Hurricane-induced wave modelling depends on wind forcing that captures storm intensity, structure, and evolution. However, widely used wind products often underestimate extreme winds and misrepresent storm structure, leading to substantial wave prediction errors. This study evaluates three wind-forcing enhancement strategies for WAVEWATCH III simulations during Hurricanes Ida (2021), Helene (2024), and Milton (2024). These include statistical bias correction, improved parametric models, and the Weather Research and Forecasting (WRF) model. A hybrid framework is introduced that blends a modified parametric vortex using adaptive storm-scaled radii and applies targeted corrections to residual intensity biases. Model performance is assessed against stationary and drifting buoy observations. Statistical corrections improve predictions when storm structure and timing are well captured but can overcorrect waves by up to ~4m otherwise. Dynamically blended parametric-ERA5 winds with blending zones defined as 1–3 times maximum wind radius better reproduce storm structure. Hybrid approaches provide the most robust performance, yielding the highest correlations, and lowest Hanna–Heinold errors, reducing ERA5 HH by 0.07 and 0.15. ERA5-WRF configurations capture peak timing (≤1–2 h), intensities, low track bias (≤51-km), and coherent vortex structure. These results support an event-adaptive framework for selecting wind-forcing strategies, balancing accuracy and robustness for hurricane wave modeling.

High frequency radars (HFR) from five Texas shelf stations (RLVR, SSDE, MBNP, ANWR, PINS) monitor inner-, mid-, and outer-shelf circulation. For the reanalysis of the HFR data, we applied IOOS QA/QC procedures and adopted the optimal interpolation (OI) technique to generate surface current vectors. Validation of HFR velocity vectors against Texas Automated Buoy System (TABS) currents and self-locating datum marker buoy (SLDMB) drifters confirms the fidelity of the HFR dataset following the results of the statistical tests employed. This validation revealed the OI parameters for estimating HFR vectors with improved spatial resolution and accuracy. The results indicate that the OI yields reliable Lagrangian (drifters) and Eulerian (buoys) velocities and, most importantly, captures the general circulatory patterns on the Texas Shelf. Furthermore, the patterns from the self organizing map (SOM) not only showed the two dominant circulatory patterns on Texas Shelf but also revealed a dominant transitional pattern. Based on the Hovmoller and estimates of surface transports, the study indicates that the net surface transport on the Texas Shelf vary simultaneously with the seasonal wind variability.

We developed a statistical model simulating time series of mechanically stimulated bioluminescence potential (BP) measured in situ by a bathyphotometer. The model accounts for advection of a collection of individual specimens through the bathyphotometer with prescribed physical parameters. The characteristics of mechanically stimulated bioluminescent emissions (flash kinetics parameters) of individual specimens are taxon specific and randomized to account for natural variability within a taxon. Presented numerical results demonstrate the ability of the model to reproduce BP time series recorded by the bathyphotometer during a survey conducted in the fjord of Svalbard, Norway. With the proper composition of the bioluminescent community and taxa abundances, the model correctly reproduces statistics of BP emissions in the observed time series. The results of simulations reveal a strong potential for the model tuning and calibration with respect to in situ BP records. Further model calibration to a particular bathyphotometer will require more detailed statistics for flash kinetics parameters. The presented statistical model can be used to derive statistically tested transformation functions between the in situ BP measurements recorded by different bathyphotometers.

This study evaluates the performance of WAVEWATCH III model driven by different wind forcing products (ECMWF-forecast, ERA5, HRRR, and Copernicus), as well as the behavior of different parameterizations of the model's source terms controlling energy input and dissipation (ST4, ST6) and quadruplet wave-wave interactions (Discrete Interaction Approximation- DIA, Generalized Multiple DIA- GMD) during Hurricane Ida (2021). We also compare the performance of the model configured on uniform unstructured grids (30, 20, 10, 3, and 1 km) and conventional non-uniform unstructured grids. Key findings show that ECMWF-forecast and HRRR outperformed other wind forcing products in capturing wind speeds relative to buoys, satellite and the revised Atlantic hurricane database (HURDAT2) observations. However, all wind products tended to underestimate wind speeds above 20 m/ s, with ECMWF and HRRR occasionally performing better for most wind speed values above 35 m/s relative to observations. The corresponding wave simulation results indicated that Ida's wave fields were better captured by model simulations with ECMWF and HRRR wind products, with biases of 2% against buoys in the Gulf of Mexico and 6% and 3% respectively against satellite data. The wave model source terms comparison results showed that simulations with ST4-DIAs exhibited superior performance in bulk wave analyses and were computationally efficient. Simulation using ST4-GMD with three quadruplets performed better than with five, while those with ST6DIA and ST6-GMD showed the highest error in bulk metrics. We also highlighted limitations in bulk wave analysis by computing partial Hs and 1D spectra density differences between model and buoy for some selected source terms. This reveals consistent overestimation at the lowest frequency bin (0.05-0.1 Hz) and underestimation of the three higher frequency bins (0.1-0.15; 0.15-2; 0.2-0.49 Hz) with a mix of negative and positive energy density difference across different frequencies. The parameterization ST4-DIA with optimal wind adjustment parameter outperformed other configurations in the three high frequency bins having lowest HH, RMSE, and bias. Lastly, the grid comparison result showed a reduction in Hs bias from 30 km to 1 km grid configuration by up to 13% (over 4 m). The 3 km and 1 km grids generally showed similar results. Conventional unstructured grid with 95k nodes demonstrated comparable or slightly better performance than 1 km grid with 1.86 million nodes. Increasing model's resolution did not reduce wave biases when the wind field was misrepresented, and even the source term with the highest agreement with observations could not compensate for these wind uncertainties.

