Integrating Environmental Monitoring and Observing Systems in Support of Science to Inform Decision-Making

Porter, Dwayne E.; Dorton, Jennifer; Leonard, Lynn; Kelsey, Heath; Ramage, Dan; Cothran, Jeremy; Jones, Adrian B.; Galvarino, Charlton; Subramanian, V. Anantha; Hernandez, Debra

doi:10.1016/b978-0-12-802022-7.00022-5

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…For example, in the case of limited number of buoys to be deployed, one should identify priority areas first and then deploy instruments in different regions of variability, maximizing the benefits of such observing systems [e.g., Liu et al, 2015]. For marine resources management, one also needs to properly identify priority areas for decision-making [e.g., Porter et al, 2015]. The SOM identified regions of SSH variability are consistent with the long-term mean LC patterns and their standard deviations, as well as the anomaly patterns from the joint EOF-wavelet analysis.…”

Section: Discussionmentioning

confidence: 99%

Patterns of the loop current system and regions of sea surface height variability in the eastern Gulf of Mexico revealed by the self‐organizing maps

et al. 2016

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The Self‐Organizing Map (SOM), an unsupervised learning neural network, is employed to extract patterns evinced by the Loop Current (LC) system and to identify regions of sea surface height (SSH) variability in the eastern Gulf of Mexico (GoM) from 23 years (1993–2015) of altimetry data. Spatial patterns are characterized as different LC extensions and different stages in the process of LC eddy shedding. The temporal evolutions and the frequency of occurrences of these patterns are obtained, and the typical trajectories of the LC system progression on the SOM grid are investigated. For an elongated, northwest‐extended, or west‐positioned LC, it is common for the LC anticyclonic eddy (LCE) to separate and propagate into the western GoM, while an initially separated LCE in close proximity to the west Florida continental slope often reattaches to the LC and develops into an elongated LC, or reduces intensity locally before moving westward as a smaller eddy. Regions of differing SSH variations are also identified using the joint SOM‐wavelet analysis. Along the general axis of the LC, SSH exhibits strong variability on time scales of 3 months to 2 years, also with energetic intraseasonal variations, which is consistent with the joint Empirical Orthogonal Function (EOF)‐wavelet analysis. In the more peripheral regions, the SSH has a dominant seasonal variation that also projects across the coastal ocean. The SOM, when applied to both space and time domains of the same data, provides a powerful tool for diagnosing ocean processes from such different perspectives.

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Section: Discussionmentioning

confidence: 99%

Patterns of the loop current system and regions of sea surface height variability in the eastern Gulf of Mexico revealed by the self‐organizing maps

et al. 2016

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“…The role of data management in observing systems is of increasing importance (Porter et al, 2015) due to the fact that more and more very different observing methods are combined in such systems.…”

Section: Introductionmentioning

confidence: 99%

Accessing diverse data comprehensively – CODM, the COSYNA data portal

et al. 2016

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Abstract. The coastal observation system COSYNA aims to describe the physical and biogeochemical state of a regional coastal system. The COSYNA data management is the link between observations, model results and data usage. The challenge for the COSYNA data management CODM 1 is the integration of diverse data sources in terms of parameters, dimensionality and observation methods to gain a comprehensive view of the observations. This is achieved by describing the data using metadata in a generic way and by making all gathered data available for different analyses and visualisations in an interrelated way, independent of data dimensionality. Different parameter names for the same observed property are mapped to the corresponding CF 2 standard name (Eaton et al., 2010) leading to standardised and comparable metadata. These metadata together with standardised web services are the base for the data portal. The URLs of these web services are also stored within the metadata as direct data access URLs, e.g. a map such as a GetMap request.

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“…Design of the proposed GoM CHOS was also informed by the highly successful environmental observing systems in place at regional, national, and global scales [e.g., GEO www.earthobservations.org; https://www.earthobservations.org/ geoss.php NOAA's National Weather Service (www.weather. gov)], IOOS (ioos.noaa.gov) (39)(40)(41)(42), and which provide information on atmospheric, oceanic, climate, weather, and biological conditions critical to life and livelihoods. Further details are provided in (1), which serves as a repository for information generated by the project.…”

Section: Methodsmentioning

confidence: 99%

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

Sandifer

Knapp

Lichtveld

et al. 2020

Front. Public Health

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The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental Sandifer et al. Community Health Observing System observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.

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Integrating Environmental Monitoring and Observing Systems in Support of Science to Inform Decision-Making

Cited by 6 publications

References 8 publications

Patterns of the loop current system and regions of sea surface height variability in the eastern Gulf of Mexico revealed by the self‐organizing maps

Patterns of the loop current system and regions of sea surface height variability in the eastern Gulf of Mexico revealed by the self‐organizing maps

Accessing diverse data comprehensively – CODM, the COSYNA data portal

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

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