Moored Buoy Data Research Articles

Characterizing the Effect of Ocean Surface Currents on Advanced Scatterometer (ASCAT) Winds Using Open Ocean Moored Buoy Data

The ocean surface current influences the roughness of the sea surface, subsequently affecting the scatterometer’s measurement of wind speed. In this study, the effect of surface currents on ASCAT-retrieved winds is investigated based on in-situ observations of both surface winds and currents from 40 open ocean moored buoys in the tropical and mid-latitude oceans. A total of 28,803 data triplets, consisting of buoy-observed wind vectors, current vectors, and ASCAT Level 2 wind vectors, were collected from the dataset spanning over 10 years. It is found that the bias between scatterometer-retrieved wind speed and buoy-observed wind speed is negatively correlated with the ocean surface current speed. The wind speed bias is approximately 0.96 times the magnitude of the downwind surface current. The root-mean-square error between the ASCAT wind speeds and buoy observations is reduced by about 15% if rectification with ocean surface currents is involved. Therefore, it is essential to incorporate surface current information into wind speed calibration, particularly in regions with strong surface currents.

SAR and ASCAT Tropical Cyclone Wind Speed Reconciliation

Wind speed reconciliation across different wind sources is critically needed for extending available satellite wind records in Tropical Cyclones. The deviations between wind references of extremes, such as the moored buoy data and dropsonde wind estimates for guidance on geophysical model function development, are one of the main causes of wind speed differences for wind products, for instance, the overestimation of Synthetic Aperture Radars (SARs) relative to ASCAT winds. The study proposes a new wind speed adjustment to achieve mutual adjustment between ASCAT CMOD7 winds and simultaneous SAR wind speeds. The so-called CMOD7D-v2 adjustment is constructed based on the statistical analysis of SAR and ASCAT Tropical Cyclone acquisitions between 2016 and 2021, showing a satisfactory performance in wind speed reconciliation for winds with speeds higher than 14 m/s. Furthermore, the error characteristics of the CMOD7D-v2 adjustment for Tropical Cyclone winds are analyzed using the Triple Collocation analysis technique. The analysis results show that the proposed wind adjustment can reduce ASCAT wind errors by around 16.0% when adjusting ASCAT winds to SAR wind speeds. In particular, when downscaling SAR winds, the improvement in ASCAT wind errors can be up to 42.3%, effectively alleviating wind speed differences across wind sources. Furthermore, to avoid the impacts of large footprints by ASCAT sensors, wind speeds retrieved from SAR VV signals (acting as a substitute for ASCAT winds) are adjusted accordingly and compared against SAR dual-polarized winds and collocated Stepped Frequency Microwave Radiometer (SFMR) observations. We find that the bias values of adjusted winds are lower than products from other adjustment schemes by around 5 m/s at the most extreme values. These promising results verify the plausibility of the CMOD7D-v2 adjustment, which is conducive to SAR and ASCAT wind speed comparisons and extreme wind analysis in Tropical Cyclone cases.

Analysis of meteorological and oceanic conditions during freak wave events in the Indian Ocean

Unexpected occurrences of freak waves endanger safe navigation and operational activities in the ocean. To reduce the potential marine accidents, many oceanographers have investigated the probability and mechanism of freak wave occurrences and discussed some relevant environmental factors leading to freak wave generations. However, study on the occurrence of freak waves in the Indian Ocean is very limited. In this study, the existence of freak waves is shown in the north Indian Ocean using observed moored buoy data. Fifty five freak wave events are reported by analyzing available buoy data from the year 2009–2017. All of the events occurred in a combined sea state of swell and wind sea. This study further provides a detailed analysis of the met-ocean conditions, which took place during some selected incidents of the freak wave in the north Indian Ocean. The commonly observed factors during the freak wave events in the north Indian Ocean are cross sea condition, increase in wind speed, rapid development in the sea state and steepness is greater than 0.01. Our analysis supports the fact that swell and wind sea make a coupled system during freak wave events.

