Ocean Model Predictions Research Articles

Controlling Buoyancy-Driven Profiling Floats for Applications in Ocean Observation

Establishing a persistent presence in the ocean with an autonomous underwater vehicle (AUV) capable of observing temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we examine the utility of vehicles that can only control their depth in the water column for such extended deployments. We present a strategy that utilizes ocean model predictions to facilitate a basic level of autonomy and enables general control for these profiling floats. The proposed method is based on experimentally validated techniques for utilizing ocean current models to control autonomous gliders. With the appropriate vertical actuation, and utilizing spatio–temporal variations in water speed and direction, we show that general controllability results can be met. First, we apply an A* planner to a local controllability map generated from predictions of ocean currents. This computes a path between start and goal waypoints that has the highest likelihood of successful execution. A computed depth plan is generated with a model-predictive controller (MPC), and selects the depths for the vehicle so that ambient currents guide it toward the goal. Mission constraints are included to simulate and motivate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA, USA, that show encouraging results in the ability of a drifting vehicle to reach a desired location.

Oceanographic pursuit: Networked control of multiple vehicles tracking dynamic ocean features

We present an integrated framework for joint estimation and pursuit of dynamic features in the ocean, over large spatial scales and with multiple collaborating vehicles relying on limited communications. Our approach uses ocean model predictions to design closed-loop networked control at short time scales, and the primary innovation is to represent model uncertainty via a projection of ensemble forecasts into local linearized vehicle coordinates. Based on this projection, we identify a stochastic linear time-invariant model for estimation and control design. The methodology accurately decomposes spatial and temporal variations, exploits coupling between sites along the feature, and allows for advanced methods in communication-constrained control. Simulations with three example datasets successfully demonstrate the proof-of-concept.

고해상도 해양예보모형 HYCOM에 재현된 쓰시마난류

This study investigates the characteristic of the Tsushima Warm Current from an assimilated high resolution global ocean prediction model, <TEX>$1/12^{\circ}$</TEX> Global HYbrid Coordiate Ocean Model (HYCOM). The model results were verified through a comparison with current measurements obtained by acoustic Doppler current profiler (ADCP) mounted on the passenger ferryboat between Busan, Korea, and Hakata, Japan. The annual mean transport of the Tsushima Warm Current was 2.56 Sverdrup (Sv) (1 Sv = <TEX>$10^6m^3s^{-1}$</TEX>), which is similar to those from previous studies (Takikawa et al. 1999; Teague et al. 2002). The volume transport time series of the Tsushima Warm Current from HYCOM correlates to a high degree with that from the ADCP observation (the correlation coefficient between the two is 0.82). The spatiotemporal structures of the currents as well as temperature and salinity from HYCOM are comparable to the observed ones.

Uncertainty of transmission loss due to small scale fluctuations of sound speed in two environments

Seismic oceanography techniques reveal detection of small scale variations in sound speed not detectable via conventional oceanographic means, i.e., frequent XBT or CTD casts). Due to computational and practical limitations, such small scale spatial and temporal detail that exists in a real ocean environment is not typically included in acoustic ocean models. However, such measurements can provide insight to the small scale variability (uncertainty) that exists in the ocean but is not predicted by mesoscale ocean models. We show acoustic predictions made with the Range Dependent Acoustic Model (RAM) using measured seismic oceanography and CTD data at two locations in significantly different environments. Additionally, the CTD measurements are smoothed to a resolution comparable to that provided by a dynamic ocean model and acoustic predictions are computed. The Uncertainty Band (UBAND) algorithm (UBAND) [Zingarelli,”A mode-based technique for estimating uncertainty in range-averaged transmission loss results from underwater acoustic calculations,” J. Acoust. Soc. Am. 124(4) (2008)] is applied to the smoothed oceanographic data using estimates of sound speed uncertainty calculated from the high resolution measurements. We find reasonable estimates of uncertainty due to the small scale oceanography that is not characterized by mesoscale ocean models.

Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data

The operational ocean prediction model for the North and Baltic Seas of the German Maritime and Hydrographic Agency (BSH) is augmented with a multivariate data assimilation (DA) system. We report on the implementation and performance of the scheme which is based on ensemble forecasting. Here we apply the localised Singular Evolutive Interpolated Kalman (SEIK) filter for assimilating the NOAA AVHRR-derived sea surface temperature (SST) data.Results are presented for two periods: October 2007 is used for calibration and March 2011 for the analysis of the performance in a pre-operational phase. The major forecast improvement is found to be a reduction in the local temperature bias. As compared with the regular BSH forecast without assimilation, the root mean square difference between the predicted SST and satellite observations is reduced on average from 0.87°C to 0.53°C for March 2011. The quality of the predicted fields that were not assimilated (velocities, sea level and salinity) is preserved as is confirmed by independent in situ data. The results have required adjustment of the conditional data error statistics.The experiments conducted with different timing and frequency of data assimilation and variable forecasting periods show that the DA system corrects systematic model uncertainties and, due to memory to the corrections, improves prediction over periods of up to 5days. The results also explicitly illustrate a lower quality of the AVHRR daytime product and reveal low informative influence of the data on the forecasting system when daytime SSTs are assimilated additionally to midnight observations.

Assessing the fidelity of surface currents from a coastal ocean model and HF radar using drifting buoys in the Middle Atlantic Bight

The rapid expansion of urbanization along the world’s coastal areas requires a more comprehensive and accurate understanding of the coastal ocean. Over the past several decades, numerical ocean circulation models have tried to provide such insight, based on our developing understanding of physical ocean processes. The systematic establishment of coastal ocean observation systems adopting cutting-edge technology, such as high frequency (HF) radar, satellite sensing, and gliders, has put such ocean model predictions to the test, by providing comprehensive observational datasets for the validation of numerical model forecasts. The New York Harbor Observing and Prediction System (NYHOPS) is a comprehensive system for understanding coastal ocean processes on the continental shelf waters of New York and New Jersey. To increase confidence in the system’s ocean circulation predictions in that area, a detailed validation exercise was carried out using HF radar and Lagrangian drifter-derived surface currents from three drifters obtained between March and October 2010. During that period, the root mean square (RMS) differences of both the east–west and north–south currents between NYHOPS and HF radar were approximately 15 cm s−1. Harmonic analysis of NYHOPS and HF radar surface currents shows similar tidal ellipse parameters for the dominant M2 tide, with a mean difference of 2.4 cm s−1 in the semi-major axis and 1.4 cm s−1 in the semi-minor axis and 3° in orientation and 10° in phase. Surface currents derived independently from drifters along their trajectories showed that NYHOPS and HF radar yielded similarly accurate results. RMS errors when compared to currents derived along the trajectory of the three drifters were approximately 10 cm s−1. Overall, the analysis suggests that NYHOPS and HF radar had similar skill in estimating the currents over the continental shelf waters of the Middle Atlantic Bight during this time period. An ensemble-based set of particle tracking simulations using one drifter which was tracked for 11 days showed that the ensemble mean separation generally increases with time in a linear fashion. The separation distance is not dominated by high frequency or short spatial scale wavelengths suggesting that both the NYHOPS and HF radar currents are representing tidal and inertial time scales correctly and resolving some of the smaller scale eddies. The growing ensemble mean separation distance is dominated by errors in the mean flow causing the drifters to slowly diverge from their observed positions. The separation distance for both HF radar and NYHOPS stays below 30 km after 5 days, and the two technologies have similar tracking skill at the 95 % level. For comparison, the ensemble mean distance of a drifter from its initial release location (persistence assumption) is estimated to be greater than 70 km in 5 days.

Controlling Minimally-Actuated Vehicles for Applications in Ocean Observation

Establishing a persistent presence in the ocean with an AUV to observe temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we propose a strategy that utilizes ocean model predictions to increase the autonomy and control of Lagrangian or profiling floats for precisely this purpose. An A* planner is applied to a local controllability map generated from predictions of ocean currents to compute a path between prescribed waypoints that has the highest likelihood of successful execution. The control to follow the planned path is computed by use of a model predictive controller. This controller is designed to select the best depth for the vehicle to exploit ambient currents to reach the goal waypoint. Mission constraints are employed to simulate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA USA, and show surprising results in the ability of a Lagrangian float to reach a desired location.

