National Data Buoy Center Buoy Research Articles

Tropical cyclone wave data assimilation impact on air-ocean-wave coupled Hurricane Harvey (2017) forecast

The impact of surface wave assimilation on hurricane track and intensity forecasts has been investigated using a fully coupled air-ocean-wave tropical cyclone data assimilation and forecast modeling system. A new 3DVAR wave assimilation method in the Navy Coupled Ocean Data Assimilation system (NCODA) maps the 1D wave energy spectra from buoys to 2D directional wave energy spectra using the maximum likelihood method (MLM) and corrects the wave model forecast component directional wave energy spectra. The Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclone Prediction (COAMPS-TC) is used to conduct three Hurricane Harvey (2017) air-ocean-wave coupled data assimilation and forecasting experiments with and without the wave data assimilation. Hurricane Harvey traversed through the Western Gulf of Mexico from 24 August to 1 September, 2017 and made landfall in the Texas and Louisiana coast. Validation of track, maximum wind speed, significant wave height, and mean absolute wave periods show wave assimilation of the 1D wave energy spectra from 13 National Data Buoy Center (NDBC) buoys reduced the forecast errors of these parameters compared to experiments without the wave assimilation. In spite of this positive outcome, the wave assimilation is unable to reduce Harvey’s 0-120 h forecast mean wave direction errors and correlation compared to the NDBC buoy time series

Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models.

A Mooring Development and Implementation Case Study of a Government/Private Partnership

Abstract In today's world where there is an increasing need to monitor and understand our changing oceans, under flat or shrinking budgets, it is challenging for a single organization to tackle problems of this scale and magnitude alone. Engineers and scientists from the National Oceantic and Atmospheric Administration's National Data Buoy Center (NDBC) and the Monterey Bay Aquarium Research Institute (MBARI) began a collaboration with this challenge in mind. The collaboration took advantage of each organization's strengths: MBARI has the capability to rapidly develop new instrument systems, and NDBC has the infrastructure on which to test their capabilities in the field. Here, we present development efforts that led to the deployment of a small partial pressure of carbon dioxide (pCO2) sensor system developed by MBARI on an NDBC buoy. The overarching goal was to demonstrate the utility of NDBC's new Auxiliary (Aux) module and determine the ease of adding third-party instrumentation to NDBC buoys. The Aux module is part of NDBC's latest generation of weather buoy data acquisition and reporting system, which provides more modularity and flexibility over previous systems. Given the large number of NDBC buoys currently in service, this capability opens up the possibility of a dense array of low-cost sensors to complement more highly specialized expensive mooring systems that are sparsely distributed. The partnership process led to the successful deployment of the pCO2 system on NDBC buoy 46013 off Bodega Bay, California, about 48 nautical miles northwest of San Francisco, for over a year of unattended operation. The technical and scientific results are described.

