Buoy Observations Research Articles

Development and validation of a parametric tropical cyclone wave height prediction model

A parametric model for computationally efficient wave height estimation within tropical cyclone conditions is developed. An updated wind vortex model is used to force a moving grid version of the WAVEWATCH III spectral model, to generate a large synthetic tropical cyclone wave field database (approximately 400 simulations). This extensive database is used to develop the parametric model which accounts for the key variables defining the magnitude and spatial distribution of waves within tropical cyclones. The model uses JONSWAP-type scaling and accounts for the effects of the moving vortex of the tropical cyclone in defining the wave field. A total of 28 buoy observations together with satellite altimeters measurements during the passage of 17 tropical cyclones (hurricanes) in the North Atlantic Ocean and Gulf of Mexico were used to validate the predicted wave heights. The resulting model is computationally efficient, making it ideal for engineering applications which require simulation of numerous tropical cyclone cases. Despite some limitations, it is a useful tool for the prediction of wave heights under extreme tropical cyclone forcing.

The role of sea spray in air-sea fluxes during Typhoon Molave: a study based on drifting buoy observations

The exchange of heat and momentum between the ocean and the atmosphere greatly affects the growth of typhoons. Utilizing the meteorological and oceanic variables observed by a Drifting Air-sea Interface Buoy (DrIB) during Typhoon Molave, a new air-sea turbulent fluxes product (referred to as DrIB product) is developed with the consideration of the thermal and dynamic effects of sea spray in the Coupled Ocean Atmosphere Response Experiment algorithm. The performances of two reanalysis products, ERA5 and MERRA2, under typhoon conditions are evaluated by comparing them to the DrIB observations. In particular, the air-sea turbulent fluxes during Typhoon Molave are systematically studied. The averaged heat (momentum) flux of the DrIB product is ~200% (~30%) higher than the reanalysis. However, the reanalysis products have higher latent heat than the DrIB product, because the reanalysis products have lower wind speed, smaller air-sea temperature difference, and drier atmosphere. The sea spray-induced mean heat (momentum) flux increase is ~1% (8%) in normal weather and is ~5% (17%) at the during-typhoon stage. Sea spray amplifies the dominance of wind speed on heat fluxes and weakens the contribution of air-sea temperature and humidity differences to heat fluxes. Sea spray starts to obviously contribute to the heat fluxes at a 10-m wind speed of ~10 m/s, and it non-linearly accelerates the air-sea heat exchange at a 10-m wind speed of ~20 m/s. When the 10-m wind speed is less than 20 m/s, the basic momentum flux (without sea spray effects) at the air-sea interface is roughly one or two orders of magnitude higher than the sea spray-induced momentum flux. Including the sea spray effects, the maximum momentum flux can even double at the 10-m wind speed of ~30 m/s.

Characterization of the wind field in the Kuroshio extended body sea based on buoy observation

In this paper, we propose to combine the data from CKEO and KEO buoys and reanalysis data to study the prevailing wind types, temperature changes and carbon fluxes in the Kuroshio Extension in different seasons in recent years, in order to deepen our understanding of the physical and chemical environment and its evolution in the sea. In this paper, we analyze the characteristics of the wind field of the Kuroshio Extension using the CKEO buoy system at a fixed coordinate (149.25°E, 39°N) in the northwest Pacific Ocean, using four methods: wind speed and direction rose diagram, wind speed comparison line diagram, temperature comparison line diagram and carbon flux variation line diagram. Compared with the KEO buoy observation system, the CKEO buoy observation system provides high quality sea-air observation data in the Kuroshio Extension, which provides valuable information for the study of sea-air interaction in the Kuroshio Extension.

