Moored Buoy Research Articles

Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

Forecasting of ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based on computationally demanding forecasts of three-dimensional ocean currents. Herein, we investigate a complementary approach for shorter time scales by using the recently proposed two-stage implicit equal-weights particle filter applied to a simplified ocean model. To achieve this, we present a new algorithmic design for a data-assimilation system in which all components – including the model, model errors, and particle filter – take advantage of massively parallel compute architectures, such as graphical processing units. Faster computations can enable in-situ and ad-hoc model runs for emergency management, and larger ensembles for better uncertainty quantification. Using a challenging test case with near-realistic chaotic instabilities, we run data-assimilation experiments based on synthetic observations from drifting and moored buoys, and analyze the trajectory forecasts for the drifters. Our results show that even sparse drifter observations are sufficient to significantly improve short-term drift forecasts up to twelve hours. With equidistant moored buoys observing only 0.1% of the state space, the ensemble gives an accurate description of the true state after data assimilation followed by a high-quality probabilistic forecast.

Observed Upper Ocean Seasonal and Intraseasonal Variability in the Andaman Sea

AbstractThe observed seasonal and intraseasonal evolution of near‐surface meteorological and oceanographic variables in the Andaman Sea for the period March 2014 to December 2017 are examined using moored buoy observations at 10.5°N, 94°E. The amplitude of temperature inversions is very weak (0.2 to 0.4 °C), and they appeared primarily during winter (November–January) and latter part of summer (May–August). The net surface heat flux plays a primary role, and vertical processes term contributes secondarily to determine the seasonal mixed layer (ML) heat storage variability. Consistent with the seasonal variations of formation and strength of temperature inversion, vertical processes term shows a positive tendency during winter. The sea surface salinity shows large amplitude intraseasonal variability during fall and winter, and it is attributed to the variability of horizontal circulation in the presence of large lateral sea surface salinity gradients at the mooring location. The sea surface temperature shows the presence of strong intraseasonal variability between 20 and 80 days, though its amplitude of oscillation is distinctly higher during May–October than November–April. Band‐pass filtered (20–80 days) time series of different components of the ML heat budget shows that the net surface heat flux primarily determines the intraseasonal ML heat storage variability. Our analysis further shows that during May–October, both net shortwave radiation and latent heat flux together determine the modulation of the intraseasonal net surface heat flux. In contrast, latent heat flux acts as the sole factor to determine the modulation of the intraseasonal net surface heat flux during November–April.

Retrieval of sea surface salinity from SMAP L‐band radiometer: A novel approach for wind speed correction

AbstractThe present article introduces a new sea surface salinity (SSS) retrieval technique from the Soil Moisture Active Passive (SMAP) L–band (1.4 GHz) microwave radiometer. The ocean surface wind speed (WS) correction, which is very critical in any of the SSS retrieval techniques, is accounted for with a different approach. A model function between ΔSSS (the difference between SSS of flat and rough ocean surfaces) and ocean surface WS is developed and used to correct the impact of ocean surface roughness due to the winds on SSS. In addition to WS correction, the retrieval technique appropriately accounts for the effects of atmospheric attenuation on L‐band frequency due to water vapour and oxygen, and ocean surface temperature on SSS. The performance of the estimations is statistically evaluated by comparing the estimations with the in situ Argo, buoy SSS, and SMAP version 3, 70 km resolution SSS observations in global oceans during 2016. The retrieved salinity well captured the global distribution patterns of SSS, including low and high values. Comparison of retrieved salinity with Argo and SMAP products show a root‐mean‐square error (RMSE) of 0.32 and 0.39 psu, a Pearson correlation coefficient of 0.94 and 0.93, and a scatter index of 0.0093 and 0.011 respectively on a monthly time‐scale. In addition, retrieved SSS promisingly captured day‐to‐day variability in SSS observed by tropical moored buoy SSS with RMSE of 0.36 psu and a Pearson correlation coefficient of 0.94. The validation confirms the potential ability of the new retrieval technique in capturing SSS changes on daily and monthly time‐scales.

