows for Ocean Science
No full textDigital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog searches and data access now make it 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 enter 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 geoscience domains
Analysis and Visualization of Coastal Ocean Model Data in the Cloud
No full textThe 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 needed data can be extracted via services such as OPeNDAP. Analysis and visualization are 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 are stored in the Zarr format, a Cloud-friendly n-dimensional array format that allows performant extraction of data by anyone without relying on data services like OPeNDAP. Interactive visual exploration of data on complex, large 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 examples are given: (1) Calculating the maximum water level at each grid cell from a 53-GB, 720-time-step, 9-million-node triangular mesh ADCIRC simulation of Hurricane Ike; (2) Creating a dashboard for visualizing data from a curvilinear orthogonal COAWST/ROMS forecast model
Advances in a Distributed Approach for Ocean Model Data Interoperability
No full textAn 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
Model Data Interoperability for the United States Integrated Ocean Observing System (IOOS)
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