Spatial and temporal wave climate variability along the south coast of Sweden during 1959–2021

This study presents 62 years of hindcast wave climate data for the south coast of Sweden from 1959–2021. The 100-km-long coast consists mainly of sandy beaches and eroding bluffs interrupted by headlands and harbours alongshore, making it sensitive to variations in incoming wave direction. A SWAN wave model of the Baltic Sea, extending from the North Sea to the Åland Sea, was calibrated and validated against wave observations from 16 locations distributed within the model domain. Wave data collected from open databases were complemented with new wave buoy observations from two nearshore locations within the study area at 14 and 15 m depth. The simulated significant wave height showed good agreement with the local observations, with an average R2 of 0.83. The multi-decadal hindcast data was used to analyse spatial and temporal wave climate variability. The results show that the directional distribution of incoming waves varies along the coast, with a gradually increasing wave energy exposure from the west towards the east. The wave climate is most energetic from October to March, with the highest wave heights in November, December, and January. In general, waves from westerly directions dominate the annual wave energy, but within the hindcast time series, a few years had larger wave energy from easterly directions. The interannual variability of total wave energy and wave direction is correlated to the North Atlantic Oscillation (NAO) index. In the offshore, the total annual wave energy had a statistically significant positive correlation with the NAO DJFM station-based index, with a Spearman rank correlation coefficient of 0.51. In the nearshore, the correlation was even stronger. Future studies should investigate the possibility of using the NAO index as a proxy for the wave energy direction and its effect on coastal evolution.

Ambiguous Variations in Tropical Latent Heat Flux since the Years around 1998

Abstract The tropical latent heat flux (LHF) has experienced a significant increase under the background of global warming in the past four decades. However, since the years around 1998, the long-term LHF variations in the tropics have been found to be quite different in various flux products. Three different trends in the LHF, climbing, near zero, and declining, are suggested by five widely used flux products, which hinders our knowledge of the actual LHF variations. Although there are buoy observations in the tropics, these observations are hard to use to evaluate flux products as they have been assimilated and/or used as benchmarks in the flux data production. This study aims to identify credible long-term LHF variations since 1998. A linear model decomposing the LHF variations into contributions from sea surface wind U and air–sea humidity differences is first applied. The linear model results show that the LHF variations have been more positively connected to U variations since 1998. Evidence from in situ and remote sensing observations is subsequently employed to identify how U has varied recently. Both Global Tropical Moored Buoy Array (GTMBA) buoy observations (from 82 buoys) and a multisensor merged satellite product support a slightly downward trend in U in the last two decades. Such a weakening of U is not conducive to oceanic evaporation and leads to a reduced LHF. Consequently, a declining LHF under a weakening U since the emergence of the global warming “hiatus” in approximately 1998 might be more convincing in the sense of data accuracy and physical consistency. Significance Statement The latent heat flux acts as the language of air–sea interactions. This study aims to examine how the tropical latent heat flux has changed since the emergence of the global warming slowdown in approximately 1998. The most striking finding is that the long-term variations in the tropical latent heat flux are fairly inconsistent in several widely used flux products in the last two decades. The sea surface wind variation is found to be the primary contributor to the latent heat flux variation after 1998. Observational evidence from buoy and remote sensing data is hence employed to clarify the actual sea surface wind and the latent heat flux variations.

The Changjiang River plume shifts from carbon source to sink when net community production exceeds a threshold in early autumn

Estuaries are crucial components of the global ocean carbon cycle due to their high productivity. However, our understanding of the carbon source-sink dynamics at the air-sea interface of estuaries is incomplete, largely due to the rapidly changing environmental conditions. To address this, we conducted a study in early autumn 2016 using high-resolution biogeochemical data collected through buoy observations in the Changjiang River plume (CRP). Using a mass balance approach, we examined the factors driving changes in the sea surface partial pressure of carbon dioxide (pCO2) and quantified the net community production (NCP) in the mixed layer. We also explored the relationship between NCP and the carbon source-sink dynamics at the air-sea interface. Our results revealed that biological activities (64.0 %) and seawater mixing (19.7 %, including lateral transport and vertical mixing) were the dominant factors controlling changes in sea surface pCO2 during the study period. Moreover, NCP in the mixed layer was affected by factors such as light availability and the presence of respired organic carbon associated with vertical mixing of seawater. Notably, we observed a strong correlation between NCP and the difference in pCO2 between air and sea (δpCO2), with a threshold NCP value of 308.4 mmol m−2 d−1 identified as an indicator of the transition from a CO2 source to a sink in the CRP. Hence, we suggest that the NCP in a specific ocean box has a threshold, beyond which the air-sea interface in estuaries will change from a carbon source to a carbon sink, and vice versa.

