• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.183-188
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• 2001

The NOAA AVHRR remotely sensed SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the sea near korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple SST images, all of images must be consistent exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which automatically detects cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$3$^{\circ}C$ as a criterion of SST anomalies). The remotely sensed SST data are tuned by comparing remotely sensed data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel and the SST anomalies are studied by statistical method. It was found that the SST anomalies are rather persistent for one or two months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST. In the Markov lprocess model of SST anomalies, autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. The developed algorithm with automatic cloud pixel detection and rediction of future SST is expected to be incorporated to the operational real time service of SST around Korea.

• Atmosphere
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• v.27 no.3
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• pp.317-329
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• 2017

Ocean mixed layer (OML) depth affects diurnal cycle of sea surface temperature (SST) induced by change of solar radiation absorption and heat budget in ocean. The diurnal SST variation can lead to convection over the ocean, which can impact on localized precipitation both over coastal and inland. In this study, we investigate the OML characteristics affecting the diurnal cycle of SST for the Korean Peninsula and surrounding areas. To analyze OML characteristics, HYCOM oceanic mixed layer depth (MLD) and wind field at 10 m from ERA-interim during 2008~2016 are used. In the winter, MLD is deeply formed when the strong wind field is located on perpendicular to continental slope over deep seafloor areas. Besides, cooling SST-induced vertical mixing in OML is reinforced by dry cold air originated from Siberia. The OML in summer is shallowly distributed about 20 m. In order to estimate the impact of OML model in high resolution NWP model, four experimental simulations are performed. At this time, the prognostic scheme of skin SST is applied in NWP to simulate diurnal SST. The simulation results show that CNTL (off-OML) overestimates diurnal cycle of SST, while EXPs (on-OML) indicate similar results to observations. The prediction performance for precipitation of EXPs shows improvement compared with CNTL over coastal as well as inland. This results suggest that the application of the OML model in summer season can contribute to improving the prediction for performance of SST and precipitation over coastal area and inland.

• 한국해양학회지
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• v.27 no.1
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• pp.35-45
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• 1992

This paper is concerned with the development and application of a stochastic model for predicting E1 nino phenomenon. For this, first a general criterion for determining E1 nino phenomenon, including period and strength, which is based on partial sum of monthly sea surface temperatures (SST) anomalies, is proposed, Secondly, the annual fluctuations, periodicity and dependence of monthly mean of equatorial Pacific SST during the period 1951-1990 are analyzed. Based on these, time series nonlinear regression model for the prediction of SST have been derived. A statistical procedure for using the model to predict the SST have been derived. A statistical procedure for using the model to predict the SST level is also proposed.

• The Journal of Korean Physical Therapy
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• v.23 no.1
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• pp.37-43
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• 2011

Purpose: We studied the changes in motor response time and stop signal response time following visuomotor skill learning of a stop signal task in young healthy subjects. This study also was designed to determine what an effective practice is for different stop signals in the stop signal task (SST). Methods: Forty-five right-handed normal volunteers without a history of neurological dysfunction were recruited. They all gave written informed consent. In all subjects, motor reaction time (RT) and stop signal reaction time (SSRT) were measured for the stop signal task. Tasks were classified into three categories: predictable-stop signal task (P-SST) practice group random-stop signal task (R-SST) practice group control group. Results: Motor reaction time in the P-SST was significantly reduced when comparing pre- and post-tests (p<0.05). Stop signal reaction times in the P-SST and the R-SST were significantly reduced following motor skill learning (p<0.05). Also, the reaction time of the R-SST was shorter than that of the P-SST. Conclusion: These findings indicate that practice of an SST improves motor performance and stop function for some stop signals in the SST. P-SST practice was effective in the stop function of regular movement because of faster of the motor prediction and preparation but the R-SST was effective in the stop function of movements because of faster motor selection.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.454-464
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• 2010

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Ni$\tilde{n}$o-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.