Inter-comparison of model, satellite and in situ tropical cyclone heat potential in the North Indian Ocean

The North Indian Ocean (NIO) experiences frequent tropical cyclones (TCs). TC heat potential (TCHP) is a major ocean parameter responsible for TC genesis and intensification changes. In this study, Indian National Centre for Ocean Information Services-Global Ocean Data Assimilation System (INCOIS-GODAS) model and satellite-derived TCHP data from National Remote Sensing Centre (NRSC) and National Oceanic and Atmospheric Administration (NOAA) are validated against TCHP from in situ profiles in the NIO during the period 2011–2013 for buoys and during 2005–2015 for Argo data. Data from eight moored buoys (6 in Bay of Bengal and 2 in Arabian Sea) under the Ocean Moored Buoy Network are used. Comparison of model and in situ TCHP yields correlation coefficients (root-mean-square errors in kJ/cm2) of 0.74 (17.75), 0.59 (15.34), 0.70 (17.68), 0.60 (22.24), 0.57 (19.52), 0.73 (17.88) and 0.77 (39.17) at buoy locations BD08, BD09, BD10, BD11, BD13, AD06 and AD10. The scatter indices between collocated TCHP values at these locations were 0.32, 0.22, 0.30, 0.30, 0.31, 0.58 and 0.41. Further, it was found that satellite-based TCHP from NRSC match better with in situ as compared to near-real-time TCHP data obtained from NOAA. TCHP from INCOIS-GODAS model, NOAA delayed time data and NRSC TCHP data set are also in good agreement with those from Argo profiles. As a case study, model and in situ TCHP were compared during a TC, “Thane” at two buoy locations (BD11 and BD13), closest to its track. The analysis revealed underestimation of model TCHP at BD11, but good correlation at BD13. This could be attributed to the existence of a strong temperature inversion at BD11. It is observed that although the model is able to capture features like barrier and inversion layers, the temperature and depth of such layers are underestimated. Further, the recovery time from the influence of TC on the ocean subsurface is also much longer in case of the model which thus needs to be fine-tuned. Seasonal comparison of TCHP from various sources with in situ estimated TCHP also shows better correlation between all the products for the pre-summer monsoon compared to the post-summer monsoon season.

Joint Detection and Localization of Vessels at Sea With a GNSS-Based Multistatic Radar

This paper addresses the exploitation of global navigation satellite systems as opportunistic sources for the joint detection and localization of vessels at sea in a passive multistatic radar system. A single receiver mounted on a proper platform (e.g., a moored buoy) can collect the signals emitted by multiple navigation satellites and reflected from ship targets of interest. This paper puts forward a single-stage approach to jointly detect and localize the ship targets by making use of long integration times (tens of seconds) and properly exploiting the spatial diversity offered by such a configuration. A proper strategy is defined to form a long-time and multistatic range and Doppler (RD) map, where the total target power can be reinforced with respect to, in turn, the case in which the RD map is obtained over a short dwell and the case in which a single transmitter is employed. The exploitation of both the long integration time and the multiple transmitters can greatly enhance the performance of the system, allowing counteracting the low-power budget provided by the considered sources representing the main bottleneck of this technology. Moreover, the proposed single-stage approach can reach superior detection performance than a conventional two-stage process where peripheral decisions are taken at each bistatic link and subsequently the localization is achieved by multilateration methods. Theoretical and simulated performance analysis is proposed and also validated by means of experimental results considering Galileo transmitters and different types of targets of opportunity in different scenarios. Obtained results prove the effectiveness of the proposed method to provide detection and localization of ship targets of interest.

Accuracy Assessment of HY-2A Scatterometer Wind Measurements During 2011–2017 by Comparison With Buoys, ASCAT, and ERA-Interim Data

Chinese satellite HY-2A, carrying a Ku-band scatterometer (HSCAT), was launched in August 2011. This letter validates wind vectors retrieved by the HSCAT during October 2011–July 2017 by comparing with wind data recorded by global moored buoys, the Advanced Scatterometer (ASCAT), and ERA-Interim. HSCAT wind data are collocated with moored-buoy observations made by the National Data Buoy Center, tropical atmosphere ocean/triangle trans-ocean buoy network, pilot research moored array in the Tropical Atlantic, and research moored array for African–Asian–Australian monsoon analysis and prediction. Only buoys located offshore are selected. Their spatial and temporal differences are limited to 25 km and 30 min. The results show that the HSCAT wind speed has bias of 0.09–0.28 m/s and root-mean-square error (RMSE) of 1.24–1.52 m/s, which satisfies the mission specification of less than 2 m/s. The HSCAT wind direction has bias of 0.8°–0.9° and RMSE of 21.5°–25.8°, which is close to the mission specification of less than 20°. The HSCAT wind retrievals are overestimates at wind speeds lower than 5 m/s. The RMSEs of the HSCAT wind speed and direction decrease with increasing wind speed. Wind vectors from HSCAT and ASCAT are compared using spatial and temporal windows of 0.1° and 10 min. Results show the consistency of the wind speed; RMSEs of wind speed and direction are 1.50 m/s and 22.90°. The comparison of HSCAT and ERS-Interim wind data shows that the HSCAT provides global ocean wind data at the level required for data application.

