Performance_Work_Statement_-_Quatification_of_Water_Level_Measurement_Uncertainty.pdf

NOAA Na�onal Ocean Service Center for Opera�onal Oceanographic Products and Services Engineering Division

Performance Work Statement Quantification of Water Level Measurement Uncertainty

141016-24-0057

The Center for Operational Oceanographic Products and Services (CO-OPS) collects, analyzes and distributes historical and real-time observations and predictions of water levels, coastal currents and other meteorological and oceanographic data. This is part of an integrated National Ocean Service (NOS) program supporting safe maritime navigation, more productive water-borne commerce, and the needs of the National Weather Service, coastal zone management, engineering and surveying communities. The Center manages the National Water Level Observation Network (NWLON) and National Current Observation Program (NCOP) around the coastal United States and the national network of Physical Oceanographic Real-Time Systems (PORTS®) in major U.S. harbors. CO-OPS resides within NOAA's National Ocean Service.

CO-OPS maintains a variety of sensors and equipment needed to ensure the efficient collection and dissemination of water level, currents, and meteorological data. In order to maintain these systems, CO-OPS uses a network of systems designed to calibrate, set, and configure sensors and electronics for use in the NWLON and PORTS observatories. Data gathered from these sensors undergo a complex Data Quality and Assurance Procedure (DQAP) to produce an average water level value every 6 minutes.

1. BACKGROUND

CO-OPS is aware of recent efforts throughout international oceanographic and meteorological measurement communities to improve uncertainty quantification of real-time measurements and derived analysis products. Although CO-OPS invests a significant level of effort in sensor test and evaluation efforts from which results are used to estimate measurement error and sensor accuracies, it currently does not provide an uncertainty quantification for real-time observations provided by its observing system networks.

Our current measurement practice is to conduct a calibration validation of manufacturer supplied equipment within the controlled laboratory environment. The often complex calculation of a comprehensive and consistent uncertainty budget can be a time-consuming task. Developing a correct probability distribution based on input from each uncertainty component often requires information and technical expertise that are not readily available.

The uncertainty of measurement is a quantitative indication of the variability of the quantity value.

It is a result of incomplete knowledge of the value of the measured quantity and the factors influencing it. It is important to quantify and document measurement uncertainty so it can be used for decision making regarding the suitability of using a given measurement in various applications and for properly defining statistical confidence in derived analysis products.

CO-OPS has gone to great lengths to disclose all known details associated with measurement error for the sensors employed by real-time field systems. In 2018, CO-OPS formally documented

Performance Work Statement – NOAA CO-OPS – May 2024 the National Water Level Observation Network (NWLON) Requirements which detailed minimum data accuracy and uncertainty requirements and supporting documentation. These error 1 estimates can be easily applied to estimate the accuracy of a sensor or measurement system.

CO-OPS invests heavily in the test and evaluation of all new water level sensors and derivation of final measurement system error estimates go far beyond the simple approach of stating vendor provided specifications. Every new make/model of a water level sensor undergoes a broad range of laboratory and field testing prior to transitioning to operational use. Field testing involves data collection across a variety of coastal environment types and is often conducted for durations of 12 months and longer. Additionally, results from controlled lab testing and field testing are used to design and develop laboratory calibration facilities and to develop calibration methods that are specifically targeted toward reducing measurement uncertainty. Furthermore, most of CO-OPS’ measurement system test and evaluation results and data sets are presented at external conferences and published within publicly available domains.

However, CO-OPS published measurement error estimates are different from a quantified uncertainty. They are mainly based on empirical test results that demonstrate the difference between the measurements from a test sensor and some other reference. Although laboratory measurements and associated testing involve a well defined reference or standard, field observations often involve comparison to a variety of other measurement technologies. Although significant effort is put forth to ensure references include a variety of measurement technologies, and where available, a well established, well proven reference sensor, summary field test results are actually better defined as statistics that summarize the precision between a test and reference sensor (or multiple references).