• Atmosphere
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• v.24 no.4
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• pp.533-540
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• 2014

Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this study. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Lee and Seo (2013). To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 2 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and the spring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score reaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.3
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• pp.270-281
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• 2018

Application of the LeMoS hybrid (LH) URANS/LES method for the wake parameters prediction is considered. The wake fraction coefficient is calculated for inland ship model M1926 under shallow water conditions and compared to results of PIV measurements. It was shown that due to lack of the resolved turbulence at the interface between LES and RANS zones the artificial grid induced separations can occur. In order to overcome this drawback, a shielding function is introduced into LH model. The new version of the model is compared to the original one, RANS $k-{\omega}$ SST and SST-IDDES models. It is demonstrated that the proposed modification is robust and capable of wake prediction with satisfactory accuracy.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.20 no.1
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• pp.1-15
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• 2015

Impacts of Sea Surface Temperature (SST) assimilation to the prediction of upper ocean temperature is investigated by using a regional ocean forecasting system, in which 3-dimensional optimal interpolation is applied. In the present study, Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset is adopted for the daily SST assimilation. This study mainly compares two experimental results with (Exp. DA) and without data assimilation (Exp. NoDA). When comparing both results with OSTIA SST data during Sept. 2011, Exp. NoDA shows Root Mean Square Error (RMSE) of about $1.5^{\circ}C$ at 24, 48, 72 forecast hour. On the other hand, Exp. DA yields the relatively lower RMSE of below $0.8^{\circ}C$ at all forecast hour. In particular, RMSE from Exp. DA reaches $0.57^{\circ}C$ at 24 forecast hour, indicating that the assimilation of daily SST (i.e., OSTIA) improves the performance in the early SST prediction. Furthermore, reduction ratio of RMSE in the Exp. DA reaches over 60% in the Yellow and East seas. In order to examine impacts in the shallow costal region, the SST measured by eight moored buoys around Korean peninsula is compared with both experiments. Exp. DA reveals reduction ratio of RMSE over 70% in all season except for summer, showing the contribution of OSTIA assimilation to the short-range prediction in the coastal region. In addition, the effect of SST assimilation in the upper ocean temperature is examined by the comparison with Argo data in the East Sea. The comparison shows that RMSE from Exp. DA is reduced by $1.5^{\circ}C$ up to 100 m depth in winter where vertical mixing is strong. Thus, SST assimilation is found to be efficient also in the upper ocean prediction. However, the temperature below the mixed layer in winter reveals larger difference in Exp. DA, implying that SST assimilation has still a limitation to the prediction of ocean interior.

• Atmosphere
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• v.26 no.4
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• pp.635-647
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• 2016

The effects of sea surface temperature (SST) gradient induced by the previous typhoon on the following typhoon motion over East Asia have been investigated using Weather Research and Forecasting (WRF) model for the previous Typhoon Bolaven (1215) and following Typhoon Tembin (1214). It was observed that Typhoon Bolaven remarkably reduced SST by about $7^{\circ}C$ at Yellow Sea buoy (YSbuoy). Using the WRF experiments for the imposed cold wake over West of Tembin (WT) and over East of Tembin (ET), this study demonstrates that the effects of eastward SST gradient including cold wake over WT is much significant rather than that over ET in relation to unexpected Tembin's eastward deflection. This difference between two experiments is attributed to the fact that cold wake over WT increases the magnitude of SST gradient under the eastward SST gradient around East Asia and the resultant asymmetric flow deflects Typhoon Tembin eastward, which is mainly due to the different atmospheric response to the SST forcing between ET and WT. Therefore, it implies that the enhanced eastward SST gradient over East Asia results in larger typhoon deflection toward the region of warmer SST according to the location of the cold wake effect. This result can contribute to the improvement of track prediction for typhoons influencing the Korean Peninsula

• Nuclear Engineering and Technology
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• v.41 no.7
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• pp.907-920
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• 2009

This study was performed to evaluate the prediction capability of a commercial CFD code and to investigate the effects of different geometries such as a 4.4 mm tube and an 8/10 mm annular channel on the detailed flow structures. A numerical simulation was performed for the conditions, at which the experimental data was produced by the test facility SPHINX. A 2-dimensional axisymmetric steady flow was assumed for computational simplicity. The RNG $\kappa-\varepsilon$ turbulence model (RNG) with an enhanced wall treatment option, SST $\kappa-\omega$ (SST) and low Reynolds Abid turbulence model (ABD) were employed and the numerical predictions were compared with the experimental data generated from the experiment. The effects of the geometry on heat transfer were investigated. The flow and temperature fields were also examined in order to investigate the mechanism of heat transfer near the wall. The local heat transfer coefficient predicted by the RNG model is very close to the measurement result for the tube. In contrast, the local heat transfer coefficient predicted by the SST and ABD models is closer to the measurement for the annular channel.