Vembu Subramanian

Integrating Environmental Monitoring and Observing Systems in Support of Science to Inform Decision-Making: Case Studies for the Southeast

Abstract IOOS ® is a network of coastal and ocean observing systems providing data, data management, models and other products in support of science-based decision-addressing issues of marine safety, coastal hazards, climate variability, ecosystem management, and water quality. IOOS Regional Associations (RAs) work at federal, state, and local levels assuring that systems provide information at appropriate spatial and temporal scales addressing local to regional concerns and informing decision-making. Stakeholder engagement is the primary way for RAs to make positive impacts within their region. The Southeast Coastal Ocean Observing Regional Association (SECOORA) strives to identify stakeholders, determine observing needs, and provide data and products to support science-informed decision-making. In this chapter, we review the state of observing system efforts in the Southeast; present case studies demonstrating the value of integrating data from these systems to support marine safety, water quality, and ecosystem management decision-making; and present recommendations for the path forward.

IOOS vocabulary and ontology strategy for observed properties

With the rapid growth of coastal ocean observations becoming available for integration by US Integrated Ocean Observing System (IOOS) Regional Associations and federal data assembly centers, there is a need for the establishment of IOOS Parameter Vocabulary strategy. Currently, different data naming conventions are being used by existing regional and subregional coastal ocean observing systems. This makes things complicated for the discovery, access and proper usage of the valuable data. To eliminate the misuse and misinterpretation of the data being made available and to facilitate the discovery and proper use of in data scientific research and other management applications, the authors have presented the development of IOOS Parameter Vocabulary and recommended a strategy to move this forward with ocean observing community engagement.

Improvements in Data Management Practices within West Florida Shelf Coastal Ocean Monitoring and Prediction System

In this paper, we describe the improvements that are being carried out in data management practices within West Florida Shelf Coastal Ocean Monitoring and Prediction System (COMPS). COMPS, has been in operation since 1997 providing near real-time weather and numerical ocean circulation models data needed for public, federal, state and local emergency management officials and researchers via Internet (http://comps.marine.usf.edu). COMPS program has grown since 1997, and presently we maintain twelve coastal and eight offshore buoy weather monitoring stations located along the coast and offshore of the West Florida Shelf. In addition to in-situ weather monitoring platforms, we also maintain a Hi-Frequency radar network that provides surface currents up to 200 km from the shore. With the growth in the COMPS program, and its participation in various Regional and National Ocean observing system data management related activities and projects, we took a project to make improvements in data management practices and COMPS web site. Preliminary results are presented here.

Data Return Aspects of CODAR and WERA High-Frequency Radars in Mapping Currents

Two types of high-frequency (HF) radar systems, long-range CODAR SeaSonde and medium-range WERA, are concurrently operated on the West Florida Coast for the purpose of observing coastal ocean currents and waves. In this chapter, we examine the data return aspect of HF radar performance, using radial currents measured with the CODAR SeaSonde and WERA systems at the same site origin – Venice, Florida. Based on the data collected during February 2 – 5 March, 2014, our analysis revealed that the two HF radar systems exhibited complicated data return variations in both the spatial and temporal domains. Even though data return was generally higher near the site origin rather than in the outer band of the offshore radar footprint, it was unevenly distributed across the bearing angles. The long-range CODAR tended to have more data return in the northern half of its footprint, while the medium-range WERA’s data return was more evenly distributed across the bearing angles. Both radar systems exhibited diurnal and synoptic variations in data return; however, the peak performance hours differed. The 4.90 MHz CODAR system tended to have a higher data return during the daytime hours, while the 12.58 MHz WERA system tended to return more data during nighttime hours. The CODAR system exhibited increased data return performance during the conditions of high sea state, while the WERA system’s performance did not exhibit an obvious sea state relationship with waves measured using an offshore Waverider buoy.

An end-to-end workflow for assessing sea surface temperature, salinity and water level predicted by coastal ocean models

Southeast Coastal Ocean Observing Regional Association (SECOORA) is currently supporting a multiscale, multi-resolution modeling subsystem for the US Southeast coastal waters to deliver model data and products for coastal resource and emergency response managers and other users. The models that are currently supported in the SECOORA foot print include: regional scale nowcast/forecast ocean circulation modeling system; estuarine and surge/inundation prediction (nowcast/forecast); beach water quality modeling in support of swimming advisories and fisheries habitat modeling for improving stock assessment. Effective use of coastal ocean model forecasts requires a thorough understanding of model skill for different environmental scenarios, regions and times. Computation of model skill, however, has historically been difficult due to varying data conventions, distribution techniques and lack of general tools for discovery, access and use. SECOORA is addressing this problem by developing reproducible workflows for model skill assessment that can be run on any Mac, Windows or Linux computer using free, extensible software. The workflow first discovers datasets via a catalog search over the distributed data holdings of US-IOOS, using bounding box, time range and variable search capabilities of the Open Geospatial Consortium (OGC) Catalog Services for the Web (CSW). The workflow then locates known web service endpoints (OGC Sensor Observation Service (SOS) for sensor data, OPeNDAP with Climate Forecast (CF) Conventions for model output) in the metadata, and extracts data directly from these distributed services. The workflow then extracts time series from the observations and models, does QA/QC, and computes skill metrics in an automated fashion. The results are also displayed qualitatively in an interactive mapping function. The workflow has been written using python within the IPython Notebook (aka Jupyter Notebook), which allows using a standard web browser as a client, and documents the workflow. The environment required to run the notebooks has also been standardized, allowing anyone to install and reproduce our results using free software in a matter of minutes.

Infrastructure and tools for serving, accessing, and analyzing ocean information from the Integrated Ocean Observing System

According to the Integrated Coastal Ocean Observation System (ICOOS) Act of 2009 the U.S. Integrated Ocean Observing System (IOOS®) Enterprise extends across 17 federal agencies and 11 regional associations and includes numerous actors from within those organizations. One of the primary functions IOOS provides is a Data Management and Communications (DMAC) Subsystem that aims to make discoverable and accessible data and information from multiple disciplines across the aforementioned enterprise. With such diverse participation and broad mandate for the types of data included in IOOS, it is unrealistic to expect that a single data center is capable of aggregating, managing, curating, and distributing all of the ocean data of interest to the IOOS enterprise. Instead, the IOOS enterprise implements a distributed data network bound together by a few key features of a shared vision for data discovery and access. This paper will discuss progress and lessons learned from nearly ten years of experience in creating or adapting the standards, tools, and community needed to develop and maintain the distributed data network that will support IOOS efforts in science, operational decision making, and product delivery. The current configuration of the DMAC subsystem of IOOS is a combination of people, process, and technology that provide a service to the nation. The primary service DMAC provides is to deliver well curated and documented ocean data andinformation to the public using the World Wide Web as the primary platform. Within these three areas (people, process, and technology) choices are made based on resources, policy mandates, available skills, technical maturity and capability, and customer requirements. Collectively the choices within each area determine the architecture of the DMAC system and will form the organization for this paper. The “people” form the main stakeholder groups of IOOS, both the builders and the users, so understanding how these various stakeholder groups work cooperatively to grow the DMAC system is critical to progress. The “process” area determines how the people work together and the policy constraints the system is under. Finally, the “technology” includes the software and standards DMAC implements to address the system needs.