Buoy Data Research Articles

A Sensitivity Analysis of the Wind Forcing Effect on the Accuracy of Large-Wave Hindcasting

Deployment of wave energy converters (WECs) relies on consistent and accurate wave resource characterization, which is typically achieved through numerical modeling using deterministic wave models. The accurate predictions of large-wave events are critical to the success of wave resource characterization because of the risk on WEC installation, maintenance, and damage caused by extreme sea states. Because wind forcing is the primary driver of wave models, the quality of wind data plays an important role in the accuracy of wave predictions. This study evaluates the sensitivity of large-wave prediction to different wind-forcing products, and identifies a feasible approach to improve wave model results through improved wind forcing. Using a multi-level nested-grid modeling approach, we perform a series of sensitivity tests at four representative National Data Buoy Center buoy locations on the U.S. East and West Coasts. The selected wind-forcing products include the Climate Forecast System Reanalysis global wind product and North American Regional Reanalysis regional wind product as well as the observed wind at the buoys. Sensitivity test results indicate a consistent improvement in model predictions for the large-wave events (e.g., >90th percentile of significant wave height) at all buoys when observed-wind data were used to drive the wave model simulations.

Wind and Wave Extremes from Atmosphere and Wave Model Ensembles

The present work develops an innovative approach to wind speed and significant wave height extreme value analysis. The approach is based on global atmosphere–wave model ensembles, the members of which are propagated in time from the best estimate of the initial state, with slight perturbations to the initial conditions, to estimate the uncertainties connected to model representations of reality. The low correlation of individual ensemble member forecasts at advanced lead times guarantees their independence and allows us to perform inference statistics. The advantage of ensemble probabilistic forecasts is that it is possible to synthesize an equivalent dataset of duration far longer than the simulation period. This allows the use of direct inference statistics to obtain extreme value estimates. A short time series of six years (from 2010 to 2016) of ensemble forecasts is selected to avoid major changes to the model physics and resolution and thus ensure stationarity. This time series is used to undertake extreme value analysis. The study estimates global wind speed and wave height return periods by selecting peaks from ensemble forecasts from +216- to +240-h lead time from the operational ensemble forecast dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with extreme value analyses performed on a commonly used reanalysis dataset, ERA-Interim, and buoy data. The comparison with traditional methods demonstrates the potential of this novel approach for statistical analysis of significant wave height and wind speed ocean extremes at the global scale.

An EC-Earth coupled atmosphere–ocean single-column model (AOSCM.v1_EC-Earth3) for studying coupled marine and polar processes

Abstract. Single-column models (SCMs) have been used as tools to help develop numerical weather prediction and global climate models for several decades. SCMs decouple small-scale processes from large-scale forcing, which allows the testing of physical parameterisations in a controlled environment with reduced computational cost. Typically, either the ocean, sea ice or atmosphere is fully modelled and assumptions have to be made regarding the boundary conditions from other subsystems, adding a potential source of error. Here, we present a fully coupled atmosphere–ocean SCM (AOSCM), which is based on the global climate model EC-Earth3. The initial configuration of the AOSCM consists of the Nucleus for European Modelling of the Ocean (NEMO3.6) (ocean), the Louvain-la-Neuve Sea Ice Model (LIM3) (sea ice), the Open Integrated Forecasting System (OpenIFS) cycle 40r1 (atmosphere), and OASIS3-MCT (coupler). Results from the AOSCM are presented at three locations: the tropical Atlantic, the midlatitude Pacific and the Arctic. At all three locations, in situ observations are available for comparison. We find that the coupled AOSCM can capture the observed atmospheric and oceanic evolution based on comparisons with buoy data, soundings and ship-based observations. The model evolution is sensitive to the initial conditions and forcing data imposed on the column. Comparing coupled and uncoupled configurations of the model can help disentangle model feedbacks. We demonstrate that the AOSCM in the current set-up is a valuable tool to advance our understanding in marine and polar boundary layer processes and the interactions between the individual components of the system (atmosphere, sea ice and ocean).