Controls on the production of organohalogens by phytoplankton: Effect of nitrate concentration and grazing

Using a number of laboratory culture experiments we have been able to show that trace gas release by phytoplankton is affected by changes in ambient nitrate conditions. Nutrient concentrations are fundamentally important to phytoplankton community structure and primary production, so it is important to understand if changes in nitrate levels are likely to affect metabolic processes. Recent human activities have added a large quantity of atmospheric nitrogen to the open ocean and this, in turn, will affect both carbon dioxide drawdown and nitrous oxide release. In contrast, ocean prediction models tell us that large parts of the ocean are going to become more oligotrophic. How these changes will affect other climatically active gases, e.g., organohalogens, is unclear. In this study we look at the effect of varying the nitrate to phosphate ratio on the release of three methyl halides. Some attention is also given to the effects of grazing as a means of gas release. Assuming that a 16:1 nitrate:phosphate ratio is the norm, we show that increasing the nitrate level above 16 μmol L−1 can lead to up to an 85% increase in trace gas production per unit cell while lowering the concentration can result in up to a 65% reduction. This is the first time that such a nutrient effect has been observed. Grazing by the microzooplankton Oxyrrhis marina also increases release, but whether this is due to enhanced metabolic processes caused by stress or the breaking down of cells due to grazing is unclear.

Full-field tomography and Kalman tracking of the range-dependent sound speed field in a coastal water environment

The monitoring, assessment and prediction of dynamic processes in shallow water constitute an attractive challenge. The availability of targeted observations enable high-resolution ocean forecasting to develop the 4D environmental picture. In particular, range-resolving acoustic tomography data constitute an effective way to reduce the non-uniform distribution and sparsity of standard hydrographic observations. In this paper a Kalman filtering scheme is investigated for tracking the time variations of a range-dependent sound-speed field in a vertical slice of a shallow water environment from full-field acoustic data and a propagation model taking into account the acoustic properties of the seafloor and subseafloor. The basic measurement setup for each radial of a tomography system consists of a broadband, multifrequency sound source and a vertical receiver array spanning most of the water column. The state variables represent the main features of the sound-speed field in a low dimensional parameterization scheme using empirical orthogonal functions. To test the algorithm acoustic data are synthesized from ocean model predictions obtained in support of the MREA/BP07 experiment southeast of the island of Elba, Italy. Bottom geoacoustic parameters obtained from previous acoustic inversion experiments are input to a normal mode propagation model as a background dataset. Additional data such as sea-surface temperature data from satellite or in situ hydrographic observations provide a priori approximate information about the range dependency of the subsurface structure and an estimation of the sea-surface sound speed. The evolution of the entire sound-speed field in the vertical slice is then sequentially estimated by the inversion processor. The results show that the daily space and time variations of the simulated sound-speed field can be effectively tracked with an extended Kalman filter. The depth-integrated sound-speed error (RMS) remains lower than 0.3 m/s (0.09 °C) when the benchmark environment is completely determined in the parameter space and lower than 0.7 m/s (0.22 °C) for an approximate environment parameterization.

Impact of glider data assimilation on the Monterey Bay model

Abstract Glider observations were essential components of the observational program in the Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay area during summer of 2003. This paper is focused on the impact of the assimilation of glider temperature and salinity observations on the Navy Coastal Ocean Model (NCOM) predictions of surface and subsurface properties. The modeling system consists of an implementation of the NCOM model using a curvilinear, orthogonal grid with 1–4 km resolution, with finest resolution around the bay. The model receives open boundary conditions from a regional (9 km resolution) NCOM implementation for the California Current System, and surface fluxes from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model at 3 km resolution. The data assimilation component of the system is a version of the Navy Coupled Ocean Data Assimilation (NCODA) system, which is used for assimilation of the glider data into the NCOM model of the Monterey Bay area. The NCODA is a fully 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity. Assimilation of glider data improves the surface temperature at the mooring locations for the NCOM model hindcast and nowcasts, and for the short-range (1–1.5 days) forecasts. It is shown that it is critical to have accurate atmospheric forcing for more extended forecasts. Assimilation of glider data provided better agreement with independent observations (for example, with aircraft measured SSTs) of the model-predicted and observed spatial distributions of surface temperature and salinity. Mooring observations of subsurface temperature and salinity show sharp changes in the thermocline and halocline depths during transitions from upwelling to relaxation and vice versa. The non-assimilative run also shows these transitions in subsurface temperature; but they are not as well defined. For salinity, the non-assimilative run significantly differs from the observations. However, the glider data assimilating run is able to show comparable results with observations of thermocline as well as halocline depths during upwelling and relaxation events in the Monterey Bay area. It is also shown that during the relaxation of wind, the data assimilative run has higher value of subsurface velocity complex correlation with observations than the non-assimilative run.