Wave power trends along the U.S. coastline: in situ measurements and model hindcast estimates

Observational data are successfully assessed to investigate wave power (wave energy flux per unit of wave-crest) trends within four coastal regions around the US, a parameter that is deemed vital to those responsible for coastal protection and community resilience. This study tests for shifting observational inter-annual wave power trends using a newly developed, unique, United States Army Corps of Engineers Quality Controlled Consistent Measurement Archive, and offers a viable methodology to remove documented observational time series data discontinuations. This study is one of the first to show spatially and temporally comparative observational and model wave power results, providing new information on the accuracy of model wave power estimates, while showcasing in situ wave power trends at 29 sites around the U.S. coastline. Overall, the majority of the eastern Pacific Ocean and Hawaii wave power trends are downward, with mixed slope wave power trends apparent within the Atlantic Ocean and the Gulf of Mexico. Observational and model results are similar with respect to timing, but not magnitude, of wave power peaks in long-term inter-annual trends, with the moored buoy data presenting smaller wave power ranges for two (eastern Pacific Ocean and Hawaii) of the four regions. Additionally, the detection of a noticeable variability in the wave power trend direction within each region suggests that site-specific wave power trends should not be generalised to represent a large region. This work demonstrates that observational data are essential in local and regional wave climate studies to accurately estimate wave power for coastal planners and engineers.

Validation of Sentinel-3A/3B and Jason-3 Altimeter Wind Speeds and Significant Wave Heights Using Buoy and ASCAT Data

This study validated wind speed (WS) and significant wave height (SWH) retrievals from the Sentinel-3A/3B and Jason-3 altimeters for the period of data beginning 31 October 2019 (to 18 September 2019 for Jason-3) using moored buoy data and satellite Meteorological Operational Satellite Program (MetOp-A/B) Advanced Scatterometer (ASCAT) data. The spatial and temporal scales of the collocated data were 25 km and 30 min, respectively. The statistical metrics of root mean square error (RMSE), bias, correlation coefficient (R), and scatter index (SI) were used to validate the WS and SWH accuracy. Validation of WS against moored buoy data indicated errors of 1.19 m/s, 1.13 m/s and 1.29 m/s for Sentinel-3A, Sentinel-3B and Jason-3, respectively. The accuracy of Sentinel-3A/3B WS is better than that of Jason-3. All three altimeters underestimated WS slightly in comparison with the buoy data. Errors in WS at different speeds or SWHs increased slightly as WS or SWH increased. Over time, the accuracy of the Jason-3 altimeter-derived WS improved, whereas that of Sentinel-3A showed no temporal dependence. The WSs of the three altimeters were compared with ASCAT wind data for validation purposes over the global ocean without in situ measurements. On average, the WSs of the three altimeters were lower in comparison with the ASCAT data. The accuracy of the three altimeters was found to be consistent and stable at low/medium speeds but it decreased when the WS exceeded 15 m/s. Validations of SWH against buoy wave data indicated that the accuracy of Jason-3 SWH was better than that of Sentinel-3A/3B. However, the accuracy of all three altimeters decreased when the SWH exceeded 4 m. The accuracy of Sentinel-3A and Jason-3 SWH was temporally stable, whereas that of Sentinel-3B SWH improved over time. Analyses of SWH accuracy as a function of wave period showed that the Jason-3 altimeter was better than the Sentinel-3A/3B altimeters for long-period ocean waves. Generally, the accuracy of WS and SWH data derived by the Sentinel-3A/3B and Jason-3 altimeters satisfies their mission requirements. Overall, the accuracy of WS (SWH) derived by Sentinel-3A/3B (Jason-3) is better than that retrieved by Jason-3 (Sentinel-3A/3B).

Seasonal Mixed Layer Temperature Balance in the Southeastern Tropical Atlantic

AbstractMost climate models simulate sea surface temperatures (SSTs) that are consistently 1–4 °C warmer than observed in eastern tropical Atlantic. These biases undermine seasonal prediction efforts and the credibility of climate change projections in this region. To understand what drives the seasonal cycle in upper ocean temperature near the eastern boundary of the tropical Atlantic, we use a 5‐year moored buoy data set from the Prediction and Research Moored Array in the Atlantic at 6°S, 8°E. The buoy is located along the southeastern edge of the Atlantic cold tongue where the seasonal cycle in SST, which is maximum in March and minimum in August, is influenced by the meridional movement of the Intertropical Convergence Zone (ITCZ) and formation of low‐level marine stratocumulus clouds. Associated with these seasonal changes in atmospheric conditions, surface heat fluxes on seasonal timescales are most strongly controlled by shortwave radiation and latent heat flux. The seasonal mixed layer shoals, warms, and freshens in the boreal spring coincident with a southward migration of the ITCZ. The shallow mixed layer amplifies heating from solar radiation on mixed layer temperature at this time. Conversely, during the boreal summer, upwelling leads to entrainment of cold and salty water into the surface layer. From this analysis, we discuss the relative importance of the different components of the seasonal mixed layer heat balance at 6°S, 8°E and how they can be used to better understand the sources of climate model SST biases.