The U.S. Navy’s operational wave model, WAVEWATCH III ("WW3") has globally averaged errors of 0.3-0.8 m in its forecasts of significant wave height. These errors are somewhat reduced by correcting forecasts with assimilated measurements, but at present, the Navy model is only configured to assimilate measured wave heights using optimal interpolation. This relatively crude method applies a single uniform correction to all spectral bins based on differences in total energy; it does not discriminate among the different types of waves in the spectrum.Variational data assimilation is a technique used to assimilate observational data in a manner fully consistent with model physics. Evidence from products like the Navy’s ocean circulation model NCOM-4DVAR indicates that this more comprehensive adjoint-based approach broadly outperforms alternative assimilation methods, owing to more accurate representations of the dynamics, inclusion of the time dimension in the observations, and dynamically consistent propagation of observation information from observed quantities to the fields controlling the model trajectory. In this project, we are building a comprehensive variational system for assimilation of complete wave spectra into the Navy’s operational WW3, including tangent linear (TL) and adjoint versions of the nonlinear model as well as error covariance functions. The components will be housed within NCODA, the Navy Coupled Ocean Data Assimilation software framework, and will assimilate observations at their sampling times, operating in five dimensions (i.e., spectral, spatial, and time). In its initial form, the wave assimilation system is configured to apply error corrections to a single input forcing parameter, the surface wind field, which is the primary source of error for the global wave model in open-water regions.This presentation summarizes our progress on the project to date and presents results from preliminary tests of the completed prototype system.

Sea level studies in the Mississippi Bight (MSB) are less abundant than in other coastal waters of USA. This study investigates the subinertial (time scales > 2 days) sea level anomalies in the MSB shelf. The diagnostics of the terms in the invariant form of the momentum equation were computed to determine which terms have the most influence on the anomalies in sea level. It was determined that at subinertial scales the geostrophic balance is the dominant balance in the MSB while the non-linear and time derivative terms are insignificant relative to the Coriolis term. A Least Squares procedure was applied to the subinertial surface currents data from high frequency radar surface currents (filtered with a window of 2-day Butterworth filter) to extract subinertial sea level anomalies in the MSB shelf using both geostrophic balance and the invariant form of Reynolds' averaged momentum equations. The resulting subinertial sea level anomalies were validated using sea level observations from an offshore buoy and Sentinel-3 along-track satellite altimeter data. The estimated sea level anomalies were reasonably close to observations (more than half had root mean square difference of < 0.04 m) and mostly influenced by geostrophic balance. Analysis of the empirical orthogonal functions showed that the first two modes explained the majority (85%) of the variance in the sea level anomalies estimated using the geostrophic approximation. Absolute sea level could be estimated if Global Navigation Satellite System buoys are deployed in the radar footprint.

Current parallelization trends in computer technology facilitates development of the algorithms that retrieve linear approximations of the model operators and their adjoints from ensembles of model simulations. In this study we address the problem of obtaining exact linearizations in the presence of semi-implicit numerics of the parent model under realistic constraints on the ensemble size. The method is based on factorization of the model into a sequence of local and non-local linear operators and employs prior information on the structure of the respective sparse matrices. The performance of the method is tested using 28 perturbed solutions of the shallow-water equations with a moderate size (104) state vector. Numerical experiments have shown feasibility of the approach under relatively general constraints on the structure of the parent model. Because of the substantial expense of the ensemble-based linearization, special focus is made on the assessment of the optimal frequency of such computations within the time intervals between data injections in typical operational systems.