A Physics-Based Parameterization of Air–Sea Momentum Flux at High Wind Speeds and Its Impact on Hurricane Intensity Predictions

Abstract A new bulk parameterization of the air–sea momentum flux at high wind speeds is proposed based on coupled wave–wind model simulations for 10 tropical cyclones that occurred in the Atlantic Ocean during 1998–2003. The new parameterization describes how the roughness length increases linearly with wind speed and the neutral drag coefficient tends to level off at high wind speeds. The proposed parameterization is then tested on real hurricanes using the operational Geophysical Fluid Dynamics Laboratory (GFDL) coupled hurricane–ocean prediction model. The impact of the new parameterization on the hurricane prediction is mainly found in increased maximum surface wind speeds, while it does not appreciably affect the hurricane central pressure prediction. This helps to improve the GFDL model–predicted wind–pressure relationship in strong hurricanes. Attempts are made to provide physical explanations as to why the reduced drag coefficient affects surface wind speeds but not the central pressure in hurricanes.

An assessment of a nowcast/forecast system for the straits of Florida/Florida Current regime

The Florida Current (FC) largely fills the Straits of Florida and is variable on a broad spectrum of time and space scales. Some portions of the variability are due to variable forcing by tides, winds, heating/cooling, and throughflow; other portions are due to intrinsic instabilities of the FC. To predict, as well as to better understand this complex regime, a now-cast/forecast system (East Florida Shelf Information System (EFSIS)) has been implemented and assessed (http://efsis. rsmas.miami.edu). EFSIS is based on an implementation of the Princeton Ocean Model (POM) with mesoscale — admitting resolution on a curvilinear grid. It is forced by a mesoscale numerical weather prediction system (called Eta) run operationally by the National Centers for Environmental Prediction (NCEP), eight tidal constituents from a global tidal model, and lateral boundary conditions from an operational global ocean prediction model, i.e., the Navy Coastal Ocean Model (NCOM). Real-time observations of coastal sea level, coastal sea surface temperature, coastal HF radar-derived surface current maps, and FC volume transport are used to verify and validate EFSIS. EFSIS is part of an evolving strategy for real-time predictive coastal ocean modeling methodology, and for fostering the understanding of the variability of the regime on several time and space scales. Here, some of the verification and validation results are provided, as well as diagnostic analyses of dynamical processes. The central point is that an example is provided of a ‘scientific revolution’ in progress that combines real-time observations and numerical circulation models to yield a credible sequence of synoptic views of coastal ocean circulation for the first time.

Ocean model predictions of chemistry changes from carbon dioxide emissions to the atmosphere and ocean

We present ocean chemistry calculations based on ocean general circulation model simulations of atmospheric CO2 emission, stabilization of atmospheric CO2 content, and stabilization of atmospheric CO2 achieved in total or in part by injection of CO2 to the deep ocean interior. Our goal is to provide first‐order results from various CO2 pathways, allowing correspondence with studies of marine biological effects of added CO2. Parts of the Southern Ocean become undersaturated with respect to aragonite under the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 emission pathways and the WRE pathways that stabilize CO2 at 650 ppm or above. Cumulative atmospheric emission of 5000 Pg C produces aragonite undersaturation in most of the surface ocean; 10,000 Pg C also produces calcite undersaturation in most of the surface ocean. Stabilization of atmospheric CO2 at 450 ppm produces both calcite and aragonite undersaturation in most of the deep ocean. The simulated SRES pathways produce global surface pH reductions of ∼0.3–0.5 units by year 2100. Approximately this same reduction is produced by WRE650 and WRE1000 stabilization scenarios and by the 1250 Pg C emission scenario by year 2300. Atmospheric emissions of 5000 Pg C and 20,000 Pg C produce global surface pH reductions of 0.8 and 1.4 units, respectively, by year 2300. Simulations of deep ocean CO2 injection as an alternative to atmospheric release show greater chemical impact on the deep ocean as the price for having less impact on the surface ocean and climate. Changes in ocean chemistry of the magnitude shown are likely to be biologically significant.