Sea surface temperature from the new Japanese geostationary meteorological Himawari‐8 satellite

AbstractHimawari‐8 is a new geostationary meteorological satellite operated by the Japan Meteorological Agency (JMA). The Earth Observation Research Center of the Japan Aerospace Exploration Agency in collaboration with the JMA produces skin sea surface temperatures (SSTs) from Himawari‐8 data. A new quasi‐physical algorithm was used to calculate SSTs. Cloud screening based on the Bayesian inference method was used to detect cloudy pixels. Himawari‐8 SSTs from June to September 2015 were compared with drifting and tropical moored buoy data. This comparison showed a root‐mean‐square difference of ∼0.59 K and a bias of ∼−0.16 K against the buoy data. Positive and variable biases were found in seas along the viewing boundaries.

Verification of model wave heights with long-term moored buoy data: Application to wave field over the Indian Ocean

This paper describes the results obtained using a modified version (ModWAM) of the global wave model WAM, in which new parameterizations have been applied based on the seasonal changes and extreme weather events that have occurred in the Indian Ocean. Model significant wave heights (Hs) have been verified using Hs data extracted for the period 2000–2006 from 10 moored data buoys, deployed in the north Indian Ocean. Satellite altimeter Hs has also been used for the model comparison. Based on the error estimates of significant wave heights and spectral wave energy, improvement achieved in wave prediction using ModWAM is demonstrated. We find that the ModWAM improved the accuracy of significant wave height prediction in deep water considerably (rmse of ModWAM is less than that of rmse of WAM), and provided better presentation for high waves that prevailed during southwest monsoon (e.g. Hs, of the order of. 6.0m in June 2005) and extreme weather events (e.g. cyclone that occurred in May 2005), compared to WAM; but, it still underestimates Hs for high waves. Comparison between modeled and measured spectra shows that ModWAM overpredicts spectral energy at low frequencies, and underpredicts at high frequencies.

Propagation of Atlantic Ocean swells in the north Indian Ocean: a case study

Abstract. An analysis of altimeter significant wave height data of May 2007 revealed the occurrence of an extreme weather event off southern tip of South Africa in the Atlantic Ocean, and generation of a series of very high swells at 40° S. These swells propagated towards northeast and broke over La Réunion island in the Indian Ocean on 12 May 2007. The wave model WAVEWATCH III was used to study the propagation of these swells in the Indian Ocean. The model was validated for the Indian Ocean using moored buoy data at 12 locations and merged altimeter wave data. The wave model accurately reproduced the event of May 2007. Swell heights, of the order of 15.0 m, at the generation area reduced to 6.0 m near La Réunion island. This study shows that the swells generated in the Roaring Forties of the Atlantic Ocean (between 15° to 80° E longitude) propagate in the NE/NNE direction towards the north Indian Ocean, and wave characteristics of the Arabian Sea are least influenced compared to that of Bay of Bengal, when swells from the Atlantic Ocean enter the Indian Ocean. The double peak spectrum extracted for the Bay of Bengal indicates that one of the peaks is due to swells generated off southern tip of South Africa.

