Center For Operational Oceanographic Products And Services Research Articles

Recent Development and Field Test of CO-OPS' Real-Time, Shallow Water CURrents BuoY (CURBY)

Abstract The National Oceanic and Atmospheric Administration (NOAA) National Ocean Service Center for Operational Oceanographic Products and Services (CO-OPS) manages the National Current Observation Program (NCOP) and Physical Oceanographic Real-Time Systems (PORTS®). These programs provide tide and current predictions, as well as real-time current and meteorological information. Outdated current predictions, navigational support requirements, and incident response scenarios (e.g., oil spills, vessel accidents) have highlighted CO-OPS' need for a rapidly deployable system that provides near-surface current and meteorological observations. To address this, CO-OPS designed, developed, and tested a real-time system based on a surface buoy platform, hereinafter referred as CURrents BuoY (CURBY). This paper provides an overview of the system design, field test results, operational applications, and future plans.In 2018, CO-OPS completed the build, integration, and testing of the first prototype CURBY. A successful field test was completed during 2018 in the Chesapeake Bay, and the first operational deployment followed shortly on the Delaware River in 2019. Resulting measurements were used to improve tidal current predictions and to plan for a 2021 regional survey. Initial success with tidal current survey operations led to design enhancement and wider use. During 2020‐2021, CO-OPS partnered with the NOAA Office of Response and Restoration to build two new CURBYs to support emergency response applications in the Gulf of Mexico region. During 2022, two CURBY systems were deployed in the Columbia River, Oregon, to support additional NCOP operations. Future plans include establishing a long-term CURBY system for Kings Bay, Georgia, PORTS®.

Wave Measurements from Radar Tide Gauges.

Currently the NOAA Center for Operational Oceanographic Products and Services (CO-OPS) is transitioning the primary water level sensor at most NWLON stations from an acoustic ranging system to microwave radars. With no stilling well and higher resolution of the open sea surface, microwave radars have the potential to provide real-time wave measurements at NWLON sites. Radar sensors at tide stations may offer a low cost, convenient way to increase nearshore wave observational coverage throughout the U.S. to support navigational safety and ocean research applications. Here we present the results of a field study comparing wave height measurements from four radar water level sensors with two different signal types (pulse and continuous wave swept frequency modulation-CWFM). A nearby bottom acoustic wave and current sensor is used as a reference. An overview of field setup and sensors will be presented, along with an analysis of performance capabilities of each radar sensor. The study includes results from two successive field tests. In the first, we examine the performance from a pulse microwave radar (WaterLOG H-3611) and two CWFM (Miros SM-94 and Miros SM-140). While both types of radars tracked significant wave height well over the test period, the pulse radar had less success resolving high frequency wind wave energy and showed a high level of noise towards the low frequency end of the spectrum. The pulse WaterLOG radar limitations were most apparent during times of high winds and locally developing seas. The CWFM radars demonstrated greater capability to resolve those higher frequency energies while avoiding low frequency noise. The initial field test results motivated a second field test, focused on the comparison of wave height measurements from two pulse radar water level sensors, the WaterLOG H3611 and the Endress and Hauser Micropilot FMR240. Significant wave height measurements from both radar water level sensors compared well to reference AWAC measurements over the test period, but once again the WaterLOG radar did not adequately resolve wind wave energy in high frequency bands and showed noise towards the low frequency end of the spectrum. The E+H radar demonstrated greater capability to measure waves.