2. SCOPE

CO-OPS seeks outside expertise and support for the development and exploration of a measurement uncertainty quantification method, following the latest guidance provided by the International Bureau of Weights and Measures (BIPM) Guide to the Expression of Uncertainty in Measurement (GUM). As discussed in Appendix A, Summary of Issue, CO-OPS does not have a rigorous validated process to quantify water level measurement uncertainty. A summary of our Observing Programs can be found in Appendix B.

The objective of this agreement is to advance the exploration and refinement of a method for quantifying measurement uncertainty, aligning with the contemporary standards observed in the global oceanographic and meteorological measurement communities. The scope of work encompasses tasks such as data analysis, custom coding, algorithm modification, testing, and documentation of all CO-OPS water level observation systems. These efforts are directed towards the comprehensive identification and quantification of significant sources of measurement error and uncertainty associated with the diverse range of water level sensors utilized by CO-OPS across various coastal environments and conditions.

1 National Water Level Observation Network Requirements, NOAA’s National Ocean Service Center for Operational Oceanographic Products and Services (CO-OPS), October 2018.

Performance Work Statement May 2024

The Contractor will work at their own location, and shall be required to attend bi-weekly virtual project meetings with Project Officers located in the Chesapeake, VA CO-OPS Field Office.

CO-OPS will make all existing data sensor data available for digital retrieval. All work product, data, and publications resulting from this effort shall be provided, in its entirety, to CO-OPS in a digital, unlocked, editable format.

A summary of known contributors to measurement uncertainty for each of the primary water level sensor types employed by CO-OPS observing system network listed above is also included in Appendix A. These sources of uncertainty reflect all known factors. Sampling rate and averaging computation every six minutes is also outlined, and varies between different sensor types.

3. TASKS AND DELIVERABLES

As an independent entity, not representing the Department of Commerce, National Ocean Service, or CO-OPS, the Contractor is tasked with providing comprehensive services outlined below. This involves supplying qualified personnel, materials, equipment, and facilities not otherwise supplied by the Government. The Contractor will engage in all facets of the assessment, encompassing requirements analysis, system design, testing, data analysis, document drafting, and document publication.

The primary objective of this contract is to pioneer a method for quantifying measurement uncertainty, aligning with the latest international standards in oceanographic and meteorological measurements, particularly as articulated in the International Bureau of Weights and Measures (BIPM) Guide to the Expression of Uncertainty in Measurement (GUM). This objective will be 2 achieved through a thorough analysis and assessment of data from each specified sensor. Tasks include data analysis, custom coding, software design, algorithm modification, testing, documentation, operation, and maintenance. The goal is to identify and quantify sources of measurement error and uncertainty for the diverse suite of water level sensors used by CO-OPS across coastal environments.

3.1. Review and Evaluate CO-OPS’ Quantification of Measurement Uncertainty (attachment 1).

Task 1: Analyze the provided documentation and assess the Quantification of Measurement Uncertainty for each sensor.

Task 2. Conduct a thorough examination of each sensor, identifying areas for improvement and recommending corrections, including additional sources of uncertainty.

2 The International Bureau of Weights and Measures (BIPM) Guide to the Expression of Uncertainty in Measurement (GUM) is a document that provides a framework for evaluating and expressing the uncertainty associated with measurements. The GUM was first published in 1993 and has since been updated in 1995 and 2008.

Task 3. Evaluate the reliability of the current uncertainty methodology employed by CO-OPS for each sensor.

Task 4. If the uncertainty methodology utilized by CO-OPS is determined to be unreliable or sub-optimal for any given sensor, provide a summary detailing a new or enhanced methodology to be implemented.

Deliverable 1: Sensor-Specific Assessments and Recommendations.

● Thoroughly document assessments for each sensor.

● Document any additional sources of sensor measurement errors identified and the reliability of uncertainty methodology being employed.

● Identify and recommend improvement areas for existing uncertainty methodology for each sensor.

● If recommended, a summary of the new methodology to be implemented

3.2. Engage in the exploration and development of a measurement uncertainty quantification method in alignment with the latest international standards in oceanographic and meteorological measurements.

Task 5: Evaluate all known contributors to uncertainty for each water level sensor type utilized within NWLON.