A 3D parallel particle-in-cell solver for extreme wave interaction with floating bodies

Floating structures are widely used for vessels, offshore platforms, and recently considered for deep water floating offshore wind system and wave energy devices. However, modelling complex wave interactions with floating structures, particularly under extreme conditions, remains an important challenge. Following the three-dimensional (3D) parallel particle-in-cell (PIC) model developed for simulating wave interaction with fixed bodies, this paper further extends the methodology and develops a new 3D parallel PIC model for applications to floating bodies. The PIC model uses both Lagrangian particles and Eulerian grid to solve the incompressible Navier-Stokes equations, attempting to combine both the Lagrangian flexibility for handling large free-surface deformations and Eulerian efficiency in terms of CPU cost. The wave-structure interaction is resolved via inclusion of a Cartesian cut cell method based two-way strong fluid-solid coupling algorithm that is both stable and efficient. The numerical model is validated against 3D experiments of focused wave interaction with a floating moored buoy. Good agreement between the numerical and experimental results has been achieved for the motion of the buoy and the mooring force. Additionally, the PIC model achieves a CPU efficiency of the same magnitude as that of the state-of-the-art OpenFOAM® model for an extreme wave-structure interaction scenario.

Comparison of Oceansat-2 Scatterometer Wind Data with Global Moored Buoys and ASCAT Observation

The Oceansat-2 satellite was launched on 23 September 2009 by the Indian Space Research Organization (ISRO). In this study, the historic archived OSCAT wind vectors are compared with the global moored buoys’ wind observations, including the U.S. National Data Buoy Center (NDBC), the Tropical Atmosphere Ocean (TAO), the Pilot Research Moored Array in the Tropical Atlantic (PIRATA), the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and Advanced Scatterometer (ASCAT) wind data in the same period of OSCAT by calculating the statistical parameters, namely, the root mean square error (RMSE), bias (mean of residuals), and correlation coefficient (R) between the collocated data. The comparisons with the global moored buoys show that the OSCAT wind vectors are consistent with buoys’ wind measurements. The average errors of the OSCAT wind vectors are 1.20 m/s and 17.7°. The analysis of the OSCAT wind vector errors at different buoy wind speeds in bins of 1 m/s indicates that the accuracy of the OSCAT wind speed first increases and then decreases with the increasing wind speed. The comparisons of OSCAT wind vectors and ASCAT wind vectors show that the average RMSEs of their differences are 1.27 m/s and 20.17°. In general, the accuracies of the OSCAT wind vectors satisfy the general scatterometer’s mission requirement and are consistent with ASCAT wind data. OSCAT wind vectors can be used in the global change study by the combination with other scatterometer data.

Transverse motion instability of a submerged moored buoy.

Wave energy converters and other offshore structures may exhibit instability, in which one mode of motion is excited parametrically by motion in another. Here, theoretical results for the transverse motion instability (large sway oscillations perpendicular to the incident wave direction) of a submerged wave energy converter buoy are compared to an extensive experimental dataset. The device is axi-symmetric (resembling a truncated vertical cylinder) and is taut-moored via a single tether. The system is approximately a damped elastic pendulum. Assuming linear hydrodynamics, but retaining nonlinear tether geometry, governing equations are derived in six degrees of freedom. The natural frequencies in surge/sway (the pendulum frequency), heave (the springing motion frequency) and pitch/roll are derived from the linearized equations. When terms of second order in the buoy motions are retained, the sway equation can be written as a Mathieu equation. Careful analysis of 80 regular wave tests reveals a good agreement with the predictions of sub-harmonic (period-doubling) sway instability using the Mathieu equation stability diagram. As wave energy converters operate in real seas, a large number of irregular wave runs is also analysed. The measurements broadly agree with a criterion (derived elsewhere) for determining the presence of the instability in irregular waves, which depends on the level of damping and the amount of parametric excitation at twice the natural frequency.