Using sentinel-1A SAR wind retrievals for enhancing scatterometer and radiometer regional wind analyses

ABSTRACTScatterometer surface wind speed and direction observations in combination with radiometer wind speeds allow to generate surface wind analyses with high space and time resolutions over global as well as at regional scales. Regarding scatterometer sampling schemes and physics, the resulting surface wind analyses suffer from lack of accuracy in areas near coasts. The use of the synthetic aperture radar (SAR) onboard the Sentinel-1A satellite attempts to address the enhancement of surface wind analyses issues. In this study, SAR wind speeds and directions retrieved from backscatter coefficients acquired in interferometric wide (IW) swath mode are used. Their accuracy is determined through comprehensive comparisons with moored buoy wind measurements. SAR and buoy winds agree well at offshore and nearshore locations. The statistics characterizing the comparison of SAR and buoy wind speeds and directions are of the same order as those obtained from scatterometer (Advanced SCATterometer (ASCAT) and RapidScat) and buoy wind comparisons. The main discrepancy between SAR and buoy data are found for high wind speeds. SAR wind speeds exceeding 10 m s–1 tend to be underestimated. A similar conclusion is drawn from SAR and scatterometer wind speed comparisons. It is based on the underestimation of SAR backscatter coefficient (σ°) with respect to σ° estimated from scatterometer winds and the geophysical model function (GMF) named CMOD-IFR2 (Ifremer C band MODel). New SAR wind speeds are retrieved using CMOD-IFR2. The corrected SAR retrievals allow better determination of the spatial characteristics of surface wind speeds and of the related wind components in near-coast areas. They are used for enhancing the determination of the spatial structure function required for the estimation of wind fields gridded in space and time at the regional scale. The resulting wind fields are only determined from scatterometer wind observations in combination with radiometer retrievals. Their qualities are determined through comparisons with SAR wind speeds and directions, and through their application for determination of wind power off Brittany coasts.

Typhoon/Hurricane-Generated Wind Waves Inferred from SAR Imagery

The wide-swath mode of synthetic aperture radar (SAR) is a good way of detecting typhoon/hurricane winds with a cross-polarization mode. However, its ability to detect wind waves is restricted because of its spatial resolution and nonlinear imaging mechanisms. In this study, we use the SAR-retrieved wind speed, Sentinel-1 SAR wave mode and buoy data to examine fetch- and duration-limited parametric models (denoted H-models), to estimate the wave parameters (significant wave height Hs, dominant wave period Tp) generated by hurricanes or typhoons. Three sets of H-models, in total 6 models, are involved: The H-3Sec model simulates the wave parameters in 3 sections of a given storm (right, left and back); H-LUT models, including the H-LUTI model and H-LUTB model, provide a better resolution of the azimuthal estimation of wind waves inside the storm by analyzing the dataset from Bonnie 1998 and Ivan 2004; and the third set of models is called the H-Harm models, which consider the effects of the radius of the maximum wind speed rm on the wave simulation. In the case of typhoon Krovanh, the comparison with wave-mode measurements shows that the duration-limited models underestimate the high values for the wind-wave Hs, while the fetch models’ results are more accurate, especially for the H-LUTI model. By analyzing 86 SAR wave mode images, it is found that the H-LUTI model is the best among the 6 H-models, and can effectively simulate the wind-wave Hs, except in the center area of the typhoon; root mean square errors (rmse) can reach 0.88 m, and the coefficient correlation (R2) is 0.86. The H-Harm models add rm as an additional factor to be considered, but this does not add significant improvement in performance compared to the others. This limitation is probably due to the fact that the data sets used to develop the H-Harm models have only a limited coverage range, with respect to rm. Applying H-models to RADARSAT-2 ScanSAR mode data, we compare the retrieved wave parameters to collected buoy measurements, showing good consistency. The H-LUTI model, using a fetch-limited function, does the best among these 6 H-models, whose rmse and R2 are 0.86 m and 0.77 for Hs, and 1.06 s and 0.76 for Tp, respectively. Results indicate the potential for H-models to simulate waves generated by typhoons/hurricanes.

Development of a warning model for coastal freak wave occurrences using an artificial neural network

The potential for coastal freak waves (CFWs) represents a threat to people living in coastal areas. CFWs are generated via the evolution of a wave and its interactions with coastal structures or rocks; however, the exact mechanism of their formation is not clear. Here, a data-driven warning model based on an artificial neural network (ANN) is proposed to predict the possibility of CFW occurrence. Seven parameters (significant wave height, peak period, wind speed, wave groupiness factor, Benjamin Feir Index (BFI), kurtosis, and wind-wave direction misalignment) collected prior to the occurrence of the CFW are used to develop the model. The buoy data associated with 40 known CFW events are used for model training, and the data associated with 23 such events are used for validation. The use of data obtained during the 6-h period prior to CFW occurrence combined with the same amount of non-CFW data is shown to produce the best model. Two validations using media-published and camera-recorded CFW events show that the accuracy rate (ACR) exceeds 90% and the recall rate (RCR) exceeds 87%, demonstrating the accuracy of the proposed model. This warning model has been implemented in operational runs since 2016.

