Search Results (7)

This manuscript presents a self-interferometric phase analysis technique for sea surface observation using a single scatterometer system. The self-interferometric phase is proposed to complement the imprecise analysis results due to the very meager signal strength measured at a high incident angle of more than 30°, which is a vulnerability of the existing analysis method using the Doppler frequency based on the backscattered signal strength. Moreover, compared to conventional interferometry, it is characterized by the phase-based analysis using consecutive signals from a single scatterometer system without any auxiliary system or channel. To apply the interferometric signal process on the moving sea surface observation, it is necessary to secure a reference target; however, this is hard to solve in practice. Hence, we adopted the back-projection algorithm to project the radar signals onto a fixed reference position above the sea surface, where the theoretical model for extracting the self-interferometric phase was derived from the radar-received signal model applying the back-projection algorithm. The observation performance of the proposed method was verified using the raw data collected at the Ieodo Ocean Research Station in Republic of Korea. In the observation result for wind velocity at the high incident angles of 40° and 50°, the self-interferometric phase analysis technique shows a better performance of a correlation coefficient of more than about 0.779 and an RMSE (root-mean-square error) of about 1.69 m/s compared to the existing method of a correlation coefficient of less than 0.62 and RMSE of more than 2.46 m/s. Full article

Many countries worldwide promote artificial reef projects to increase and preserve fishery resources; however, how artificial reefs form fisheries is unclear. Nevertheless, specific hydraulic features of artificial reefs may attract fish. We selected an underwater reef as a research site to clarify this hypothesis. In this study, environmental conditions around the underwater reef were modeled and quantitatively assessed using numerical analysis. We identified two hydraulic features related to fish attraction: the wake region and the local upwelling region. Their spatial distributions were superimposed on the path of a fishing vessel that was monitored using an automatic identification system (AIS). We showed that various hydraulic characteristics (such as wake region, local upwelling region, and flow velocity) identified in the path of the fishing vessel can be quantitatively evaluated. Increasing amounts of information from the AIS can be used to identify the hydraulic features that attract the most fish and therefore improve the productivities of artificial reefs. Full article

Surface solar irradiance (SSI) is a crucial component in climatological and agricultural applications. Because the use of renewable energy is crucial, the importance of SSI has increased. In situ measurements are often used to investigate SSI; however, their availability is limited in spatial coverage. To precisely estimate the distribution of SSI with fine spatiotemporal resolutions, we used the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A) equipped with the Advanced Meteorological Imager (AMI). To obtain an optimal model for estimating hourly SSI around Korea using GK2A/AMI, the convolutional neural network (CNN) model as a machine learning (ML) technique was applied. Through statistical verification, CNN showed a high accuracy, with a root mean square error (RMSE) of 0.180 MJ m⁻², a bias of −0.007 MJ m⁻², and a Pearson’s R of 0.982. The SSI obtained through a ML approach showed an accuracy higher than the GK2A/AMI operational SSI product. The CNN SSI was evaluated by comparing it with the in situ SSI from the Ieodo Ocean Research Station and from flux towers over land; these in situ SSI values were not used for training the model. We investigated the error characteristics of the CNN SSI regarding environmental conditions including local time, solar zenith angle, in situ visibility, and in situ cloud amount. Furthermore, monthly and annual mean daily SSI were calculated for the period from 1 January 2020 to 31 January 2022, and regional characteristics of SSI around Korea were analyzed. This study addressed the availability of satellite-derived SSI to resolve the limitations of in situ measurements. This could play a principal role in climatological and renewable energy applications. Full article