GPS measurements of ocean loading and its impact on zenith tropospheric delay estimates: a case study in Brittany, France

The results from a global positioning system (GPS) experiment carried out in Brittany, France, in October 1999, aimed at measuring crustal displacements caused by ocean loading and quantifying their effects on GPS-derived tropospheric delay estimates, are presented. The loading effect in the vertical and horizontal position time series is identified, however with significant disagreement in amplitude compared to ocean loading model predictions. It is shown that these amplitude misfits result from spatial tropospheric heterogeneities not accounted for in the data processing. The effect of ocean loading on GPS-derived zenith total delay (ZTD) estimates is investigated and a scaling factor of 4.4 between ZTD and station height for a 10° elevation cutoff angle is found (i.e. a 4.4-cm station height error would map into a 1-cm ZTD error). Consequently, unmodeled ocean loading effects map into significant errors in ZTD estimates and ocean loading modeling must be properly implemented when estimating ZTD parameters from GPS data for meteorological applications. Ocean loading effects must be known with an accuracy of better than 3 cm in order to meet the accuracy requirements of meteorological and climatological applications of GPS-derived precipitable water vapor.

Short‐period oceanic circulation: Implications for satellite altimetry

Atmospherically forced, high‐frequency oceanic variability is investigated using different configurations of an ocean general circulation model. At periods less than 20 days, the dynamic response of the sea surface to pressure loading exceeds that due to wind stress, and is mostly barotropic. Energy at these periods aliases into satellite altimeter measurements of sea surface height (SSHT). The global variance of collinear (≈10 day) differences of this modelled aliased SSHT is between (2 cm)² and (3.5cm)², depending on the model configuration used. The local variance can reach (14cm)² at some high latitude locations. We use the ocean model predictions to remove the high‐frequency signals from TOPEX/Poseidon (T/P) observations. We obtain a global variance reduction in collinear differences of up to (2cm)², about 7% of the T/P signal. Our model has difficulty in predicting the variability at periods less than 5 days.

Temporal and spatial scales of the Yellow Sea thermal variability

This paper presents an analysis on the space/time statistical thermal structure in the Yellow Sea from the Navy's Master Observation Oceanography Data Set during 1929–1991. This analysis is for the establishment of an Optimum Thermal Interpolation System of the Yellow Sea (a shallow sea), for the assimilation of observational data into coastal σ coordinate ocean prediction models (e.g., the Princeton Ocean Model), and for the design of an optimum observational network. After quality control the data set consists of 35,658 profiles. Sea surface temperatures at 50% and 80% water depths are presented here as representing the thermal structure of surface, middepth, and near‐bottom layers. In the Yellow Sea shelf the temporal and spatial signals fluctuate according to the Asian monsoon. Variation of surface forcing from winter to summer monsoon season causes the change of the thermal structure, including the decorrelation scales. Our computation shows that the seasonal variation of the surface horizontal decorrelation scale is around 90 km from 158 km in winter to 251 km in summer and the seasonal variation of the surface temporal decorrelation scale is around 2.4 days from 14.7 days in winter to 12.3 days in summer. The temporal decorrelation scale increases with depth in both summer (evident) and winter (slight). The near‐bottom water (σ=0.8) has the longest temporal scale in summer, which could be directly related to the existence of the Yellow Sea Cold Water throughout the summer in the middle of the Yellow Sea. The temporal and spatial decorrelation scales obtained in this study are useful for running optimum interpolation models and for designing an optimum observational network. The minimum sampling density required to detect thermal variability in the Yellow Sea shelf would be 50–80 km and 4–6 day intervals per temperature measurement with the knowledge that the subsurface features will also be adequately sampled.

Parametric models for acoustic convergence zone variations

A model that is designed to provide feedback for assessment and improvement of ocean prediction models is presented. Typical prediction model temperature field output is assumed to be modeled statistically and parametrically. An objective is to relate resulting ocean environmental parameters to some natural acoustic metrics corresponding to measurable features of the received field. The approach uses a canonical sound-speed profile [J. C. Miller, J. Acoust. Soc. Am. 71, 859–862 (1982)] which has closed-form ray solutions in a range-independent medium. By incorporating the environmental parameters into the sound-speed profile and employing the ray geometry for a shallow acoustic source, expressions for convergence zone widths and ranges are developed. A sensitivity examination shows that thermocline scale is the dominant factor for convergence zone variabilities. Implications of the relationships for model assessment will be discussed. [Work supported by INO.]