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

This paper introduces a method for estimating a series of sea state parameters from satellite-borne synthetic aperture radar (SAR). The method was realized in a near real time (NRT) application which allows for the processing of data from different satellites and modes. The algorithm estimates the total significant wave height Hs, dominant and secondary swell and windsea wave heights, first, and second moment wave periods, the mean wave period and the period of wind sea. The algorithm was applied to the Sentinel-1 (S1) C-band Interferometric Wide Swath Mode (IW), Extra Wide (EW) and Wave Mode (WV) Level-1 (L1) products and also extended to X-band TerraSAR-X (TS-X) StripMap (SM) products. The scenes are processed in raster and result in continuous sea state fields, with the exception of S1 WV, where averaged values for sea state parameters for along-orbit imagettes of 20 km × 20 km are presented.The developed empirical algorithm consists of two parts: a first CWAVE_EX (extended CWAVE) part, based on a linear regression approach, and a subsequent machine learning part using the support vector machine (SVM) technique. A series of new data preparation steps (i.e. filtering, denoising) and new features estimated from SAR images are also introduced. The algorithm was tuned and validated using two independent global wave model hindcasts, WaveWatch-3 and MFWAM as well as National Data Buoy Center (NDBC) measurements. The achieved root mean squared errors (RMSE) for CWAVE_EX for the total Hs are 0.60 m for low-resolution modes S1 IW (10 m pixel) and EW (40 m pixel) and 0.35 m for S1 WV and TS-X SM (pixel spacing ca.1–4 m) in comparison to model predictions. The RMSEs of the retrieved wave periods are in the range of 0.45–0.90 s for all of the satellites and models considered. Similarly, the dominant and secondary swell, and wind sea wave height RMSEs are in the range of 0.35–0.80 m. The SVM postprocessing improves the accuracy of the initial results of CWAVE_EX for Hs and reaches an RMSE of 0.25 m for S1 WV. Comparisons to 64 NDBC buoys, collocated at distances shorter than 50 km to S1 WV imagettes worldwide, result in an RMSE of 0.41 m. All results and the methods presented are novel in terms of the accuracy achieved, combining the classical approach with machine learning techniques, and performing an automatic NRT processing of multiparametric sea state fields from L1 data with automatic switching for different satellites and modes.The complete archive of S1 WV L1 Single Look Complex products from December 2014 until February 2021 was processed to create a sea state parameter database and validated using model hindcast and buoy measurements. The derived parameters are available to the public within the scope of the European Space Agency's Climate Change Initiative.

Validation of Wave Spectral Partitions From SWIM Instrument On-Board CFOSAT Against In Situ Data

The surface waves investigation and monitoring (SWIM) instrument onboard the China–France Oceanography Satellite (CFOSAT) can retrieve directional wave spectra with a wavelength range of 70–500 m. This study aims to validate the partitioned integrated wave parameters (PIWPs) from SWIM, including partitioned significant wave height (PSWH), partitioned peak wave period (PPWP), and partitioned peak wave direction (PPWD), against those from National Data Buoy Center (NDBC) buoys. With quasi-simultaneous spectra from two NDBC buoys 13 km away from each other near Hawaii, the methods of comparing PIWPs from two sets of spectra were discussed first. After cross-assigning partitions according to the spectral distance, it is found that wrong cross-assignments lead to many outliers strongly impacting the estimate of error metrics. Three methods, namely comparing only the best-matched partition, changing the threshold of spectral distance during cross-assignment, and maximum likelihood estimation of root-mean-square error (RMSE) of PIWPs, were used to reduce the impact of potential wrong cross-assignments. Using these methods, the SWIM PIWPs were validated against NDBC buoys. The results show that SWIM performs well at finding the spectral peaks of different partitions with the RMSE of PPWPs and PPWDs of 0.9 s and 20°, respectively, which can be a useful complement for other wave observations. However, the accuracy of PSWH from SWIM is not that good at this stage, probably because the high noise level in the spectra impacts the result of the partitioning algorithm. Further improvement is needed to obtain better PSWH information.

Accuracy Evaluation of CFOSAT SWIM L2 Products Based on NDBC Buoy and Jason-3 Altimeter Data

Chinese-French Oceanography Satellite (CFOSAT), the first satellite which can observe global ocean wave and wind synchronously, was successfully launched On 29 October 2018. The CFOSAT carries SWIM that can observe ocean wave on a global scale. Based on National Data Buoy Center (NDBC) buoys and Jason-3 altimeter data, this study evaluated the accuracy of L2 level products of CFOSAT SWIM from August 2019 to September 2020. The results show that the accuracy of the nadir Significant Wave Height (SWH) data of the SWIM wave spectrometer is good. Compared with the data of the NDBC buoys and Jason-3 altimeter, the RMSE of the nadir box SWH were 0.39 and 0.21 m, respectively. The variation trend of SWH were first increasing and then decreasing with the increasing of the wave height. The precision of off-nadir wave spectrum SWH is not better than nadir box SWH data. Accuracy was evaluated for off-nadir data from August 2019 to June 2020 and after June 2020, respectively. After linear regression correction, the accuracy of off-nadir wave spectrum SWH was improved. The data accuracy evaluation and comparison of different time period showed that the off-nadir wave spectrum SWH accuracy was improved after the data version was updated in June 2020, especially for 6° and 8° wave spectrum. The precision of off-nadir wave spectrum SWH decreases with the increasing of wave height. The accuracy of the dominant wave direction of each wave spectrum is also not very good, and the accuracy of the dominant wave direction of 10° wave spectrum is slightly better than the others. In general, the accuracy of SWIM nadir beam SWH data reaches the high data accuracy of traditional altimeter, while the accuracy of off-nadir wave spectrum SWH is less than that of nadir beam SWH data. The off-nadir SWH data accuracy after June 2020 has been greatly improved.