Operational Wind Wave Prediction System at KMA

An overview of the current operational wind wave prediction system and of new developments at the Korea Meteorological Administration (KMA) is presented. KMA has operated a numerical ocean wave prediction system since 1992. The first major upgrade was done in 1999 with the adaptation of a third-generation wave model (WAM) for both regional (ReWAM-0.25 deg resolution, covering Northeast Asia) and global (GoWAM-1.25 deg resolution) domains. In 2005, the KMA replaced its NEC SX5 computer with a 1024-CPU Cray X1E system, a Parallel Vector Processor (PVP) machine with 128 node modules. A coastal ocean wave prediction system (CoWAM) has been designed and is currently under test mode. Six CoWAM domains, each of 3 deg longitude by 2 deg latitude in size, with a mesh size of 1 km, are nested inside the regional ocean wave prediction system. The directional wave spectra at the boundaries of the CoWAM are provided from a 1/12° resolution upgraded version of the operational ReWAM. The WAVEWATCH-III code (developed at NOAA) is used for the upgraded ReWAM and new CoWAM system. To ensure the required model performance in the Massively Parallel Processor (MPP) architecture of the new supercomputer, a Message Passing Interface (MPI) has been implemented in the model source code. The sea surface wind and significant wave height are verified routinely on a monthly basis. The global moored buoy data (including the coastal buoys operated by KMA) and remote sensing data from Topex/Poseidon, and Jason (retrieved wave heights) and QuikSCAT (retrieved wind data) are used for verification of the wave prediction system. This validation and a comparison of results from the new ReWAM and CoWAM against buoy data off the coast of Korea are discussed.

Comparison of Five Satellite-Derived Latent Heat Flux Products to Moored Buoy Data

Abstract Five satellite products of latent heat flux at the sea surface were compared to bulk fluxes calculated with data from 75 moored buoys, on almost 36 successive months from 1998 to 2000. The five products compared are the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Dataset (HOAPS-2), the Japanese Ocean Flux Datasets with Use of Remote Sensing Observations (J-OFURO), the Jones dataset, the Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF-2), and the Bourras–Eymard–Liu dataset (BEL). The comparisons were performed under tropical and midlatitude environmental conditions, with three datasets based on 66 Tropical Atmosphere–Ocean array (TAO) buoys in the tropical Pacific, nine National Data Buoy Center (NDBC) buoys off the U.S. coasts, and four Met Office/Météo-France (UK–MF) moorings west of the United Kingdom and France, respectively. The satellite products did not all compare well to surface data. However, for each in situ dataset (TAO, NDBC, or UK–MF) at least one satellite product was found that had a good fit to surface data, that is, an rms deviation of 15–30 W m−2. It was found that HOAPS-2, J-OFURO, GSSTF-2, and BEL satellite products had moderate systematic errors with respect to surface data, from −13 to 26 W m−2, and small biases at midlatitudes (6–8 W m−2). Most of the satellite products were able to render the seasonal cycle of the latent heat flux calculated with surface data. The estimation of near-surface specific humidity was found to be problematic in most products, but it was best estimated in the HOAPS-2 product. GSSTF-2 and J-OFURO strongly overestimated the surface flux variations in time and space compared to surface data and to a flux climatology. With respect to TAO data, Jones fluxes yielded good results in terms of rms deviation (27 W m−2) but also presented a large systematic deviation. Overall, for application of the satellite fluxes to the world oceans, it was found that HOAPS-2 was the most appropriate product, whereas for application to the Tropics, BEL fluxes had the best performance in rms with respect to TAO data (24 W m−2).

An analysis of the accuracy of Japanese Ocean Flux data sets with Use of Remote sensing Observations (J‐OFURO) satellite‐derived latent heat flux using moored buoy data

To analyze the accuracy of Japanese Ocean Flux data sets with Use of Remote sensing Observations (J‐OFURO) latent heat flux (LHF), comparisons with moored buoy data are conducted. In the tropical region, J‐OFURO LHF is characterized by large overestimations when buoy LHF has large values. On the other hand, in the midlatitudes, J‐OFURO LHF tends to be underestimated when buoy LHF has large values. We have concluded that these errors are mainly caused by the accuracy of surface air specific humidity. The large scatter between satellite and buoy products is due to the low accuracy of not only specific humidity data but also wind speed data in the midlatitudes. The J‐OFURO LHF bias errors are compared with the bias errors of selected satellite (GSSTF2 and HOAPS1) and reanalysis (NRA1, NRA2, ERA15 and ERA40) data sets.