The paper presents an analysis of tensor expressions in different curvilinear orthogonal coordinate systems. The analysis reveals specific properties of a number of approximated coordinate systems widely used in the studies of ocean dynamics. The paper consists of two parts. The part 1 presents a brief overview of the key definitions and important relations of tensor analysis which are utilized in part 2 of the paper. The part 2 considers invariant representation of different types of vector products, divergence of vector field and divergence of symmetric tensor of rank 2, gradient of a scalar filed, curl of a vector filed. The part 2 also discusses specific properties of the rate-of-strain tensor, general form of the Laplace operator, properties of operator nabla, and general forms of material derivative for scalar and vector fields. The equations for the properties under consideration are derived for the physical components of the corresponding tensors.

Monitoring surface currents by coastal high-frequency radars (HFRs) is a cost-effective observational technique with good prospects for further development. An important issue in improving the efficiency of HER systems is the optimization of radar positions on the coastline. Besides being constrained by environmental and logistic factors, such optimization has to account for prior knowledge of local circulation and the target quantities (such as transports through certain key sections) with respect to which the radar positions are to be optimized.In the proposed methodology, prior information of the regional circulation is specified by the solution of the 4D variational assimilation problem, where the available climatological data in the Bering Strait (BS) region are synthesized with dynamical constraints of a numerical model. The optimal HFR placement problem is solved by maximizing the reduction of a posteriori error in the mass, heat, and salt (MHS) transports through the target sections in the region. It is shown that the MHS transports into the Arctic and their redistribution within the Chukchi Sea are best monitored by placing HFRs at Cape Prince of Wales and on Little Diomede Island. Another equally efficient configuration involves placement of the second radar at Sinuk (western Alaska) in place of Diomede. Computations show that 1) optimization of the HFR deployment yields a significant (1.3-3 times) reduction of the transport errors compared to nonoptimal positioning of the radars and 2) error reduction provided by two HFRs is an order of magnitude better than the one obtained from three moorings permanently maintained in the region for the last 5 yr. This result shows a significant advantage of BS monitoring by I-LFRs compared to the more traditional technique of in situ moored observations. The obtained results are validated by an extensive set of observing system simulation experiments.

Sea surface height anomalies observed by satellites in 1992–2010 are combined with monthly climatologies of temperature and salinity to estimate circulation in the southern Bering Sea. The estimated surface and deep currents are consistent with independent velocity observations by surface drifters and Argo floats parked at 1,000 m. Analysis reveals 1–3-Sv interannual transport variations of the major currents with typical intra-annual variability of 3–7 Sv. On the seasonal scale, the Alaskan Stream transport is well correlated with the Kamchatka (0.81), Near Strait (0.53) and the Bering Slope (0.37) currents. Lagged correlations reveal a gradual increase of the time the lags between the transports of the Alaskan Stream, the Bering Slope Current and the Kamchatka Current, supporting the concept that the Bering Sea basin is ventilated by the waters carried by the Alaskan Stream south of the Aleutian Arc and by the flow through the Near Strait. Correlations of the Bering Sea currents with the Bering Strait transport are dominated by the seasonal cycle. On the interannual time scale, significant negative correlations are diagnosed between the Near Strait transport and the Bering Slope and Alaskan Stream currents. Substantial correlations are also diagnosed between the eddy kinetic energy and Pacific Decadal Oscillation.

[1] A new mean dynamic topography (MDT) for the Bering Sea is presented. The product is obtained by combining historical oceanographic and atmospheric observations with high-resolution model dynamics in the framework of a variational technique. Eighty percent of the ocean data underlying the MDT were obtained during the last 25 years and include hydrographic profiles, surface drifter trajectories, and in situ velocity observations that were combined with National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) atmospheric climatology. The new MDT quantifies surface geostrophic circulation in the Bering Sea with a formal accuracy of 2–4 cm/s. The corresponding sea surface height (SSH) errors are estimated by inverting the Hessian matrix in the subspace spanned by the leading modes of SSH variability observed from satellites. Comparison with similar products based on in situ observations, satellite gravity, and altimetry shows that the new MDT is in better agreement with independent velocity observations by Argo drifters and moorings. Assimilation of the satellite altimetry data referenced to the new MDT allows better reconstruction of regional circulations in the Bering Sea. Comparisons also indicate that MDT estimates derived from the latest Gravity Recovery and Climate Experiment geoid model have more in common with the presented sea surface topography than with the MDTs based on earlier versions of the geoid. The presented MDT will increase the accuracy of calculations of the satellite altimeter absolute heights and geostrophic surface currents and may also contribute to improving the precision in estimating the geoid in the Bering Sea.