Large waves and drifting buoys in the Southern Ocean

In February 2017, a wave buoy was deployed in the Southern Ocean, south of New Zealand at 52°45.71′S, 169°02.54′E. For 170 days, the moored buoy transmitted spectral and time domain observations, including measurements during a storm with a particularly large individual wave of 19.4-m crest-to-trough height. We present a detailed analysis of this storm and the measured and modelled spectra, also considering the probability of the occurrence of single large waves. After 170 days, the buoy broke free of its mooring and started drifting eastwards, still transmitting the essential data, thereby providing a unique opportunity to examine large wave events and their corresponding spectral conditions in the Southern Ocean on both moored and free-drifting platforms. The results are discussed quantitatively.

Best Practices for the Ocean Moored Observatories

Real-time spatio-temporal meteorological and oceanographic data from the Ocean moored observatories are essential for precise forecast of ocean state, climate variability studies and reliable weather prediction. Precise spatio-temporal measurement of subsurface parameters such as temperature, salinity and current are essential for understanding the intra-seasonal and inter-annual evolution of the monsoons and tropical cyclones. To cater to this time-critical information, moored observatories have to be continuously operational in the harsh marine environment by measuring these Essential Ocean Variables, though bio-fouling and corrosion limits the life time and accuracy of the highly precise measuring instruments. Thus, best practices in the moored observations are essential for accurate and cost-effective long term ocean observation. The Indian moored buoy network which is operational since 1997, has been providing quality data over the past one decade. This paper describes the best operational practices and quality control processes followed in the Indian moored buoy system design, sensor calibration, testing, integration, deployment, retrieval and data quality control over the past two decades, which has helped to achieving an average meteorological data return of 90 % .

Analysis of drift characteristic in conductivity and temperature sensors used in Moored buoy system

Conductivity and Temperature (CT) sensors are attached in the Ocean Moored buoy network in Northern Indian Ocean (OMNI) at different depths upto 500 m below the sea surface. Drift in these sensors were analyzed based on the result of calibration. For detailed investigation, the sensor's drift is categorized based on the position of sensors in the mooring as surface layer (above 50 m), thermocline layer (50–200 m) and deep layer (200–500 m) and analysis was carried out at both Arabian Sea (AS) and Bay of Bengal BoB. The drift analysis revealed that over the time of deployment the drift in temperature sensor was very minimal and well within the accuracy limit. However, the drift in the conductivity sensors was more significant. Drift in the conductivity sensor decreases as the deployment depth of the sensor increases and also drift in the sensors which were deployed in AS is high as compared to the sensors deployed in BoB. Biofouling of the sensor is measured spatially in AS and BoB and it indicates that biofouling and Abrasive scouring of cells are due to the flow of high concentrations of plankton in the euphotic zone(top layer), which may cause the drift in the conductivity sensor.

A new global gridded sea surface temperature product constructed from infrared and microwave radiometer data using the optimum interpolation method

A new 0.1° gridded daily sea surface temperature (SST) data product is presented covering the years 2003–2015. It is created by fusing satellite SST data retrievals from four microwave (WindSat, AMSR-E, ASMR2 and HY-2A RM) and two infrared (MODIS and AVHRR) radiometers (RMs) based on the optimum interpolation (OI) method. The effect of including HY-2A RM SST data in the fusion product is studied, and the accuracy of the new SST product is determined by various comparisons with moored and drifting buoy measurements. An evaluation using global tropical moored buoy measurements shows that the root mean square error (RMSE) of the new gridded SST product is generally less than 0.5°C. A comparison with US National Data Buoy Center meteorological and oceanographic moored buoy observations shows that the RMSE of the new product is generally less than 0.8°C. A comparison with measurements from drifting buoys shows an RMSE of 0.52–0.69°C. Furthermore, the consistency of the new gridded SST dataset and the Remote Sensing Systems microwave-infrared SST dataset is evaluated, and the result shows that no significant inconsistency exists between these two products.

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.