Wave Measurements From Radar Tide Gauges

Currently the NOAA Center for Operational Oceanographic Products and Services (CO-OPS) is transitioning the primary water level sensor at most NWLON stations from an acoustic ranging system to microwave radars. With no stilling well and higher resolution of the open sea surface, microwave radars have the potential to provide real-time wave measurements at NWLON sites. Radar sensors at tide stations may offer a low cost, convenient way to increase nearshore wave observational coverage throughout the U.S. to support navigational safety and ocean research applications. Here we present the results of a field study comparing wave height measurements from four radar water level sensors with two different signal types (pulse and continuous wave swept frequency modulation-CWFM). A nearby bottom acoustic wave and current sensor is used as a reference. An overview of field setup and sensors will be presented, along with an analysis of performance capabilities of each radar sensor. The study includes results from two successive field tests. In the first, we examine the performance from a pulse microwave radar (WaterLOG H-3611) and two CWFM (Miros SM-94 and Miros SM-140). While both types of radars tracked significant wave height well over the test period, the pulse radar had less success resolving high frequency wind wave energy and showed a high level of noise towards the low frequency end of the spectrum. The pulse WaterLOG radar limitations were most apparent during times of high winds and locally developing seas. The CWFM radars demonstrated greater capability to resolve those higher frequency energies while avoiding low frequency noise. The initial field test results motivated a second field test, focused on the comparison of wave height measurements from two pulse radar water level sensors, the WaterLOG H3611 and the Endress and Hauser Micropilot FMR240. Significant wave height measurements from both radar water level sensors compared well to reference AWAC measurements over the test period, but once again the WaterLOG radar did not adequately resolve wind wave energy in high frequency bands and showed noise towards the low frequency end of the spectrum. The E+H radar demonstrated greater capability to measure waves.

Tidal Datums with Spatially Varying Uncertainty in North-East Gulf of Mexico for VDatum Application

We conducted a VDatum-spatially varying uncertainty study for the North-East Gulf of Mexico. The newly developed tide model incorporated the latest available National Ocean Service (NOS) bathymetry survey data and National Geodetic Survey (NGS) shoreline data, and the datum products reflected the updated tidal datum data from the Center for Operational Oceanographic Products and Services (CO-OPS). A gridding technique based on the wavelength of long waves in the deep ocean was applied to improve model efficiency. In this study, we highlight the creation of the tidal datum products and associated spatially varying uncertainty, which was developed by blending the model results, observations, and measurement errors together using a spatially varying uncertainty method based on a variational approach. The study found that model errors, measurement errors, and lack of observations can contribute to large uncertainty in the tidal datum products. The need for high quality bathymetry data in coastal areas is essential for reducing model error. As for the large uncertainty due to lack of observations or large measurement error, this can be improved by placement of new observations with high precision. Compared to a single uncertainty value, the spatially varying uncertainty provides more accurate representation of the uncertainty for the tidal datum products in VDatum. The uncertainty results will be used to help with decision-making on placement of new tide gauges to further reduce the uncertainty in the VDatum products.

Reducing Wave-Induced Microwave Water-Level Measurement Error with a Least Squares–Designed Digital Filter*

Abstract A microwave water-level sensor, the Design Analysis model H-3611i, will soon enter service at tide stations operated by the National Oceanic and Atmospheric Administration’s Center for Operational Oceanographic Products and Services (CO-OPS) as part of the National Water Level Observation Network. CO-OPS tests include a multisensor deployment at the U.S. Army Corps of Engineers Field Research Facility at Duck, North Carolina, to evaluate microwave water-level measurement error over a wide range of Atlantic Ocean sea states. In situ precision and accuracy of processed (6-min average) water level is found to depend on sea state in addition to data processing methods and sensor operating mode. Estimates over selected 6-h measurement periods show that a degree-two polynomial successfully models the increase in sensor standard error with increasing zero-moment (Hm0) wave height but with differences in rate of error increase dependent on the application of a prefilter and choice of sensor operating mode. Prefiltering of 1-Hz “fast mode” sensor output to remove variance at selected wind-wave frequencies can reduce standard error during extreme conditions (Hm0 ≈ 3 m) from approximately ±3 cm without prefiltering to about ±1 cm using a least squares–designed (LSD) digital filter with a 60-s cutoff period. When wave heights are elevated, skewed non-Gaussian distributions develop within the 1-Hz (360 s) sample domain wherein a 3σ outlier elimination process applied without prefiltering can introduce a negative bias of up to 5 cm in individual 6-min water-level averages.