A hybrid background error covariance (BEC) model for three-dimensional variational data assimilation of glider data into the Navy Coastal Ocean Model (NCOM) is introduced. Similar to existing atmospheric hybrid BEC models, the proposed model combines low-rank ensemble covariances B-m with the heuristic Gaussian-shaped covariances B-0 to estimate forecast error statistics. The distinctive features of the proposed BEC model are the following: (i) formulation in terms of inverse error covariances, (ii) adaptive determination of the rank m of B-m with information criterion based on the innovation error statistics, (iii) restriction of the heuristic covariance operator B-0 to the null space of B-m, and (iv) definition of the BEC magnitudes through separate analyses of the innovation error statistics in the state space and the null space of B-0.The BEC model is validated by assimilation experiments with simulated and real data obtained during a glider survey of the Monterey Bay in August 2003. It is shown that the proposed hybrid scheme substantially improves the forecast skill of the heuristic covariance model.

: Temperature and salinity profiles observed by gliders in the Monterey Bay in August 2003 are assimilated into NCOM model in the framework of a 3dVar scheme with a hybrid background error covariance (BEC) representation. The model performance is validated against independent mooring observations for the assimilation runs with I-hour analysis cycle In the first experiment the background error statistics was estimated using the ensemble of model states spanning the entire observation period, whereas in the second experiment the BEC information was acquired by averaging over the 3day floating temporal window (FTW) centered at the analysis time. It is found that the FTW scheme provides lower discrepancy between the values of temperature, salinity and velocity predicted by the model and observed at the moorings. The improvement becomes more clearly visible during the upwelling and relaxations events, associated with intermittent wind forcing. During these periods the FTW scheme provides a significantly (2-3 times) better fit to the mooring data.

Application of the 4D-var data assimilation technique to the system of the two one-way nested models is discussed. We consider preliminary result of the data assimilation experiments with the nested system involving the Bering Strait high resolution model and a coarser resolution model configured for the entire Chukchi Sea. The observations obtained both in the Chukchi Sea and the Bering Strait regions are assimilated using the “nested” adjoint models. The efficiency of several nesting schemes is analyzed. The nested variational data assimilation scheme allows us to obtain a dynamically consistent solution for the both nested models and provides an improved hindcast of the Bering Strait and Chukchi Sea circulations.

A forecast error covariance model for assimilatiing glider data is proposed. It is based on the assumption of statistical independence of the errors described by the leading dynamical modes and by the small-scale components of the errors’ projection on the subspace orthogonal to that modes. The associated cost function penalizes error projections on the modes and the magnitude of the high-pass filtered projection of the errors on the orthogonal subspace.

The Chukchi Sea (CS) circulation reconstructed for September 1990 to October 1991 from sea ice and ocean data is presented and analyzed. The core of the observational data used in this study comprises the records from 12 moorings deployed in 1990 and 1991 in U.S. and Russian waters and two hydrographic surveys conducted in the region in the fall of 1990 and 1991. The observations are processed by a two‐step data assimilation procedure involving the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System (employing a nudging algorithm for sea ice data assimilation) and the Semi‐implicit Ocean Model [utilizing a conventional four‐dimensional variational (4D‐var) assimilation technique]. The reconstructed CS circulation is studied to identify pathways and assess residence times of Pacific water in the region; quantify the balances of volume, freshwater, and heat content; and determine the leading dynamical factors configuring the CS circulation. It is found that in 1990–1991 (high AO index and a cyclonic circulation regime) Pacific water transiting the CS toward the Canada basin followed two major pathways, namely via Herald Canyon (Herald branch of circulation, 0.23 Sv) and between Herald Shoal and Cape Lisburne (central branch of circulation and Alaskan Coastal Current, 0.32 Sv). The annual mean flow through Long Strait was negligible (0.01 Sv). Typical residence time of Pacific water in the region varied between 150 days for waters entering the CS in September and 270 days for waters entering in February/March. Momentum balance analysis reveals that geostrophic balance between barotropic pressure gradient and Coriolis force dominated for most of the year. Baroclinic effects were important for circulation only in the regions with large horizontal salinity gradients associated with the fresh Alaskan and Siberian coastal currents and the Cape Lisburne and Great Siberian polynyas. In the polynyas, the baroclinic effects were due to strong salinification and convection processes associated with sea ice formation.