Validation and Calibration of Nadir SWH Products From CFOSAT and HY‐2B With Satellites and In Situ Observations

AbstractThe China France Oceanography Satellite (CFOSAT) and Haiyang‐2B (HY‐2B) satellites were successively launched in China on the 29th and 25th of October 2018, respectively. HY‐2 B carries the second Chinese radar altimeter along with three other microwave sensors, whereas CFOSAT contains the first Surface Waves Investigation and Monitoring (SWIM) instrument and a fan beam rotating scatterometer. As missions for measuring the dynamic marine environment, both satellites can measure the nadir significant wave height (SWH). For data obtained from a newly launched satellite to be utilized with confidence, the quality of SWH from the two satellites requires validation. In this study, the HY‐2B altimeter and CFOSAT nadir SWHs have been validated against the National Data Buoy Center (NDBC) buoys and the Jason‐3 altimeter SWH data, respectively, which resulted in CFOSAT nadir SWH having the best accuracy and HY‐2B having the best precision. The SWHs of the two missions are also calibrated by Jason‐3 and NDBC buoys. Following calibration, the root mean square error (RMSE) of CFOSAT and HY‐2B are 0.21 and 0.27 m, respectively, when compared to Jason‐3, and 0.23 and 0.30 m, respectively, compared to the buoys. Our results show that the two missions can provide good‐quality SWH and can be relied upon as a new data resource of global SWH.

Remote Cross-Calibration of Wave Buoys Based on Significant Wave Height Observations of Altimeters in the Northern Hemisphere

Consistency between national wave buoy networks is extremely important for wave climate studies and verification of global operational wave forecasting systems; however, it is insufficiently investigated. The validation of altimeter significant wave heights (SWHs) with the wave buoy networks of China, Europe and the National Data Buoy Center (NDBC) show significant divergence in assessments. This reveals a negative bias and larger root mean square error and scatter index from the Chinese buoy network than from the European and NDBC buoy networks. A remote cross-calibration method is presented using the collocations between altimeters and buoys to match the buoy observations from different networks. The Chinese buoys are found to yield a negative bias of −0.127 m compared to European/NDBC buoy networks. The cross-calibration equation is achieved by regression of the SWHs between the Chinese and European/NDBC buoy networks. The use of this remote cross-calibration significantly reduces the inconsistency between the Chinese and European/NDBC buoys in the validation of SWH from altimeter HY2B.

Global Calibration and Error Estimation of Altimeter, Scatterometer, and Radiometer Wind Speed Using Triple Collocation