Task 6: Employ probabilistic approaches to formulate a measurement uncertainty quantification specifically tailored for NWLON water level measurements.

Deliverable 2: Develop Methods and Protocols for Quantifying Statistical Uncertainty of CO-OPS Water Level Observations

● Create a code repository to ensure uncertainty calculation transparency and reproducibility.

● Documentation:

○ Comprehensive analyses and assessments, and offer detailed recommendations based on the evaluation of uncertainty contributors and the application of probabilistic methods.

○ M ethods for quantifying statistical uncertainty of CO-OPS water level observations

○ All recommended new and/or updated uncertainty methodologies to be implemented for each sensor.

○ Proposed new or updated methodology

3.3. Additional Data Collection

Task 7: If deemed pertinent, propose the collection of additional data that could enhance the analyses, exploring the potential for collaboration and sharing of CO-OPS sensors and equipment.

Deliverable 3: Document thorough analyses and assessments, accompanied by a detailed summary outlining the rationale and specific recommendations related to the potential collection of additional data, considering collaborative efforts and shared resources from CO-OPS.

3.4. Comprehensive Final Report Development

Task 8: Draft a comprehensive final report for review and commentary. The report should encompass a summary of the operational impact associated with each sensor, along with recommendations for mitigating uncertainty, if identified, for each sensor.

Task 9: Draft a peer-reviewed journal article encompassing all documentation and detailing the uncertainty methodology and its application at a subset of NOAA water level stations

Deliverable 4: Submit the draft final report and journal article in digital format to CO-OPS for thorough review, comment, and editing.

Task 9: Finalize the reports.

Deliverable 5: Submit the finalized report and journal article in digital format to CO-OPS, ensuring submissions incorporate all feedback and are ready for dissemination / publication.

4. GENERAL PRINCIPLES FOR DATA AND WRITTEN REPORTS

4.1. All databases shall be compliant with current data standards.

4.2. All applications, data, and reports shall be designed to assure integration and interoperability.

4.3. All applications, data, and reports shall be delivered in a timely fashion determined by the Task Manager. Applications shall include source code and detailed specifications.

4.4. Written reports shall be fully editable and provided in standard PDF file format.

4.5. Images, illustrations, and tables used in the report shall also be provided as individual files in a separate digital folder. Acceptable formats for individual image, illustration, and table files are as follows:

● Comma-separated values (.csv)

● ASCII text (.txt)

● Microsoft Excel (.xlsx)

● Microsoft Word (.docx)

● Graphics interchange format (.gif)

● Joint Photographic Experts Group (.jpeg / .jpg)

● Portable Network Graphics (.png)

● Tag Image File Format (.tiff)

● RAW (for uncompressed and unprocessed image data only)

● Portable Document Format (.pdf), and

● Audio-Video Interleave format (.avi).

5. APPLICABLE PROCEDURES AND DOCUMENTS

CO-OPS created the publicly accessible CO-OPS Field Library in order to distribute field operations reference material to in-house and contract field personnel. The Field Library contains CO-OPS Standard Operating Procedures, check lists, protocols, equipment manuals, diagrams, project instructions, and references, etc. with sensitive information removed. All references in this PWS can be found in the Field Library or provided to the Awardee via the COR.

In addition to the references mentioned in the body of this PWS, please refer to the following documents (or their most updated versions) for information regarding the installation, servicing and maintenance of CO-OPS observation systems.

5.1. Uncertainty References

● U.S. CLIVAR Uncertainty Quantification Working Group:

https://usclivar.org/working-groups/ocean-uncertainty-quantification-working-gr oup

● Bushnell, M., Waldmann, C., Seitz, S., Buckley, E., Tamburri, M.,, Hermes, J., Henslop, E., Lara-Lopez, A., Quality Assurance of Oceanographic Observations: Standards and Guidance Adopted by an International Partnership, Frontiers in Marine Science, v6, 2019, DOI=10.3389/fmars.2019.00706 https://www.frontiersin.org/articles/10.3389/fmars.2019.00706

● Supplementary Material Bushnell, et al, 2019:

https://www.frontiersin.org/articles/10.3389/fmars.2019.00706/full#supplementa ry-material

● Guide to the Expression of Uncertainty in Measurement (GUM) (BIPM, 2008) https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0 ef43f-baa5-11cf-3f85-4dcd86f77bd6

● CO-OPS Sensor Specifications and Measurement Algorithms:

https://tidesandcurrents.noaa.gov/publications/CO-OPS_Measurement_Spec.p df

● National Water Level Observation Network Requirements, NOAA’s National Ocean Service Center for Operational Oceanographic Products and Services (CO-OPS), October 2018.