Thermal stratification dynamics in a large and deep subtropical reservoir revealed by high-frequency buoy data

We measure the thermal stratification dynamics in Lake Qiandaohu, China, a deep subtropical reservoir, to better understand the mixing mechanism and its response to lake warming. A high-frequency monitoring buoy dataset from February 2016 to October 2017 is used to evaluate variations in the water temperature profile, Schmidt stability (SS) and thermocline parameters, such as the thermocline depth (TD), bottom depth (TB), thickness (TT), and strength (TS), and elucidate the potential effects of thermal stratification on the lake's ecosystem. High-frequency observation data demonstrate that the lake's thermal-stratification cycle can be divided into three stages: formation, stationary and weakening periods. Consequently, a significant positive correlation between the TB and TT during the formation period and a significant negative correlation between the TD and TT are found during the stationary and weakening periods. Additionally, strong positive correlations exist among the TS, TT and SS for all the data. Our data indicated that an increase in the air temperature caused the surface water temperature, TT, TS and SS to increase. Furthermore, thermal stratification affected the vertical distribution of dissolved oxygen and expanded the area of the hypoxic-anoxic zone. The incomplete mixing of the water from December 2016 to February 2017 because of the high air temperature, which was 2.49 °C higher than the mean air temperature of 1966–2015 (6.44 °C), created the hypoxia hypolimnion from March to May 2017. Under the background of global warming, the thermal stratification of Lake Qiandaohu will likely intensify and further significantly affect the lake's ecosystem.

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.

Evaluation of Satellite Surface Winds in Relation to Weather Regimes Over the Indian Ocean Using DYNAMO Observations

AbstractSatellites provide the most extensive surface wind data over the tropical oceans, but sampling limitations, rain contamination, and algorithm uncertainties remain. In this study, scatterometer and passive microwave swath data, and multisatellite products are evaluated using Dynamics of the Madden‐Julian Oscillation (DYNAMO) ship and moored buoy wind speed data over the Indian Ocean from September 2011 to January 2012. Each of the satellite products has <2 m/s error, consistent with previous studies. Blended products have error and bias characteristics comparable with swath data and provide better coverage. The errors and biases depend on the prevailing weather regime. The equatorial region is characterized by weeks of winds <5 m/s with little rain punctuated by episodes of winds >10 m/s and heavy rainfall associated with MJO events. The blended products resolve the episodic enhanced equatorial winds associated with the active phase of the MJO. However, due to limited sampling and rain contamination, the swath data did not fully resolve the peak MJO winds. In the Southern Hemisphere trade wind regime, rain contamination is less of a factor and winds are steadier. Satellite wind estimates of equatorial (trade wind) regime tended to be biased low (high). The surface wind products underestimate higher winds during the active MJO due to limited sampling and have high wind bias in the trade wind regime. These weather regime‐dependent biases should be taken into account for applications using these wind products for ocean circulation studies and estimating air‐sea fluxes over the Indian Ocean.

Wave spectrum retrieval from airborne sunglitter images

Reconstruction and evolution of two-dimensional spectra of surface waves in the Gulf of Mexico are derived from airborne sun-glitter imagery. As the proposed method is based on a linear transfer function deduced from the shape of the sunglitter brightness, the absolute wavenumber elevation spectrum does not require any additional assumption or information about sky brightness, wind or wave energy. The detailed description of the airborne image processing method is given. As demonstrated, retrieved spectra agree well with nearby NDBC buoy data, both for spectrum shape, level and energy angular distribution. The 180-degree wave direction ambiguity, inherent to image-derived spectra, is eliminated by using cross-correlation analysis between two consecutive images. A case study corresponding to the spectral evolution with increasing distance from shore in slanting-fetch conditions is then considered. Energy level and peak position transformation are consistent with established approximations and laws of wind-sea development. The technical requirements (flight altitude, image resolution, view angles, etc) and applicability of the suggested methodology are also discussed. These results demonstrate the potential efficiency of high resolution sea state monitoring from drones or light aircrafts using sunglitter imagery.