The accuracy of wind speed measurements is important in many applications. In the present work, error standard deviations of wind speed measured by satellites and National Data Buoy Center (NDBC) buoys were estimated using triple collocation. The satellites included six altimeters, three scatterometers, and four radiometers. The six altimeters were TOPEX, ERS-2, JASON-1, ENVISAT, JASON-2, and CRYOSAT-2, whilst the three scatterometers were QUIKSCAT, METOP-A, and METOP-B and the four radiometers included SSMI-F15, AMSR-2, WINDSAT, and GMI. Hence, a total of 14 platform measurements, including NDBC buoy data, were used and the error standard deviations of each estimated. It was found that altimeters have the smallest error standard deviations for wind speed measurements followed by scatterometers and then radiometers. NDBC buoys have the largest error standard deviation. Since triple collocation can simultaneously perform error estimation as well as calibration for a given reference, this method enables us to perform intercalibration between platform measurements including NDBC buoy. In addition, the calibration relations obtained from triple collocation were compared with the calibrations obtained from the widely used reduced major axis (RMA) regression approach. This method, to some extent, can accommodate measurements in which both platforms contain errors. The results showed that calibration relations obtained from RMA and triple collocation are very similar, as indicated by statistical parameters such as RMSE, correlation coefficient, scatter index, and bias.

Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

AbstractGlobal ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

Validation of Sentinel-3A/3B Satellite Altimetry Wave Heights with Buoy and Jason-3 Data.

The validation of significant wave height (SWH) data measured by the Sentinel-3A/3B SAR Altimeter (SRAL) is essential for the application of the data in ocean wave monitoring, forecasting and wave climate studies. Sentinel-3A/3B SWH data are validated by comparisons with U. S. National Data Buoy Center (NDBC) buoys, using a spatial scale of 25 km and a temporal scale of 30 min, and with Jason-3 data at their crossovers, using a time difference of less than 30 min. The comparisons with NDBC buoy data show that the root-mean-square error (RMSE) of Sentinel-3A SWH is 0.30 m, and that of Sentinel-3B is no more than 0.31 m. The pseudo-Low-Resolution Mode (PLRM) SWH is slightly better than that of the Synthetic Aperture Radar (SAR) mode. The statistical analysis of Sentinel-3A/3B SWH in the bin of 0.5 m wave height shows that the accuracy of Sentinel-3A/3B SWH data decreases with increasing wave height. The analysis of the monthly biases and RMSEs of Sentinel-3A SWH shows that Sentinel-3A SWH are stable and have a slight upward trend with time. The comparisons with Jason-3 data show that SWH of Sentinel-3A and Jason-3 are consistent in the global ocean. Finally, the piecewise calibration functions are given for the calibration of Sentinel-3A/3B SWH. The results of the study show that Sentinel-3A/3B SWH data have high accuracy and remain stable.

Calibration of HY-2A satellite significant wave heights with in situ observation

Significant wave height (SWH) can be computed from the returning waveform of radar altimeter, this parameter is only raw estimates if it does not calibrate. But accurate calibration is important for all applications, especially for climate studies. HY-2a altimeter has been operational since April 2012 and its products are available to the scientific community. In this work, SWH data from HY-2A altimeters are calibrated against in situ buoy data from the National Data Buoy Center (NDBC), Distinguished from previous calibration studies which generally regarded buoy data as “truth”, the work of calibration for HY-2A altimeter wave data against in situ buoys was applied a more sophisticated statistical technique—the total least squares (TLS) method which can take into account errors in both variables. We present calibration results for HY-2A radar altimeter measurement of wave height against NDBC buoys. In addition, cross-calibration for HY-2A and Jason-2 wave data are talked over and the result is given.

A geometrical optics model based on the non-Gaussian probability density distribution of sea surface slopes for wind speed retrieval at low incidence angles

ABSTRACTIn this study, a large amount of data from precipitation radar (PR) and National Data Buoy Center (NDBC) buoys are collocated for the development and validation of a Geometrical Optics Model, in order to retrieve wind speed at small incidence angles. The omni-directional model is developed based on the combination of the quasi-specular scattering theory and non-Gaussian probability density distribution of ocean surface slope, and can be applied at incidence angles as high as 15°. There are four parameters included in the proposed model: the effective Fresnel reflection coefficient, the mean square slope, and the two coefficients associated with the kurtosis of the sea surface slope distribution. Using one half of the collocated data, the dependence of the four parameters on the in situ wind speed is acquired. The results show that the effective Fresnel reflection coefficient has a decrease relative to that obtained in previous studies. We combine the proposed model with the maximum-likelihood estimation (MLE) technique to retrieve the ocean surface wind speed at the 10 m height. The retrieved wind speeds are then validated against those measured by the NDBC buoys. The comparison shows that the root mean square error (RMSE) and bias between the model retrievals and buoy observations are 1.54 m s–1 and 0.1 m s–1, respectively, revealing high agreements in the wind speed estimations. The results of this study indicate that the proposed model and the PR measurements at low incidence angles can provide reasonably accurate estimates of the surface wind speeds within the range of 0–20 m s–1.