5.2. Sensor References

● User’s Guide for Model 4100/4110 Series Aquatrak

● Aquatrak Calibration Procedure

● Field Installation Procedures for Design Analysis WaterLog H3611i Microwave

Radar Water Level Sensor Using the Sutron Data Collection Platform, Ver 1.0

● MWWL Sensor Annual Inspection Checklist

● WaterLog H-3551 Gas Purge Bubbler Owner’s Manual

● WaterLog H-334 Absolute Shaft Encoder Owner’s Manual

● Field Installation Procedures for Design Analysis WaterLog® H3611i

Microwave Radar Water Level Sensor Using the Sutron Data Collection Platform

Other references or publications listed in the CO-OPS Field Library may be obtained by contacting a POC listed in Section 9. GOVERNMENT POINT OF CONTACT.

6. PERIOD OF PERFORMANCE

The Period of Performance shall be twelve months from the date of award of this contract, and shall include all elements listed in Section 3. TASKS AND DELIVERABLES.

7. MISCELLANEOUS

As needed, the Contractor shall participate with assigned government personnel in data integration and implementation of shared information management resources.

8. IT SECURITY

The Certification and Accreditation (C&A) or Assessment and Authentication (A&A) requirements of Clause 48 CFR 1352.239-72 do not apply, and a Security Accreditation Package is not required. The contractor will not supply any software products. The contractor will not have remote access to any government owned IT equipment.

9. GOVERNMENT POINT OF CONTACT

The Primary Points of Contact (POC) for the Center of Operational Oceanographic Products and Services are:

CO-OPS Chief Scientist:

Dr. Greg Dusek

NOAA/NOS/CO-OPS

1305 East West Hwy Bldg. SSMC4 Silver Spring, MD 20910 240-533-0588 gregory.dusek@noaa.gov

CO-OPS Deputy Chief Scientist:

Dr. Laura Fiorentino

NOAA/NOS/CO-OPS

672 Independence Pkwy Chesapeake, VA 23320 757-302-8072 laura.fiorentino@noaa.gov

Contract Rep:

Jennifer McRae

NOAA/NOS/CO-OPS

672 Independence Pkwy Chesapeake, VA 23320 757-302-8051 jennifer.mcrae@noaa.gov

CO-OPS Signatory Date

Performance Work Statement May 2024

Digitally signed by Scott Mowery Date: 2024.08.05 11:17:33 -04'00'

APPENDIX A – SUMMARY of ISSUE

Quantification of Measurement Uncertainty

Initial thoughts for CO-OPS Real-Time Water Level Measurement Applications

This short paper provides a summary problem statement and suggests a way forward for the development and exploration of a measurement uncertainty quantification method, following the latest guidance provided by the International Bureau of Weights and Measures (BIPM) Guide to the Expression of Uncertainty in Measurement (GUM).

Measurement Uncertainty

Our current measurement practice is to conduct a calibration validation of manufacturer supplied equipment within the controlled laboratory environment. The uncertainty of measurement is a quantitative indication of the variability of the quantity value. It is a result of incomplete knowledge of the value of the measured quantity and the factors influencing it. It is important to quantify and document measurement uncertainty so it can be used for decision making regarding the suitability of using a given measurement in various applications and for properly defining statistical confidence in derived analysis products.

Steffen Seitz of the National Metrology Institution, Germany, offered the following convenient definition of measurement uncertainty in a September 2020 presentation at the Ocean Best Practices Workshop IV:

“Uncertainty of a measurement is the quantification of the doubt that exists about the quantity value of a measurement, i.e. because of the impossibility to: 1)define all relevant measurement parameters exactly 2) realize all defined parameters exactly.”