Resonant inertial oscillations in moored buoy ocean surface winds

The surface winds from the moored buoy data set available from the National Data Buoy Center are examined for the occurrence of inertial range oscillations in the surface winds by employing a novel joint time frequency analysis of the two-component vector winds. This data set was chosen for its high time resolution (1-h sampling) that enabled the full characterization of these inertial oscillations, its at least partially global coverage, and its record length, which is needed to extract the signals from the background noise. The observation system is comprised of over 200 sites with measurements as far back as 1972. Because of the large amount of data, many harmonics of the diurnal atmospheric tidal signature in the surface winds (24, 12, 8, and 3 h) were found in high-precision power spectra. These tidal oscillations are found to be important contributors to the inertial range oscillation at latitudes of 30°, where the largest inertial range oscillations are found. By turning to local (in time) spectral analysis using the S-transform, considerable energy was also found in the inertial frequency ranges at other latitudes, typically in the form of very large (>10 m s—1), yet short-lived (of the order of a few days) events. The surface wind rotation direction in near inertial frequency bands favors resonant inertial oscillations at all latitudes, and in every season of the year.

Resonant inertial oscillations in moored buoy ocean surface winds

The surface winds from the moored buoy data set available from the National Data Buoy Center are examined for the occurrence of inertial range oscillations in the surface winds by employing a novel joint time frequency analysis of the two-component vector winds. This data set was chosen for its high time resolution (1-h sampling) that enabled the full characterization of these inertial oscillations, its at least partially global coverage, and its record length, which is needed to extract the signals from the background noise. The observation system is comprised of over 200 sites with measurements as far back as 1972. Because of the large amount of data, many harmonics of the diurnal atmospheric tidal signature in the surface winds (24, 12, 8, and 3 h) were found in high-precision power spectra. These tidal oscillations are found to be important contributors to the inertial range oscillation at latitudes of 30°, where the largest inertial range oscillations are found. By turning to local (in time) spectral analysis using the S-transform, considerable energy was also found in the inertial frequency ranges at other latitudes, typically in the form of very large (>10 m s—1), yet short-lived (of the order of a few days) events. The surface wind rotation direction in near inertial frequency bands favors resonant inertial oscillations at all latitudes, and in every season of the year.

Variability of Upper Ocean Heat Balance in the Shikoku Basin during the Ocean Mixed Layer Experiment (OMLET)

The heat balance of the surface layer in the vicinity of the former Ocean Weather Station “Tango” (OWS-T; 29°N, 135°E), where a large amount of heat is transported by the Kuroshio and transferred to the atmosphere, was studied by during Ocean Mixed Layer Experiment (OMLET) as an oceanographic component of the Japanese World Climate Research Program (1987–1991). Temperature and velocity in the upper ocean measured using a surface moored buoy system deployed by the Ocean Research Institute, the University of Tokyo, in total 668 days of four time series namely the periods of April 1988–November 1988 (OMELET-88), August 1989–February 1990 (OMLET-89), April 1990–September 1990 (OMLET-901) and September 1990–January 1991 (OMLET-902). We have analyzed the moored buoy data of the upper 100 m for the latter three time series (OMLET-89, -901 and -902) and here we discuss the heat balance of the upper 100 m, in combination with surface heat flux and oceanographic data provided by the Japan Meteorological Agency. A large fluctuation of oceanic heat convergence/divergence of 200–300 W/m2 in amplitude with predominant period of 20–30 days occurred in the first half of OMLET-89 period, which was just the early stage in the formation process of a large meander path of the Kuroshio. A large amount of heat convergence of 71 and 79 W/m2 on average was detected in observation period of OMLET-89 and -901, respectively. During OMLET-902, relatively small heat convergence of 13 W/m2 was obtained. It is suggested that these variations of oceanic heat convergence in this region were closely related to the fluctuation of the Kuroshio axis to the south of Japan.

An Evaluation of Environment Canada's Operational Ocean Wave Model Based on Moored Buoy Data

Abstract An operational ocean wave model called the Canadian Spectral Ocean Wave Model (CSOWM) has been implemented in the operational forecasting system of the Atmospheric Environment Service, Environment Canada, since early 1991. The present operational version of the CSOWM is a first-generation deep-water wave model and is designed to operate over two separate oceanic regions, namely, the northwest Atlantic and the northeast Pacific. The model is run twice daily at the Canadian Meteorological Centre in Montreal and is driven by surface-level winds obtainable from the operational weather prediction models of the CMC. For the Atlantic region, the CSOWM is driven by the 10-m-level winds obtainable from the regional finite-element weather prediction model, while for the Pacific region the CSOWM uses the 1000-mb-level winds obtainable from the global spectral model. The wave model's most important output parameter, namely, the significant wave height, has been evaluated against buoy-measured wave heights av...