First six months quality assessment of HY-2A SCAT wind products using in situ measurements

The first Chinese microwave ocean environment satellite HY-2A, carrying a Ku-band scatteromenter (SCAT), was successfully launched in August 2011. The first quality assessment of HY-2A SCAT wind products is presented through the comparison of the first 6 months operationally released SCAT products with in situ data. The in situ winds from the National Data Buoy Center (NDBC) buoys, R/V Polarstern, Aurora Australis, Roger Revelle and PY30-1 oil platform, were converted to the 10 m equivalent neutral winds. The temporal and spatial differences between the HY-2A SCAT and the in situ observations were limited to less than 5 min and 12.5 km. For HY-2A SCAT wind speed products, the comparison and analysis using the NDBC buoys yield a bias of −0.49 m/s, a root mean square error (RMSE) of 1.3 m/s and an increase negative bias with increasing wind speed observation above 3m/s. Although less accurate of HY-2A SCAT wind direction at low winds, the RMSE of 19.19° with a bias of 0.92° is found for wind speeds higher than 3 m/s. These results are found consistent with those from R/Vs and oil platform comparisons. Moreover, the NDBC buoy comparison results also suggest that the accuracy of HY-2A SCAT winds is consistent over the first half year of 2012. The encouraging assessment results over the first 6 months show that wind products from HY-2A SCAT will be useful for scientific community.

WAVE PREDICTIONS AT THE SITE OF A WAVE ENERGY CONVERSION ARRAY

The SWAN spectral wave model was applied to a domain along the coast of Oregon State, USA with the purpose of establishing a better understanding of wave conditions at a location which has been proposed for the installation of an array of Wave Energy Converters (WECs). The model uses the directional spectrum measured at a nearby NDBC (National Data Buoy Center) buoy as input and in situ measurements of waves at the proposed WEC location for model validation. It was found that wind -wave generation and bottom friction were not significant over the domain between the NDBC buoy and the WEC site (domain size was 30km in the cross-shore and 230km in the along-shore directions). The predicted wave heights at the WEC location compared favorably to the in situ data wave heights with rms error of ~10 percent. It is suggested that some of this error may arise from the offshore boundary condition where the waves are assumed to be along-shore uniform. Future work is proposed to address this issue. Future work will also include modeling in two other domains, one very small domain to resolve individual WECs and a domain between the WECs and the shore to assess the impact of WECs on the coastline.

SAR observation and model tracking of an oil spill event in coastal waters

Oil spills are a major contributor to marine pollution. The objective of this work is to simulate the oil spill trajectory of oil released from a pipeline leaking in the Gulf of Mexico with the GNOME (General NOAA Operational Modeling Environment) model. The model was developed by NOAA (National Oceanic and Atmospheric Administration) to investigate the effects of different pollutants and environmental conditions on trajectory results. Also, a Texture-Classifying Neural Network Algorithm (TCNNA) was used to delineate ocean oil slicks from synthetic aperture radar (SAR) observations. During the simulation, ocean currents from NCOM (Navy Coastal Ocean Model) outputs and surface wind data measured by an NDBC (National Data Buoy Center) buoy are used to drive the GNOME model. The results show good agreement between the simulated trajectory of the oil spill and synchronous observations from the European ENVISAT ASAR (Advanced Synthetic Aperture Radar) and the Japanese ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array L-band Synthetic Aperture Radar) images. Based on experience with past marine oil spills, about 63.0% of the oil will float and 18.5% of the oil will evaporate and disperse. In addition, the effects from uncertainty of ocean currents and the diffusion coefficient on the trajectory results are also studied.