Another, similar definition is provided in the International Vocabulary of Metrology (BIPM, 2012).

According to the GUM, the term uncertainty of measurement denotes “a parameter, associated with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the measurand,” i.e., uncertainty of measurement is the quantification of the doubt that exists about the result of any measurement.

Measurement error indicates the difference between a measured quantity value and a reference value. Although in practice, a reference value can be a lab standard used in a calibration measurement, measurement error can be often misunderstood as it is often expressed as the deviation of the measured quantity value from a somewhat obscure “true value” that cannot be quantified due to imperfections in the measurement. In contrast, the concept of measurement uncertainty tries to quantify a range, in which the (unknown) true value is supposed to be within a given level of confidence.

Methods for quantifying uncertainty summarized in the BIPM GUM involve identifying a set of individual sources of uncertainty (typically referred to as uncertainty components), applying a

Appendix A - Summary of Issue May 2024 statistical method to develop an probability distribution for each component, and then fitting and combining to produce an overall value of uncertainty to be associated with given measurement, known as the combined standard uncertainty. Individual uncertainty components are not necessarily independent, so analysis includes quantifying how their correlation can either increase or decrease the uncertainty of the final result, depending on the degree of correlation and the model used to calculate the final result.

CO-OPS Current Methods

CO-OPS has gone to great lengths to disclose all known details associated with measurement error for the sensors employed by real-time field systems. In 2018, CO-OPS formally documented the National Water Level Observation Network (NWLON) Requirements , which detailed minimum data accuracy and uncertainty requirements and supporting documentation. These error estimates can be easily applied to estimate the accuracy of a sensor or measurement system.

CO-OPS invests heavily in the test and evaluation of all new water level sensors and derivation of final measurement system error estimates go far beyond the simple approach of stating vendor provided specifications. Every new make/model of a water level sensor undergoes a broad range of laboratory and field testing prior to transitioning to operational use. Field testing involves data collection across a variety of coastal environment types and is often conducted for durations of 12 months and longer. Additionally, results from controlled lab testing and field testing are used to design and develop laboratory calibration facilities and to develop calibration methods that are specifically targeted toward reducing measurement uncertainty. Furthermore, most CO-OPS measurement system test and evaluation results and data sets are presented at external conferences and published within publicly available domains.

However, as summarized above, CO-OPS published measurement error estimates are different from a quantified uncertainty. They are mainly based on empirical test results that demonstrate the difference between the measurements from a test sensor and some other reference. Although laboratory measurements and associated testing involve a well defined reference, or standard, field observations often involve comparison to a variety of other measurement technologies.

Although significant effort is put forth to ensure references include a variety of measurement technologies, and where available, a well established, well proven reference sensor, summary field test results are actually better defined as statistics that summarize the precision between a test and reference sensor (or multiple references).

In an effort to explore and develop a measurement uncertainty quantification method that follows the latest practices followed across the international oceanographic and meteorological measurement communities [ref], the first step suggested is to list all known contributors to uncertainty for each water level sensor type employed throughout NWLON. Next, it is proposed to seek outside support for applying the probabilistic approaches defined in references X-X to develop a measurement uncertainty quantification for NWLON water level measurements.

Following the exploration and development, assess usefulness and suitability for CO-OPS applications.

CO-OPS Primary Water Level Sensors and Components of Measurement Uncertainty.

CO-OPS has identified a list of known contributors to measurement uncertainty for each of the primary water level sensor types employed by the CO-OPS observing system network. These sources of uncertainty reflect all factors from raw sensor measurement to instantaneous 6 minute water level value. Sampling rate and averaging computation every six minutes varies between different sensor types.

1. Acoustic

Sensor Make/Model : Aquatrak 3003-XCR-4 Transducer and Model 4110 Controller

Features: 2 KHz Pulse signal, at 1 Hz rate. Transmits pulse down sounding tube in protective well. Uses echo time of flight to derive range-to-water surface measurement.