Spectral Modelling on the Characteristics of High Frequency Tail in Shallow Water Wave Spectra at Coastal Puducherry, East Coast of India

About 8 years of measured wave spectra (June 2007–December 2014) off coastal Puducherry located in the east coast of India and bordering the Bay of Bengal were analyzed with an objective to understand the slope of the high frequency tail of the wave spectrum and to determine the occurrence of single peaked, double-peaked and multi-peaked spectra in varying sea states. The temporal and inter-annual variation of the spectral energy density over the years indicates marked variability and the study signifies that wave spectra were multi-peaked from June to October and predominantly double peaked during the rest of the year. Swell and wind sea components have been estimated from the wave spectra by separation frequency method. The analysis shows that swells dominate Puducherry coastal region not only during southwest monsoon (95%), but also during the post-monsoon (100%) and northeast monsoon season. The measured wave spectra were compared with numerical wave model outputs to attain a level of confidence with the buoy data. In addition, analysis on the slope of the high frequency tail of the wave energy spectra shows that its slope varied seasonally in the range of − 1.96 to − 3.27 at the coastal location. Further, the JONSWAP model fitted into measured wave spectra showed high discrepancy between the two, especially in the high frequency tail with Scatter Index ranging between 0.79 and 3.98. The correct slope for the high frequency or even whether a unique slope exists remains elusive for the ocean wave community.

APPLICATION OF SWAN MODEL FOR HINDCASTING WAVE HEIGHT IN JEPARA COASTAL WATERS, NORTH JAVA, INDONESIA

SWAN (Simulating Wave Near-shore) is a numerical wave model for hindcasting/forecastingwave parameters in coastal areas. This numerical model is chosen because is suitable for shallow water. Thisstudy was conducted to verify the results of wave height hindcasting in Jepara coastal waters. This is expectedto support wave characteristic research based on wave forecasting for 10 years in the waters between Java,Sumatera and Kalimantan. The model is run with the third-generation mode (GEN3), which allow wind input,quadruplet and triad interactions, whitecapping, and breaking. Wind data is obtained from ECMWF (EuropeanCentre for Medium-Range Weather Forecasts) and the bathymetry from GEBCO (General Bathymetric Chartof The Oceans). The validation of the model and buoy data during July - December 1993 shows a good result(Root Mean Square Error = 0.166 and correlation/ linear regression = 0.807). Based on the literature,qualitatively the model has been verified with other simulation from another model in the same location.

Attenuation and Directional Spreading of Ocean Waves During a Storm Event in the Autumn Beaufort Sea Marginal Ice Zone

AbstractThis paper investigates the attenuation and directional spreading of large amplitude waves traveling through pancake ice. Directional spectral density is analyzed from in situ wave buoy data collected during a 3‐day storm event in October 2015 in the Beaufort Sea. Two proxy metrics for wave amplitude obtained from energy density spectra, namely, spectral amplitude and significant wave height, are used to track the waves as they propagate along transects through the array of buoys in the predominantly pancake ice field. Two types of wave buoys are used in the analysis and compared, exhibiting significant differences in the wave energy density and directionality estimates. Although exponential decay is observed predominantly, one of the two buoy types indicates a potential positive correlation between wave energy density and the occurrence of linear wave decay, as opposed to exponential decay, in accord with recent observations in the Antarctic marginal ice zone. Factors affecting the validity of this observation are discussed. An empirical power law with exponent 2.2 is also found to hold between the exponential attenuation coefficient and wave frequency. The directional content of the wave spectrum appears to decrease consistently along the wave transects, confirming that wave energy is being dissipated by the pancake ice as opposed to being scattered by ice cakes.

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.

Power spectral density analysis of ocean wave by using GPS buoy

Wave buoy equipped with a mechanical or electrical sensor have been used extensively to observe waves around the world but the integration of GPS technology with wave buoy (GPS buoy) only has been explore usefulness in this field in the early 90's. This study presents the capabilities of GPS buoy to observe wave data at Strait of Malacca by using high precision kinematic positioning approach. The GPS buoy data obtained from this observation were processed through a precise, medium-range differential kinematic technique. These data were observed at a high rate (1 Hz) interval over a period of more than 24 hours from a nearby coastal site. The kinematic coordinates of the GPS buoy were estimated via epoch-wise pre-elimination and the backward substitution algorithm. These kinematic coordinates are used to calculate the magnitude of the Power Spectral Density (PSD). PSD analysis function able to shows the strength of the variations (energy) as a function of frequency. The tidal changes and monsoon wind has been found to greatly influence the wave energy as shown in the PSD analysis. From the test result, GPS buoy and data processing technique promises a total solution as a complete ocean wave monitoring solution for Strait of Malacca.