An observational analysis of the evolution of a mesoscale anti-cyclonic eddy over the Northern Bay of Bengal during May–July 2014

The Bay of Bengal (BoB) is a distinct oceanic region for mesoscale oceanic eddies. The sea level anomaly from the Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) help to identify an unusual anti-cyclonic eddy (ACE) over head BoB during May–July 2014. Two Indian moored buoys (BD08 and BD09) located over this region aided to study the subsurface thermohaline structures of the ACE. Compared to no-eddy environment, the temperature and salinity showed an increment of ~ 3–4 °C and ~ 1–2 PSU, respectively, during the ACE life period. The temperature and depth of the isothermal layer at genesis (peak) stages are increased to ~ 30 °C (~ 30.7 °C) and ~ 20 m (30 m) when compared with no-eddy conditions (28.2 °C and 10 m). The thermocline depth is deepened to 75 m at the peak stage, while it is 50 m in no-eddy condition. A temperature difference of 3 °C between no-eddy and peak stages of ACE is observed up to 50 m. The ocean heat content (OHC) at BD08 (BD09) during genesis and peak stages has increased by ~ 72% (~ 50%) and ~ 247% (~ 181%), respectively, when compared with no-eddy conditions. Moreover, the MOHC also shows a similar increment of ~ 125% (~ 123%) and ~ 258% (~ 284%), respectively. A noticeable influence is seen in turbulent fluxes and lower atmospheric variables during eddy life. This study highlights the capability of moored buoys in understanding the subsurface thermohaline features of the eddies over northern BoB.

Wave hindcast in the North Pacific area considering the propagation of surface disturbances

Ocean surface waves in the North Pacific area are affected by storm winds. It is necessary to consider the movement of storms to predict waves. The impact of a time interpolation method for winds that considers the propagation of surface disturbances on ocean wave prediction from 2005 to 2006 in the North Pacific area is demonstrated. It is possible to interpolate surface winds, even when there are multiple cyclones and anticyclones moving in different directions at different distances. This method will be useful for wind fields with increasing amounts of spatial information. The predicted wave heights and periods from the linearly interpolated winds and the winds predicted using this new method are compared with in-situ observations from several moored buoys. The predicted wave heights are also compared with those from several drifting buoys in the northwestern Pacific. The improvement of the wave height and period prediction is evident in the case where the difference in the predicted wave parameters between the linear interpolation and the present method is large. The improvement of the wave height and period prediction is statistically significant at more than 95% in most cases. It is shown that the wave height and period prediction can be improved by improving the time interpolation method; however, the improvement of the wave direction prediction is not evident.

Evaluation of Interannual Simulations and Indian Ocean Dipole Events During 2000–2014 from a Basin Scale General Circulation Model

Interannual simulations performed using an eddy-permitting ocean general circulation model (OGCM) for years 2000–2014 were validated against buoy measurements over the Indian Ocean (IO). Model-simulated fields were evaluated extensively using multiple statistical metrics to quantify the quality of model in reproducing variability in oceanic surface and subsurface features. The model-simulated sea surface temperature (SST) at different moored buoy locations exhibits high (close to + 0.9) correlation coefficient (R) with the ranges of standard deviation (SD) of simulated SST consistent with the corresponding buoy observations. The root mean square difference (RMSD) estimated between the buoy and simulated SST was found to be less than 0.6 °C at most of the buoy locations. The model-simulated subsurface temperature profile, including the thermocline, resembled good agreement with the buoy profiles. Intraseasonal and interannual variability of 20 °C isotherm (D20) was simulated reasonably well as observed at the respective buoy locations. Mean error in surface currents was low; however, the meridional component from model simulations showed a better agreement (RMSD < 0.25 m/s) with the observations as compared to zonal components (< 0.4 m/s) for the periods of buoy data availability. The Dipole Mode Index (DMI) derived from simulated SST reproduces the positive/negative Indian Ocean Dipole (IOD) events that occurred during the simulation period. Interannual variability in temperature, currents and oceanic mixed layer depth was analyzed in response to IOD events. The anomalies in equatorial currents were found to affect the strength of coastal currents along the Indian coastlines. Model simulations showed the enhanced (suppressed) coastal upwelling process along the Sumatra coast that leads to anomalous cooling (warming) off the Sumatra coast during the positive (negative) IOD events.