Validation of Jason-1 and Envisat Remotely Sensed Wave Heights

Abstract Satellite altimetry provides an immensely valuable source of operational significant wave height (Hs) data. Currently, altimeters on board Jason-1 and Envisat provide global Hs observations, available within 3–5 h of real time. In this work, Hs data from these altimeters are validated against in situ buoy data from the National Data Buoy Center (NDBC) and Marine Environmental Data Service (MEDS) buoy networks. Data cover a period of three years for Envisat and more than four years for Jason-1. Collocation criteria of 50 km and 30 min yield 3452 and 2157 collocations for Jason-1 and Envisat, respectively. Jason-1 is found to be in no need of correction, performing well throughout the range of wave heights, although it is notably noisier than Envisat. An overall RMS difference between Jason-1 and buoy data of 0.227 m is found. Envisat has a tendency to overestimate low Hs and underestimate high Hs. A linear correction reduces the RMS difference by 7%, from 0.219 to 0.203 m. In addition to wave height–dependent biases in the altimeter Hs estimate, a wave state–dependent bias is also identified, with steep (smooth) waves producing a negative (positive) bias relative to buoys. A systematic difference in the Hs being reported by MEDS and NDBC buoy networks is also noted. Using the altimeter data as a common reference, it is estimated that MEDS buoys are underestimating Hs relative to NDBC buoys by about 10%.

Daily inter-annual simulations of SST and MLD using atmospherically forced OGCMs: Model evaluation in comparison to buoy time series

A systematic methodology for model–data comparisons of sea surface temperature (SST) and mixed layer depth (MLD) is presented using inter-annual simulations from an ocean general circulation model (OGCM), the Naval Research Laboratory (NRL) Layered Ocean Model (NLOM), over 1980–1998. The model–data comparisons performed here are applicable to other OGCMs and are sufficiently detailed to allow easy comparison of results from other models with NLOM, including statistics at specific buoy locations in specific years. There are three model configurations: coarse resolution (1/2°global) and fine resolution (1/8° global and 1/16° Pacific). These are used to assess the sensitivity to OGCM resolution, including the impact of increasing non-determinicity due to flow instabilities in the 1/8° marginally eddy-resolving and 1/1°6 fully eddy-resolving simulations. In addition, sensitivity to the choice of atmospheric forcing product is investigated. For all three models the atmospheric forcing is from the European Centre for Medium-Range Weather Forecasts (ECMWF) during 1980–1998, and for the 1/16° Pacific configuration only the forcing from the Fleet Numerical Meteorology and Oceanography Center (FNMOC) Navy Operational Global Atmospheric Prediction System (NOGAPS) during 1990–1998 is also used. Availability of NOGAPS thermal forcing begins in 1998. Daily averaged buoy time series from the National Data Buoy Center (NDBC) and the Tropical Atmosphere Ocean (TAO) array are used for inter-annual evaluation of these atmospherically forced OGCMs with no assimilation of SST data and no date-specific assimilation of any data type. For the purpose of model evaluation several statistical metrics are calculated comparing buoy and model time series: mean error (ME), root-mean-square difference (RMSD), correlation coefficient ( R), non-dimensional skill score (SS), and normalized RMSD (NRMSD). SST comparisons to the 340 yearlong daily buoy SST time series spanning 1980–1998 gave median values of − 0.09 °C for ME, 0.82 °C for RMSD, 0.92 for R, and 0.73 for SS for the 1/2° global model. Positive SS values, an indication of model success, are found for 286 out of 340 buoys (≈ 84%). An advantage of using a floating mixed layer approach in the global model is demonstrated for simulation of deep and shallow MLD at a buoy location in the Arabian Sea during the 1994–1995 Monsoon period. Model–data comparisons for 1998, when the equatorial Pacific ocean experienced a sharp transition from a strong El Niño to a strong La Niña event, revealed almost no sensitivity to the choice of thermal forcing product. In general, the sensitivity of SST accuracy to model resolution or atmospheric forcing choice was also low based on comparisons to 194 yearlong daily SST time series during 1990–1998. The median RMSD values are 0.68°, 0.61°, 0.84° and 0.84 °C for 1/2°, 1/8°, 1/16° ECMWF-forced simulations and the 1/16° with NOGAPS wind forcing and ECMWF thermal forcing. However, some regional differences exist, e.g., median RMSD values of 0.85 °C for the ECMWF-forced vs. 0.88 °C for the NOGAPS-forced simulations against the 127 TAO buoys, and 0.76 °C for NOGAPS-forced vs. 0.84 °C for ECMWF-forced simulations against the 67 NDBC buoys, all outside the equatorial region. Overall, the results of this paper revealed that 6 hourly wind and thermal forcing products exist that are sufficiently accurate to allow simulated SSTs and MLDs in an OGCM that are typically accurate to within < 1 °C in comparison to daily buoy time series, when there is no assimilation of SST data and when model SST is used in the calculation of latent and sensible heat fluxes. These results indicate that the atmospheric forcing products and the OGCM are suitable for assimilation and forecasting of SST over the global ocean.