NOAA’s Nested Northern Gulf of Mexico Operational Forecast Systems Development

The NOAA National Ocean Service’s (NOS) Northern Gulf of Mexico Operational Forecast System (NGOFS) became operational in March 2012. Implemented with the Finite Volume Coastal Ocean Model (FVCOM) as its core three-dimensional oceanographic circulation model, NGOFS produces a real-time nowcast (−6 h to zero) and six-hourly, two-day forecast guidance for water levels and three-dimensional currents, water temperature and salinity over the northern Gulf of Mexico continental shelf. Designed as a regional scale prediction system, NGOFS lacks sufficient spatial coverage and/or resolution to fully resolve hydrodynamic features in critical seaports and estuaries. To overcome this shortcoming and better support the needs of marine navigation, emergency response, and environmental management, two FVCOM-based, high-resolution, estuary-scale nested forecast modeling systems, namely the Northwest and Northeast Gulf of Mexico Operational Forecast Systems (NWGOFS and NEGOFS), have been developed through one-way nesting in NGOFS. Using the atmospheric forecast guidance from the NOAA (National Oceanic and Atmospheric Administration)/NWS (National Weather Services)’ North American Mesoscale (NAM) Forecast System, US Geological Survey (USGS) river discharge observations, and the NGOFS water level, current, water temperature and salinity as the surface, river, and open ocean boundary forcing, respectively, a six-month model hindcast for the period October 2010–March 2011 has been conducted. Modeled water levels, currents, salinity and water temperature are compared with observations using the NOS standard skill assessment software. Skill assessment scores indicated that NWGOFS and NEGOFS demonstrate improvement over NGOFS. The NWGOFS and NEGOFS are under real-time nowcast/forecast test and evaluation by NOS’s Center for Operational Oceanographic Products and Services (CO-OPS). The forecast systems are scheduled to be implemented operational on NOAA Weather & Climate Operational Supercomputing System (WCOSS) in June 2014.

Interoperability of SeaSondes and Wellen Radars in Mapping Radial Surface Currents

Abstract A dual-station high-frequency (HF) Wellen Radar (WERA) transmitting at 16 MHz has observed near-real-time surface currents over an approximate range of 100 km across the Florida Straits since July 2004. During a 10-day period in April 2005 (15–25 April), a pair of 12.6-MHz SeaSondes (SS) were deployed south of the WERAs sites by NOAA's Center for Operational Oceanographic Products and Services (CO-OPS). The resulting SS grid overlapped the southern portion of the WERA domain. During the same period of time, a bottom-mounted acoustic Doppler current profiler (ADCP) acquired subsurface current measurements within these HF radar grids starting at 14 m below the surface in water of 86-m depth. The interoperability of beam-forming (WERA) and direction-finding (SS) HF radar technologies was examined. Comparisons of radial and vector currents for an 8-day concurrent time series suggested good agreement in current direction over both domains, where the surface currents' magnitudes were a maximum of 1.2 m s−1. In the core of the radar domains consisting of 108 cells, hourly vector currents were obtained by combining WERA and SS radials. Generally, this can be done in a relatively straightforward manner, considering the geometric dilution of precision (GDOP). A second key issue is downscaling the SS measurements from a 3-km grid to a 1.1-km grid to match the WERA output. This enhanced grid spacing is important along the western flank of the Florida Current, where energetic, small-scale surface features have been observed.

