Abstract: Research vessels supported by NSF are critical platforms contributing to academic oceanographic research in the US. The “underway” data sets obtained from the continuously operating geophysical, water column, and meteorological sensors aboard these vessels provide characterization of basic environmental conditions for the oceans and are of high scientific value for building global syntheses, climatologies, and historical time series of ocean properties (e.g the World Ocean Atlas, the GMRT bathymetric synthesis, ICOADS). The Rolling deck to Repository program (www.rvdata.us) provides a central shore-side data gateway that ensures the basic documentation, assessment and submission of all environmental data from ship operators to the NOAA long-term archives for these data. R2R provides a set of data services for the oceanographic research community, including: publishing an online, searchable and browsable master cruise catalog, supported by cruise and data set DOIs; organizing, archiving, and disseminating original underway data and documents; assessing data quality on select data types; creating select post-field data products; and supporting at-sea event logging. In this presentation we will discuss new developments in R2R data services and challenges associated with ship-based data management. A significant challenge is the dramatic increase in data volumes associated with new sensors (e.g. the EK80 Sonar systems) whereby individual cruise distributions can be several terabytes. Ship operators, R2R and NCEI must design a way to move and store these growing volumes. R2R is also working to make information more accessible and complete. A new website has been launched along with API web services that allow users to find and use data more easily. R2R is working to improve device metadata, including working to identify the time sources for all environmental sensors to support accurate comparison and merging of data sets.

Abstract: This study examines the upper-ocean processes controlling the mixed layer temperature in the western Gulf of Mexico (GOM) through estimating the contributing terms in the heat equation, with an emphasis on eddies' role. The major heat contributing terms for the upper GOM were estimated using two ocean reanalysis datasets: an eddy-resolving HYbrid Coordinate Ocean Model (HYCOM) and a Simple Ocean Data Assimilation (SODA). Analysis of net surface heat fluxes from four datasets reveals that the long-term mean net surface heat flux cools the northern GOM and warms the southern GOM. Two regions are focused for analysis: an eddy-rich region where LCEs are energetic, and the southwestern Gulf where eddy activity is relatively weak and the features of near surface temperature differ from the eddy-rich region. An eddy-rich region in the western GOM is defined based on the eddy kinetic energy derived from satellite sea surface heights. The long-term mean horizontal heat advection causes a weak warming over most of the eddy rich region, partly attributed to the flow-temperature configuration that the long-term and seasonally mean flow is nearly parallel to the corresponding mean isotherms. By contrast, the temporal mean vertical heat advection causes a strong warming in the eddy rich region, partly balancing the cooling caused by net surface heat flux. The temporal mean eddy heat flux convergence in the western GOM, whose positive and negative values are not small at some locations, appears heterogeneous in space, resulting in a small term for the western GOM when area averaged. The persistent warm water in the southwestern Gulf is primarily caused by the net warming from net surface heat flux rather than from eddies and heat advection.

Abstract: Before complex analysis of oceanographic or any earth science data can occur, it must be placed in the proper domain of computing and software resources. In the past this was nearly always the scientist's personal computer or institutional computer servers. The problem with this approach is that it is necessary to bring the data products directly to these compute resources leading to large data transfers and storage requirements especially for high volume satellite or model datasets. In this presentation we will present a new technological solution under development and implementation at the NASA Jet Propulsion Laboratory for conducting oceanographic and related research based on satellite data and other sources. Fundamentally, our approach for satellite resources is to tile (partition) the data inputs into cloud-optimized and computation friendly databases that allow distributed computing resources to perform on demand and server-side computation and data analytics. This technology, known as NEXUS, has already been implemented in several existing NASA data portals to support oceanographic, sea-level, and gravity data time series analysis with capabilities to output time-average maps, correlation maps, Hovmöller plots, climatological averages and more. A further extension of this technology will integrate ocean in situ observations, event-based data discovery (e.g., natural disasters), data quality screening and additional capabilities. This particular activity is an open source project known as the Apache Science Data Analytics Platform (SDAP) (https://sdap.apache.org), and colloquially as OceanWorks, and is funded by the NASA AIST program. It harmonizes data, tools and computational resources for the researcher allowing them to focus on research results and hypothesis testing, and not be concerned with security, data preparation and management. We will present a few oceanographic and interdisciplinary use cases demonstrating the capabilities for characterizing regional sea-level rise, sea surface temperature anomalies, and ocean hurricane responses.