Six minute sampling scheme: Raw sampling rate is 1 Hz; 181, 1 Hz samples are averaged, centered on each tenth of an hour; three standard deviation outlier rejection test applied, the mean and standard deviation are then recalculated and reported along with the number of rejected points.

Sources of Measurement Uncertainty:

● Laboratory calibration (sensor offset determination)

● Field geodetic level survey to nearby bench marks (datum offset measurement)

● Temperature induced expansion/contraction of pvc calibration tube

● Temperature induced acoustic sound speed gradient down PVC sounding tube

● Ice forming inside of sounding tube

● Hydrodynamic response of well/intake in presence of waves and currents

● Fouling/growth/sedimentation near well base and intake, reducing in/out flow rates

2. Bubbler Pressure System

Sensor Make/Model : Paroscientific Digiquartz ® Series 6000 pressure transmitter, Waterlog air compressor pump

Features: Pressure transducer is mounted on surface side, in a tide house or data collection platform enclosure. An air compressor/pump system is used to maintain a constant bubble flow through a submerged orifice and is vented to account for atmospheric pressure.

Six minute sampling scheme: Raw sampling rate is 0.2 Hz (5 second period); 36, 0.2 Hz samples are averaged, centered on each tenth of an hour; three standard deviation outlier rejection test applied, the mean and standard deviation are then recalculated and reported along with the number of rejected points.

Sources of Measurement Uncertainty:

● Laboratory calibration (sensor offset determination)

● Field level survey (datum offset measurement)

● Fouling/growth on subsurface orifice

● Temporal and spatial density gradients

● Sensor drift/offset and impact on instantaneous density calculations

3. Microwave Radar

Make/Model: Xylem\Waterlog H3611 and NOAA Nile

Features: 26 GHz pulse signal, 280 ns pulse period, 0.8 ns pulse with, 1 Hz processed range output. Uses echo time of flight to derive range-to-water surface measurement

Six minute sampling scheme: Raw sampling rate is 1 Hz; 359, 1 Hz samples are averaged, centered on each tenth of an hour; three standard deviation outlier rejection test applied, the mean and standard deviation are then recalculated and reported along with the number of rejected points.

Sources of Measurement Uncertainty:

● Laboratory calibration (sensor offset determination)

● Field geodetic level survey to nearby bench marks (datum offset measurement)

● Obstructions in field of view, floating objects, etc.

● Heavy driving rain and splashing

● Presence of ice

● Large surface gravity waves and breaking waves

4. Float and Shaft Encoder System

Make/Model: BEI Shaft Angle Encoder Model MT40D-X-HSS1024N-64T-XD13-X-S-C14-X-12

Features:

Six minute sampling scheme: Raw sampling rate is 1 Hz; 181, 1 Hz samples are averaged, centered on each tenth of an hour; three standard deviation outlier rejection test applied, the mean and standard deviation are then recalculated and reported along with the number of rejected points.

Sources of Measurement Uncertainty:

● Sensor offset determination - location of zero

● Field geodetic level survey to nearby bench marks (datum offset measurement)

● Float buoyancy variation

● Belt slip/movement from shaft gear

References

1. U.S. CLIVAR Uncertainty Quantification Working Group:

https://usclivar.org/working-groups/ocean-uncertainty-quantification-working-group

2. Bushnell, M., Waldmann, C., Seitz, S., Buckley, E., Tamburri, M.,, Hermes, J., Henslop, E., Lara-Lopez, A., Quality Assurance of Oceanographic Observations: Standards and Guidance Adopted by an International Partnership, Frontiers in Marine Science, v6, 2019, DOI=10.3389/fmars.2019.00706 https://www.frontiersin.org/articles/10.3389/fmars.2019.00706

3. Supplementary Material Bushnell, et al, 2019:

https://www.frontiersin.org/articles/10.3389/fmars.2019.00706/full#supplementary-material

4. Guide to the Expression of Uncertainty in Measurement (GUM) (BIPM, 2008) https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0ef43f-baa5- 11cf-3f85-4dcd86f77bd6

5. CO-OPS Sensor Specifications and Measurement Algorithms:

https://tidesandcurrents.noaa.gov/publications/CO-OPS_Measurement_Spec.pdf

6. National Water Level Observation Network Requirements, NOAA’s National Ocean Service Center for Operational Oceanographic Products and Services (CO-OPS), October 2018.