Performance and validation of the YHGSM global spectral model coupled with the WAM model

AbstractIn this article, we describe an implementation of two‐way coupling between the Yin–He Global Spectral Model (YHGSM) and the Wave Model (WAM). The YHGSM atmosphere model gives the surface air water density and 10‐m wind for the WAM model, while the WAM model provides the Charnock coefficient as a response. The effects on the meteorological aptitude of the two‐way coupled system are assessed statistically. The coupled model exhibits a 75% significant positive impact on both the northern (NthH) and the southern hemisphere (SthH) for all metrics at almost all pressure levels. Typhoon track assessments of the coupled model will be implemented later. The results show that the coupling of the WAM model benefits typhoon landfall prediction. Finally, we make a comprehensive comparison of the significant wave height (SWH) between the YHGSM–WAM coupled model and the European Centre for Medium‐Range Weather Forecasts (ECMWF) model with buoy data from the Chinese Ocean Monitoring Network. The comparison shows that the YHGSM–WAM coupled model exhibits more accuracy and predictive stability of SWH forecasts against buoy observations than the ECMWF forecasts. The above results indicate that the YHGSM–WAM coupled model possesses superior performance to uncoupled models.

Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast

Abstract. Uncertainty quantification of long-term modeled wind speed is essential to ensure stakeholders can best leverage wind resource numerical data sets. Offshore, this need is even stronger given the limited availability of observations of wind speed at heights relevant for wind energy purposes and the resulting heavier relative weight of numerical data sets for wind energy planning and operational projects. In this analysis, we consider the National Renewable Energy Laboratory's 21-year updated numerical offshore data set for the US East Coast and provide a methodological framework to leverage both floating lidar and near-surface buoy observations in the region to quantify uncertainty in the modeled hub-height wind resource. We first show how using a numerical ensemble to quantify the uncertainty in modeled wind speed is insufficient to fully capture the model deviation from real-world observations. Next, we train and validate a random forest to vertically extrapolate near-surface wind speed to hub height using the available short-term lidar data sets in the region. We then apply this model to vertically extrapolate the long-term near-surface buoy wind speed observations to hub height so that they can be directly compared to the long-term numerical data set. We find that the mean 21-year uncertainty in 140 m hourly average wind speed is slightly lower than 3 m s−1 (roughly 30 % of the mean observed wind speed) across the considered region. Atmospheric stability is strictly connected to the modeled wind speed uncertainty, with stable conditions associated with an uncertainty which is, on average, about 20 % larger than the overall mean uncertainty.

A multivariate pseudo-likelihood approach to estimating directional ocean wave models

Abstract Buoy data in the form of multivariate time series are routinely recorded at many locations in the world’s oceans. Such data can help characterise the ocean wavefield by modelling the frequency-direction spectrum. State-of-the-art methods for estimating the parameters of such models do not make use of the full spatiotemporal content of the buoy observations due to unnecessary assumptions and smoothing. We explain how the multivariate debiased Whittle likelihood can be used to jointly estimate all parameters of such frequency-direction spectra directly from recorded time series. We apply the method to North Sea buoy data and discuss challenging practical issues.

Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate spatial and temporal criteria for collocating altimeter data to fixed-point positions and to provide practical guidance on altimeter collocation in deep waters. The results show that a temporal criterion of 30 min and a spatial criterion between 25 km and 50 km produce the best results for altimeter collocation, in close agreement with buoy data. Applying a 25 km criterion leads to slightly better error metrics but at the cost of fewer matchups, whereas using 50 km augments the resulting collocated dataset while keeping the differences to buoy measurements very low. Furthermore, the study demonstrates that using the single closest altimeter record to the buoy position leads to worse results compared to the collocation method based on temporal and spatial averaging. The final validation of altimeter data against buoy observations shows an RMSD of 0.21 m, scatter index of 0.09, and correlation coefficient of 0.98 for Hs, confirming the optimal choice of temporal and spatial criteria employed and the high quality of the calibrated AODN altimeter dataset.

Ocean response offshore of Taiwan to super typhoon Nepartak (2016) based on multiple satellite and buoy observations

Multi-satellite and buoy observation data were used to systematically analyze the ocean response offshore of Taiwan to Super Typhoon Nepartak in 2016. The satellite data showed that a high sea surface temperature combined with a thick warm water layer and deep mixed layer provided a good thermal environment for continuous intensification of the typhoon. Two high-resolution buoys (NTU1 and NTU2) moored 375 and 175 km offshore of southeastern Taiwan were used to clarify the typhoon–ocean interaction as the typhoon approached Taiwan. The ocean conditions were similar at the two buoys before the typhoon, and both buoys were on the left side of the typhoon track and suffered similar typhoon factors (e.g., typhoon intensity and translation speed) during its passage. However, the ocean response differed significantly at the two buoys. During the forced period, the entire upper ocean was cooled at NTU1. In contrast, there was a clear three-layer vertical structure at NTU2 consisting of cool surface and deep layers with a warmer layer between the two cool layers. These responses can be attributed to strong upwelling of a cold eddy at NTU1 and vertical mixing at NTU2. These results indicate that, under similar preexisting conditions and typhoon factors, the movement of ocean eddies under typhoon forcing is an unexpected mechanism that results in upwelling and thus needs to be considered when predicting changes in the ocean environment and typhoon intensity.

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-h time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

LORA: a local ensemble transform Kalman filter-based ocean research analysis

We have produced an eddy-resolving local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) for the western North Pacific (WNP) and Maritime Continent (MC) regions (LORA-WNP and LORA-MC, respectively). This paper describes the system configuration and validation comparisons with Japan Coastal Ocean Predictability Experiment 2M (JCOPE2M) reanalysis and Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) observational datasets. The results show that the surface horizontal velocity in the LORA-WNP is closer to independent drifter buoy observations in the mid-latitude region, especially along the Kuroshio Extension (KE), and is less close in the subtropical region than the JCOPE2M, although the AVISO is the closest over the whole domain. The sea surface temperatures (SSTs) in the LORA-WNP correspond better to assimilated satellite observations than the JCOPE2M over most of the domain except for coastal regions. The results using an independent buoy south of the KE indicate that better fit of temperature in the LORA-WNP may be limited to the upper 300 m depth, probably because of the prescribed vertical localization cutoff length of 370 m. In the MC region, the surface velocity in the LORA-MC is closer to the independent drifter buoys in the equatorial coastal region and is less close in the offshore region than the AVISO. The SSTs in the LORA-MC correspond better to the assimilated satellite observations in the offshore region than the nearshore region. Therefore, the LORA-WNP and LORA-MC have sufficient accuracy for geoscience research applications as well as for fisheries, marine transport, and environment consultants.