Error Quantification for Hurricane Storm Surge Simulations along the Coasts of North Carolina, South Carolina, and Georgia

This paper describes the development of a procedure to quantify errors in hurricane storm surge simulations using a decoupled wind-surge model. The state-of-the-art storm surge simulation program, advanced circulation (ADCIRC), and the Georgiou's wind-field model were used to simulate the storm surge heights associated with 169 historical hurricanes from 1922 to 2011 along the coasts of North Carolina, South Carolina, and Georgia. The storm surge modeling errors were quantified by comparing the measured with the simulated annual maximum surge heights at nine water-level observation stations maintained by the Center for Operational Oceanographic Products and Services (CO-OPS). The simulated surge heights consisted of both systematic and random errors. A power equation was used to adjust the systematic errors in the simulated storm surge heights. The random errors, which are defined as the ratio of the measured to the systematic error-adjusted simulated annual maximum surge heights, were found to follow a lognormal distribution. To confirm the validity of the error quantification methodology, the simulated annual, 5-year, and 10-year maximum surge heights were adjusted using the modeling errors quantified for each of the nine CO-OPS water stations and compared with the actual storm surge observations. Good agreements were observed between the actual measurements and the error-adjusted surge elevations. The proposed methodology for quantifying modeling errors can be applied to adjust for storm surge predictions of longer return periods and can be used to develop design surge hazard maps.

Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System

The NOAA Great Lakes Operational Forecast System (GLOFS) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water temperature and currents, and two-dimensional forecasts of water levels of the Great Lakes. This system, originally called the Great Lakes forecasting system (GLFS), was developed at The Ohio State University and NOAA’s Great Lakes Environmental Research Laboratory (GLERL) in 1989. In 1996, a workstation version of the GLFS was ported to GLERL to generate semi-operational nowcasts and forecasts daily. In 2004, GLFS went through rigorous skill assessment and was transitioned to the National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS since September 30, 2005. Hindcast, nowcast, and forecast evaluations using the NOS-developed skill assessment software tool indicated both surface water levels and temperature predictions passed the NOS specified criteria at a majority of the validation locations with relatively low root mean square error (4–8 cm for water levels and 0.5 to 1°C for surface water temperatures). The difficulty of accurately simulating seiches generated by storms (in particular in shallow lakes like Lake Erie) remains a major source of error in water level prediction and should be addressed in future improvements of the forecast system.

Sea ice surface temperature product from MODIS

Global sea ice products are produced from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) onboard both the Terra and Aqua satellites. Daily sea ice extent and ice surface temperature (IST) products are available at 1- and 4-km resolution. Validation activities during the "cold period" (when meltwater is generally not present) in the Northern Hemisphere, defined here as October through May, have been undertaken to assess the accuracy of the 1-km resolution MODIS IST algorithm and product. Validation was also done at the South Pole station in Antarctica. In the Arctic Ocean, near-surface air temperatures from the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) Alaska tide stations and from drifting buoys from the North Pole Environmental Observatory (NPEO) buoy program were compared with MODIS-derived ISTs. Using the standard MODIS sea ice product, which utilizes the MODIS cloud mask, results show a bias (mean error) of -2.1 K and a root mean square (RMS) error of 3.7 K. The negative bias means that the satellite retrieval is less than the air temperature. With the bias removed, the RMS error is 3.0 K. When additional visual cloud screening is performed to eliminate MODIS pixels thought to be contaminated by fog, results improved, with a subset of the larger dataset showing a bias of -0.9 K and an RMS error of 1.6 K. Uncertainties would be reduced in the Arctic Ocean dataset if the skin temperature of the sea ice were reported instead of the near-surface air temperatures. With the bias removed, the RMS error for the Arctic Ocean dataset is 1.3 K. Results from the South Pole station in Antarctica show that under clear skies as determined using lidar measurements, the MODIS ISTs are also very close to those of the near-surface air temperatures with a bias of -1.2 K and an RMS error of 1.7 K. With the bias removed, the RMS error for the South Pole dataset is 1.2 K. Thus, the accuracy (RMS error) of the IST measurement is 1.2-1.3 K. It is not possible to obtain an accurate IST from MODIS in the presence of even very thin clouds or fog, and this is the main limitation of the MODIS ice surface temperature product. MODIS sea ice products may be ordered from the National Snow and Ice Data Center in Boulder, CO.