Estimating Overwater Turbulence Intensity from Routine Gust-Factor Measurements

AbstractFor overwater diffusion estimates the Offshore and Coastal Dispersion (OCD) model is preferred by the U.S. Environmental Protection Agency. The U.S. Minerals Management Service has recommended that the OCD model be used for emissions located on the outer continental shelf. During southerly winds over the Gulf of Mexico, for example, the pollutants from hundreds of offshore platforms may affect the gulf coasts. In the OCD model, the overwater plume is described by the Gaussian equation, which requires the computation of σy and σz, which are, in turn, related to the turbulence intensity, overwater trajectory, and atmospheric stability. On the basis of several air–sea interaction experiments [the Barbados Oceanographic and Meteorological Experiment (BOMEX), the Air-Mass Transformation Experiment (AMTEX), and, most recently, the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE)] and the extensive datasets from the National Data Buoy Center (NDBC), it is shown that under neutral and stable conditions the overwater turbulence intensities are linearly proportional to the gust factor (G), which is the ratio of the wind gust and mean wind speed at height z (Uz) as reported hourly by the NDBC buoys. Under unstable conditions, it is first shown that the popular formula relating the horizontal turbulence intensity (σu,υ/u∗, where u∗ is the friction velocity) to the ratio of the mixing height (h) and the buoyancy length (L) (i.e., h/L) suffers from a self-correlation problem and cannot be used in the marine environment. Then, alternative formulas to estimate the horizontal turbulence intensities (σu,υ/Uz) using G are proposed for practical applications. Furthermore, formulas to estimate u∗ and z/L are fundamentally needed in air–sea interaction studies, in addition to dispersion meteorology.

Question of the pitch-roll buoy response to ocean waves as a simple harmonic oscillator?

The assumption that the East and North deck slopes of a pitch–roll buoy respond to East and North sea slopes as simple harmonic oscillators is routinely made by the National Data Buoy Center (NDBC) and others producing directional wave data. Although directional wave data derived with this assumption usually appear to be of good quality, the validity of the assumption has not previously been more directly demonstrated. In this paper, a method is proposed to judge the validity of the assumption for any set of time series records of buoy angular motion. The proposed method is applied to 200 record sets taken by an NDBC buoy located at ocean station 46024, and to five record sets taken by another NDBC buoy at 46051. For the 46024 data, it was demonstrated that the simple harmonic oscillator assumption was near perfectly valid. For the smaller 46051 data set, the simple harmonic oscillator assumption was shown to be slightly less valid.