A practical method for modeling temporally-averaged ocean wave frequency-directional spectra for characterizing wave energy climates

Wave energy resource characterization investigations have been hindered because frequency-directional wave spectra are not broadly available for many coastal regions due to the insufficient spatial coverage of buoy observation networks and spectral wave model hindcast outputs. We address this problem by developing and validating a practical method for approximating temporally-averaged frequency-directional wave spectra averaged over periods of months or years from bulk wave parameters, which are available at a much greater coverage and resolution than frequency-directional wave spectra. While the temporally averaged frequency-directional wave spectrum over these periods cannot be used for analyzing a single sea state, it aggregates multiple sea states, identifies dominant wave energy systems representing wave climate, and resolves their spectral characteristics. Therefore, modelling temporally averaged frequency-directional wave spectra is of great value for planning and designing wave energy projects, e.g., resource characterization, site assessment, and conceptual design of wave energy converters. Temporally-averaged frequency-directional wave spectra and related important wave energy parameters approximated using this method are found to be more accurate than commonly used parametric wave spectrum models. This method can be applied to a wide range of wave climates given its universality and high accuracy. Also, the availability of bulk wave parameters from multi-decade high-resolution wave model hindcasts is increasing.

Key Role of Subdaily Wind Variability for Tropical Surface Wind Stress

Abstract High-frequency observations of surface winds over the open ocean are available only at limited locations. However, these observations are essential for assessing atmospheric influences on the ocean, validating reanalysis products, and building parameterization schemes. By analyzing high-frequency measurements from the Global Tropical Moored Buoy Array, the effects of subdaily winds on the mean surface wind stress magnitude are systematically examined. Subdaily winds account for 12.4% of the total stress magnitude on average. The contribution is enhanced over the intertropical convergence zone and reaches a maximum (28.5%) in the equatorial western Pacific. The magnitude of the contribution is primarily determined by the kinetic energy of subdaily winds. Compared to the buoy observations, the ERA5 and MERRA2 subdaily winds underestimate this contribution by 51% and 63% due to underestimations of subdaily kinetic energy, leading to 7% and 8% underestimations in the total stress magnitude, respectively. Two new gustiness parameterization schemes related to precipitation are developed to account for the effect of subdaily winds, explaining ∼80% of the contribution from subdaily winds. Considering the importance of wind stress for ocean–atmosphere interactions, the inclusion of these parameterization schemes in climate models is expected to substantially improve simulations of large-scale climate variability. Significance Statement Surface wind stress drives upper-ocean circulation, which is critical for the redistribution of mass, momentum, and energy in the ocean. Moreover, it is one of the key factors controlling oceanic turbulent mixing and therefore has significant impacts on the distribution of temperature, salinity, and associated ocean variability. Using high-resolution buoy observations, this study highlights the importance of subdaily winds for integrated wind stress estimates. In addition, it finds that current state-of-the-art and widely used reanalysis products largely underestimate the effect of subdaily winds. Two new parameterization schemes are developed, leading to a better representation of the effect of subdaily winds. Including the proposed parameterization schemes in climate models is expected to substantially improve their simulations of large-scale climate variability.

Increase in the wave power caused by decreasing sea ice over the Sea of Okhotsk in winter

Increasing ocean surface waves owing to decreasing sea ice in the Sea of Okhotsk (SO) is a major concern. However, long-term trends of ocean surface waves in the SO have not yet been investigated. Therefore, the long-term trends of wave power ({P}_{mathrm{w}}) in the SO were investigated using the 40-year (from the 1980s) simulations and based on the wave model (WAVEWATCH III) enforced by three reanalyses and one satellite product. Three wave model simulations were conducted using the original (hourly or daily) reanalysis or satellite sea ice data. In addition, to quantify the responses of the long-term {P}_{mathrm{w}} trends to surface winds and sea ice, three wave model simulations were performed with climatological sea ice data, for a total of six model simulations. The model results of the original sea ice data corresponded well with the buoy observations. Moreover, {P}_{mathrm{w}} increased (~ 12–15% per decade) remarkably during winter (December–February). The increased {P}_{mathrm{w}} could be attributed to the strengthened surface winds and reduced sea ice (i.e., reduction of direct wave decay by sea ice). Moreover, a horizontal gradient of sea level pressure enhanced by the reduced sea ice strengthened the surface winds. These results suggested that the reduction of sea ice is the most important factor responsible for the positive {P}_{mathrm{w}} trend over the SO during winter.