An assessment of the impact of surface currents on wave modeling in the Southern Ocean

This paper presents an assessment of the impact of the ocean circulation on modeled wave fields in the Southern Ocean, where a systematic positive bias of the modeled wave height against altimetry data has been reported. The inclusion of ocean currents in the wave model considerably reduces the positive bias of the simulated wave height for high southern latitudes. The decrease of wave energy in the presence of currents is almost exclusively related to the reduction of the relative wind, caused by an overall co-flowing current field associated with the Antarctic Circumpolar Current. Improvements of the model results are also found for the peak period and the mean period against a long-term moored buoy. At the mooring location, the effect of currents is greater for larger and longer waves, suggesting remotely generated swells are more influenced by the currents than local waves. However, an additional qualitative analysis using high-resolution currents in a finer grid nested to the global coarser grid shows that typical resolution of global hydrodynamic reanalysis is not sufficient to resolve mesoscale eddies, and as a consequence, the simulation of mesoscale wave patterns can be compromised. The results are also discussed in terms of the accuracy of forcing fields.

Reliability Metrics From Two Decades of Indian Ocean Moored Buoy Observation Network

AbstractOver the past two decades, the Indian Ocean moored surface buoy observation networks have expanded in the spatiotemporal domain and technologically matured with system reliability, availability, cost optimization, and precision measurement of meteorological and oceanographic parameters as the key targets. Based on the return of experiences in the development, operation, and maintenance of the moored buoy observation networks over two decades, this paper for the first time summarizes the reliability metrics achieved by the National Institute of Ocean Technology (NIOT)-operated moored buoy networks, the healthiness monitoring interval implemented to achieve the highest level of on-demand reliability, the methodologies adopted to ensure highest possible system availability, and data returns. The tsunami buoy network with two buoys and the meteorological buoy with cyclone tracking sensors are found to have a mean time between failure of 1.27 years and 0.5 years, respectively. The identified performances provide a baseline for defining the technical improvement targets, identifying alternate technologies, incorporating redundancies, cost optimization, and maintenance planning. The approach serves as a reference for reliability assessment and integrity management of similar moored buoy ocean observation networks and for evolving standards for oceanographic systems.

Evaluation of ISS-RapidScat Wind Vectors Using Buoys and ASCAT Data

The International Space Station scatterometer (named ISS-RapidScat) was launched by NASA on 20 September 2014 as a continuation of the QuikSCAT climate data record to maintain the availability of Ku-band scatterometer data after the QuikSCAT missions ended. In this study, the overall archived ISS-RapidScat wind vectors in the wind speed range of 0–24 m/s are evaluated by the global moored buoys’ wind observations, including the U.S. National Data Buoy Center (NDBC), the Tropical Atmosphere Ocean (TAO), and the Pilot Research Moored Array in the Tropical Atlantic (PIRATA), the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA), and Advanced Scatterometer (ASCAT) wind data in the same period of ISS-RapidScat by calculating the statistical parameters, namely, the root mean square error (RMSE), bias (mean of residuals), and correlation coefficient (R) between the collocated data. The comparisons with the global moored buoys show that the RapidScat wind vectors are consistent with buoys’ wind measurements. The average errors of the RapidScat wind vectors are 1.42 m/s and 19.5°. The analysis of the RapidScat wind vector errors at different buoy wind speeds in bins of 1 m/s indicates that the errors of the RapidScat wind speed reduce firstly, and then increase with the increasing buoy wind speed, and the errors of the RapidScat wind direction decrease with increasing buoy wind speed. The comparisons of the errors of the RapidScat wind speed and direction at different months from April 2015 to August 2016 show that the accuracies of the RapidScat wind vectors have no dependence on the time, and the biases of the RapidScat wind speed indicate that there is an annual periodic signal of wind speed errors which are due to the annual cycle variation of ocean winds. The accuracies of the RapidScat wind vectors at different times in one day are also analyzed and the results show that the accuracy of the RapidScat wind vectors at different times of the day is basically consistent and with no diurnal variation. In order to evaluate the ISS-RapidScat wind vectors of the global oceans, the differences (RapidScat-ASCAT) in the wind speed range of 0–30 m/s are analyzed in the different months from October 2014 to August 2016, and the average RMSEs of differences between ISS-RapidScat and ASCAT wind vectors are less than 1.15 m/s and 15.21°. In general, the evaluation of the all-over archived ISS-RapidScat wind vectors show that the accuracies of the ISS-RapidScat wind vectors satisfy the general scatterometer’s mission requirement and are consistent with ASCAT wind data.