THREE-WAY ERROR ANALYSIS OF SEA SURFACE TEMPERATURE (SST) BETWEEN HIMAWARI-8, BUOY, AND MUR SST IN SAVU SEA

Variance errors of Himawari-8, buoy, and Multi-scale Ultra-high Resolution (MUR) SST in Savu Sea have been investigated. This research used level 3 Himawari-8 hourly SST, in situ measurement of buoy, and daily MUR SST in the period of December 2016 to July 2017. The data were separated into day time data and night time. Skin temperature of Himawari-8 and subskin tempertaure of MUR SST were corrected with the value of 15∆Tdept"> before compared with buoy data. Hourly SST of Himawari-8 and buoy data were converted to daily format by averaging process before collocated with MUR SST data. The number of 2,264 matchup data are obtained. Differences average between Himawari-8, buoy and MUR SST were calculated to get the value of variance (Vij). Using three-way error analysis, variance errors of each observation type can be known. From the analysis results can be seen that the variance error of Himawari-8, buoy and MUR SST are 2.5 oC, 0.28oC and 1.21oC respectively. The accuracy of buoy data was better than the other. With a small variance errors, thus buoy data can be used as a reference data for validation of SST from different observation type.

Analysis and Optimization on the Flow Ability of Wave Buoy Based on AQWA

Wave buoy is a modern ocean observation facility, and the ability of following wave is an important parameter to reflect the measurement accuracy. This article will focus on the study of hydrodynamics on the ability of three common appearance buoys. By using the hydrodynamic calculation software AQWA, this article will study the heaving response of free buoy in regular wave under frequency domain, it combined with the typical Green function method for solving the wave force and the related hydrodynamic coefficients of floating structure, and it applied the three dimensional potential flow theory and based on the single degree of freedom motion equation. According to the features of short period wave in Chinese sea states, this paper will calculate the heaving response RAOs of 25 buoys on different shapes, weight and shape parameters. Hydrodynamics calculation results show that: under the same conditions in the environment load, the flow-ability of ball float 3-2 has better performance than other buoys in working condition. The research methods of this article has provide good scientific basis for the parameter design on ocean data buoy.

Accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America): implications for estimation of cyanobacterial bloom parameters from water quality sonde measurements.

Microcystin (MCY)-producing harmful cyanobacterial blooms (cHABs) are an annual occurrence in Lake Erie, and buoys equipped with water quality sondes have been deployed to help researchers and resource managers track cHABs. The objective of this study was to determine how well water quality sondes attached to buoys measure total algae and cyanobacterial biomass and water turbidity. Water samples were collected next to two data buoys in western Lake Erie (near Gibraltar Island and in the Sandusky subbasin) throughout summers 2015, 2016, and 2017 to determine correlations between buoy sonde data and water sample data. MCY and nutrient concentrations were also measured. Significant (P < 0.001) linear relationships (R2 > 0.75) occurred between cyanobacteria buoy and water sample data at the Gibraltar buoy, but not at the Sandusky buoy; however, the coefficients at the Gibraltar buoy differed significantly across years. There was a significant correlation between buoy and water sample total chlorophyll data at both buoys, but the coefficient varied considerably between buoys and among years. Total MCY concentrations at the Gibraltar buoy followed similar temporal patterns as buoy and water sample cyanobacterial biomass data, and the ratio of MCY to cyanobacteria-chlorophyll decreased with decreased ambient nitrate concentrations. These results suggest that buoy data are difficult to compare across time and space. Additionally, the inclusion of nitrate concentration data can lead to more robust predictions on the relative toxicity of blooms. Overall, deployed buoys with sondes that are routinely cleaned and calibrated can track relative cyanobacteria abundance and be used as an early warning system for potentially toxic blooms.

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.

Ocean Wave Information Retrieval Using Simulated Compact Polarized SAR from Radarsat-2

The main objective of this paper is to demonstrate the capability of compact polarized (CP) synthetic aperture radar (SAR) to retrieve ocean wave field parameters. Souyris’ and Nord’s algorithms are used to carry out the reconstruction of CP SAR pseudo quad-polarized data for the ocean surface under both the circular transmit linear receive (CTLR) and π/4 mode. The results show that, for the CP reconstruction, Nord’s algorithm has a better convergence ability than Souyris’. In addition, the investigation of the reconstruction accuracy shows that the CTLR mode is superior to the π/4 mode, in terms of ocean surface reconstruction. It is, therefore, concluded that the reconstructed parameters of CP CTLR mode data by Nord’s algorithm adapt to retrieve ocean wave information. The ocean wave slope spectrum and other main wave parameters are also calculated from reconstructed CP data and compared with measurements from in situ National Data Buoy Center (NDBC) matched-up buoys. Comparison of CP SAR-based wave field information with buoy outputs also shows good agreement in the case of dominate wave height, wave direction, and wave period, with biases of 0.36 m, 17.96°, and 0.88 s, respectively.