Validation of Nadir SWH and Its Variance Characteristics from CFOSAT in China’s Offshore Waters

The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France Oceanography Satellite (CFOSAT) in these waters. This study focused on validating CFOSAT nadir SWH data with SWH data from in situ buoy observations for China’s offshore waters and the Haiyang−2B (HY−2B) satellite, from July 2019 to December 2021. The validation against the buoy data showed that the relative absolute error has a seasonal cycle, varying in a narrow range near 35%. The RMSE of the CFOSAT nadir SWH was 0.29 m when compared against in situ observations, and CFOSAT was found to be more likely to overestimate the SWH under calm sea conditions. The sea−surface winds play a key role in calm sea conditions. The spatial distributions of the CFOSAT and HY−2B seasonal SWHs were similar, with a two−year mean SWH−field correlation coefficient of 0.98. Moreover, the coherence between the two satellites’ SWH variance increased with SWH magnitude. Our study indicates that, in such typical monsoon−influenced waters, attention should be given to the influence of sea conditions on the accuracy of CFOSAT SWH, particularly in studies that combine data from multiple, long−duration space−based sensors.

Uncertainty in wave hindcasts in the North Atlantic Ocean

Accurate knowledge of ocean surface waves is crucial for ship design. With the significant advancements in model physics and numerical resources, recent numerical wave hindcast data has the potential to provide environmental conditions for estimating the wave load in the ship design process. To help estimate extreme wave loads with quantified uncertainty for strength assessments of ship structures, this study aims to quantify model uncertainty in the state-of-the-art numerical wave hindcast products. Model uncertainty was deduced from inter-model comparisons with four numerical wave hindcast products after model evaluations. For model evaluations, wave buoys distributed over the Northwest Atlantic and Northeast Atlantic were used as wave buoy arrays. In general, all four models accurately estimated the significant wave height Hs. However, as the wave condition becomes extreme with the increase of Hs, the model estimations of Hs deviate from the buoy data and differ from each other. For the extreme wave conditions, two wave hindcast products consistently showed better performance based on the buoy observations for both the Northwest Atlantic and Northeast Atlantic. In addition to Hs, the mean wave period Tm02 from the hindcast products are also examined in this study. Although evaluations of Tm02 are not often seen in previous studies, Tm02 is another crucial wave parameter because the ship response is often calculated as a function of Hs and Tm02. Based on careful comparisons, it is found that all the wave hindcast products underestimate Tm02 under the extreme wave conditions of Hs>10m. The joint probabilities of Hs and Tm02 for extreme wave conditions of Hs>10m and 8s<Tm02<14s are further calculated as a scatter diagram for extreme wave conditions (SDE). Among the SDEs from the buoys and four model products, the total number of samples varied by 50%, although the mean value of the four model products is close to the result from the buoys. The significance of the variation is further described in the context of the extreme wave load estimation for strength assessments of ship structures.

The onset process of the 2018/2019 Indonesian–Australian summer monsoon: The importance of the air–sea interaction

The Madden–Julian oscillation (MJO) and its associated air–sea interactions during the onset process of the 2018/2019 Indonesian–Australian summer monsoon (IASM) are investigated based on the in situ data from a moored buoy off the coast of northwest Australia, along with ERA5 reanalysis and satellite data. The results verify that the IASM onset in mid-December 2018 was triggered by the first-branch eastward-propagating MJO (FEMJO) originating from the tropical Indian Ocean. However, the strong negative SST anomaly (SSTA) was evident off northwest Australia, which weakens FEMJO over northern Australia and shifts the convective center further northward. The mixed layer heat budget analysis based on the buoy observations reveals that the increased latent heat loss that occurred before the arrival of the FEMJO convection was primarily attributed to the large air–sea temperature difference and strong winds, resulting in the pronounced SSTA.

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (&lt; 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.