The estimation of extreme ocean wave heights is important for understanding the ocean’s response to long-term changes in the ocean environment and for the effective coastal management of potential disasters in coastal areas. In order to estimate extreme wave height values in the Northwest Pacific Ocean, a 100-year return period were calculated by applying a Peak over Threshold (PoT) method to satellite altimeter SWH data from 1992 to 2016. Satellite altimeter SWH data were validated using in situ measurements from the Ieodo Ocean Research Station (IORS) south of Korea and the Donghae buoy of the Korea Meteorological Administration (KMA) off the eastern coast of Korea. The spatial distribution and seasonal variations of the estimated 100-year return period SWHs in the Northwest Pacific Ocean were presented. To quantitatively analyze the suitability of the PoT method in the Northwest Pacific, where typhoons frequently occur, the estimated 100-year return period SWHs were compared by classifying the regions as containing negligible or significant typhoon effects. Seasonal variations of extreme SWHs within the upper limit of 0.1% and the PoT-based extreme SWHs indicated the effect of typhoons on the high SWHs in the East China Sea and the southern part of the Northwest Pacific during summer and fall. In addition, this study discusses the limitations of satellite altimeter SWH data in the estimation of 100-year extreme SWHs. Full article

To investigate the correlation between underwater noise and sea level, data collected at Ieodo Ocean Research Station (from 15 May to 27 August 2013) were analyzed. Temporal variations in the overall level of underwater noise were similar to those in sea level. The average underwater noise levels at frequencies below 50 Hz were 99.0–106.6 dB during spring tides and 78.4–90.2 dB during neap tides, with a difference of 12.6–20.6 dB. When low-pass filtering at six hours was applied to the absolute value of sea level, a high correlation coefficient of 0.7 for its association with underwater noise was obtained. This strong correlation could indicate flow noise caused by currents. Underwater noise levels around the frequency of 16 Hz were relatively strongly correlated with sea level (correlation coefficient of 0.71). Wavelet analysis of the overall level of underwater noise showed strong periodicity at 0.25, 0.50, 1.0, 16.0, and 30.0 days, as well as characteristics of a tidal constituent. In wavelet coherence analysis, the overall level at a period of 0.25 days exhibited a phase difference of −90 degrees relative to sea level. This finding indicates that the noise level peaks at 1.5 h before the high tide or low tide, which was confirmed in the correlation analysis of noise level and sea height with various time lags. This study shows that the overall level of underwater noise from ebb currents is higher than that from flood currents due to differences in current velocity. Full article

Sea surface salinity (SSS) is an important tracer for monitoring the Changjiang Diluted Water (CDW) extension into Korean coastal regions; however, observing the SSS distribution in near real time is a difficult task. In this study, SSS detection algorithm was developed based on the ocean color measurements by Geostationary Ocean Color Imager (GOCI) in high spatial and temporal resolution using multilayer perceptron neural network (MPNN). Among the various combinations of input parameters, combinations with three to six bands of GOCI remote sensing reflectance (Rrs), sea surface temperature (SST), longitude, and latitude were most appropriate for estimating the SSS. According to model validations with the Soil Moisture Active Passive (SMAP) and Ieodo Ocean Research Station (I-ORS) SSS measurements, the coefficient of determination (R²) were 0.81 and 0.92 and the root mean square errors (RMSEs) were 1.30 psu and 0.30 psu, respectively. In addition, a sensitivity analysis revealed the importance of SST and the red-wavelength spectral signal for estimating the SSS. Finally, hourly estimated SSS images were used to illustrate the hourly CDW distribution. With the model developed in this study, the near real-time SSS distribution in the East China Sea (ECS) can be monitored using GOCI and SST data. Full article

Weather research models have been used to investigate the sensitivity of simulations of Typhoon Tembin (1214) to changes in three horizontal grid spacings and the effect of the cold wake generated by Typhoon Bolaven (1215). We used modified sea surface temperature (SST) to simulate Tembin as it approached after Bolaven had passed through the Ieodo Ocean Research Station and the Yellow Sea buoy in Korea. In the tropical cyclone (TC) tracking experiments, a higher resolution showed the faster and more eastward movement of TCs in all SST conditions. TCs tend to move more eastward at all resolutions particularly when there is a cold wake in their western regions. When there is no cold wake, the intensity of TC is very sensitive to the resolution of the experiment. If a cold wake is maintained on the western and eastern sides, TC intensity is less sensitive to differences in resolution. The precipitation from TCs in the cold wake of the eastern (western) region is lower (higher) than when there is no wake. The TC-generated cold wake significantly affects intensity and movement in cold wake cases in the western region, regardless of horizontal grid, for various reasons. Full article