APPENDIX B – SUMMARY of CO-OPS OBSERVING PROGRAMS

The National Water Level Observation Network (NWLON)

The NWLON is a network of 210 long-term, continuously-operating water-level stations throughout the USA, including its island possessions and territories as well as the Great Lakes.

The NWLON has expanded over time in response to increasing national and local needs.

NWLON stations are the foundation for reference stations for NOAA's vast tide prediction products, and serve as controls in determining tidal datums for all short-term water-level stations.

Technological advancements in sensors, data collection and data communications have enabled routine real-time automated data acquisition using the Geostationary Operational Environmental Satellite (GOES) and iridium satellites. NWLON data-collection platforms are now capable of measuring other oceanographic parameters in addition to water levels, including meteorological parameters. Because of these advancements, the application of NWLON data and products has broadened. For instance, the NWLON is a key part of the NOAA Tsunami Warning System and the NOAA Storm Surge Warning products. NWLON stations have standard configurations of water level sensors (including backup sensors), backup and primary data-collection platforms, solar panel power, GOES satellite transmitter radios, and cellular Internet Protocol (IP) modems.

Sensors are calibrated and vertically referenced to nearby networks of benchmarks. The data continuity, vertical stability and careful referencing of NWLON stations have enabled the data to be used to estimate relative sea-level trends for the Nation.

Physical Oceanographic Real-Time System (PORTS®)

PORTS ® is a decision support tool that improves the safety and efficiency of maritime commerce and coastal resource management. PORTS® collects and disseminates observations of water levels, currents, salinity, bridge air gap and meteorological parameters (e.g., winds, atmospheric pressure, air and water temperatures) that mariners need to navigate safely. The objectives of the PORTS® program are to promote navigation safety, improve the efficiency of U.S. ports and harbors, and ensure the protection of coastal marine resources. PORTS® promotes safer and more efficient marine navigation by (1) providing data in near real-time to inform users of actual conditions, and (2) integrating a variety of environmental parameters on one website, eliminating the need to go to multiple sources for different observation types. The ability to get a comprehensive and easily-understood view of the operating environment is particularly critical on the bridge of a vessel, where many issues can require the attention of the crew at the same time.

Since its initial deployment in 1991, PORTS® has grown exponentially, incorporated new sensor technologies needed by the maritime community, and built a solid reputation as a highly trusted source for real-time oceanographic and meteorological information to support safe and efficient maritime commerce.

Appendix B - Summary of CO-OPS Observing Systems Mayl 2024

Vertical Datum Transformation (VDatum) Program

VDatum is a free software tool being developed jointly within the National Ocean Service. VDatum is designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums - allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels. The ability to properly reference data to multiple vertical datums is critical to a variety of applications in the coastal zone. A National VDatum complements NOS’s work developing a national bathymetric database, a National Spatial Reference System (NSRS), and a national tidal datum reference system, which can be combined with the topographic data to provide seamless data products across the land-water interface. These products will also enable a consistent, accurate national shoreline to be defined relative to tidal and geodetic datums.

Some other applications that benefit from VDatum include inundation modeling (storm surge, tsunami, and sea level rise impacts), ecosystem modeling, and coastal zone management. The vertical datums incorporated into VDatum are selected so as to accommodate the wide variety of bathymetric and topographic data sources that could potentially be used as input to the transformation software. Vertical datums can be classified as tidal datums (tidally-derived surface), orthometric datums, or 3D ellipsoidal datums. Examples of the types of data that are referenced to these categories of datums include bathymetry that is usually referenced to a tidal datum, topographic data that is often surveyed relative to an orthometric datum such as the North American Vertical Datum of 1988 (NAVD88), and LIDAR data that is referenced to an ellipsoidal datum.

Appendix B - Summary of CO-OPS Observing Systems Mayl 2024