Statistical inference of weakly correlated subthermocline fields from satellite altimeter data

To initialize the barotropic and baroclinic modes, numerical ocean prediction models need information both above and below the main thermocline. Forecasts of upper ocean mesoscale variability using real and simulated data show high sensitivity to the Subthermocline pressure (STP) field. Results using simulated data indicate that the accuracy of this field may be the limiting factor on the time scale for mesoscale oceanic predictive skill. Satellite altimetry provides a potentially abundant source of sea surface height (SSH) data, but there is no comparable source of Subthermocline information on the horizon. We investigate statistical techniques to infer Subthermocline pressure anomalies from SSH data, a problem complicated by the weak correlation between the fields. This problem is addressed by using the degrees of freedom available in the data and by describing them in an efficient manner to suppress noise, eliminate unskilled or redundant estimators and to prevent ill‐conditioned matrix inversions. Multilinear regression, empirical orthogonal function (EOF) regression and principal estimator patterns are compared using data simulated by a numerical ocean model and error models. Numerous questions that need to be addressed for proper application of the statistical techniques are investigated. Topics include noise suppression and the impact of the noise on accuracy. These topics are studied as a function of decorrelation distance in the noise and the presence or absence of noise in dependent and independent data sets. In this context we also investigate dependent data set size requirements, the criteria for choosing estimators, the number, areal coverage, and spacing of sampling locations used in the estimators, the effect of the ocean dynamical regime on the results, the effects of ocean model imperfections or changes in population statistics on the results, SSH versus ΔSSH in an orbital repeat period as estimators, and the effect of orbital repeat period on Subthermocline estimates from ΔSSH data. In the presence of 40% rms noise the usual significance tests are much too conservative for this application. (However, we also found that EOFs calculated from spatially correlated, temporally uncorrelated noise can pass a popular EOF significance test based on uncorrelated noise.) Although more difficult to suppress than uncorrelated noise, correlated noise did not markedly increase the error in these tests. Spatial coverage of the estimators was found to be an important parameter, and four to five ascending‐or descending tracks per wavelength were sufficient for uniformly accurate estimates whether beneath or between altimeter tracks. In most of these tests, ΔSSH proved a better estimator than SSH, but for ΔSSH resolving the shortest major time scale is a necessity. High accuracy is not required for Subthermocline pressure anomalies to substantially enhance upper ocean forecast skill in a numerical ocean prediction model. In results to be reported elsewhere, a statistically inferred STP field substantially enhanced the skill of a Gulf Stream forecast model where the SSH was initialized from oceanic observations. The inferred STP field allowed the model to show forecast skill in comparison to persistence of the initial state.

Computational results using FOR3D and an analytic eddy model

A three-dimensional parabolic approximation model FOR3D has been developed. Its validity has been verified by means of test examples and comparisons. The present implementation of the model has some desirable capabilities, and additional useful ones are under development. A primary advantage of the model is the availability of treating azimuthal coupling. The FOR3D has been used to process environmental data obtained from ocean prediction models. In this report, an FOR3D is used to examine acoustic propagation through a parametric eddy model. Computational results will be presented, and issues relating to three-dimensional effects will be discussed. [Work supported jointly by NUSC and ONR.]

A computer model for forecasting ocean wave parameters at specific sites around the the New Zealand coast

Abstract An applications model for forecasting frequency‐directional wave spectra at any appropriately specified site is described. There are two stages to the calculations. Firstly, a spectrum is calculated based on results at nearby gridpoints from an ocean wave prediction model. This is then adjusted to make the spectrum consistent with the local wind history. Verifications of the model are made at sites off Cape Egmont and Great Barrier Island, North Island, New Zealand. These give encouraging results for the shape of the frequency spectrum, with reasonable skill evident in the energetic parts of the spectrum. The significant wave heights also agree well, with the model estimates explaining two thirds of the measured variance.