• Journal of Environmental Science International
• /
• v.18 no.8
• /
• pp.847-855
• /
• 2009

As a part of effort to establish an offshore wind resource assessment system of the Korean Peninsula, a numeric wind simulation using mesoscale climate model MM5 and a spatial distribution of offshore wind extracted from SAR remote-sensing satellite image is compared and analyzed. According to the analyzed results, the numeric wind simulation is found to have wind speed over predication tendency at the coastal sea area. Therefore, it is determined that a high-resolution wind simulation is required for complicated coastal landforms. The two methods are verified as useful ways to identify the spatial distribution of offshore wind by mutual complementation and if the meteor-statistical comparative analysis is performed in the future using adequate number of satellite images, it is expected to derive a general methodology enabling systematic validation and correction of the numeric wind simulation.

• Proceedings of the Korean Society of Marine Engineers Conference
• /
• 2006.06a
• /
• pp.195-196
• /
• 2006

Recently, there are increasing need to make a synthetic assessment about oceanic data which is collected over the various scientific field, in addition to just gathering oceanic data. In this study, we made a basic map using satellite image, aerial photo, multi-beam data, geological stratum data etc. And as well we are producing comprehensive SVT(Scientific Visualization Toolkit) which can visualize various kinds of oceanic data. These oceanic data include both survey data such as tidal height, tide, current, wave, water temperature, salinity, oceanic weather data and numeric modelling results such as ocean hydrodynamic model, wave model, erosion/sediment model, thermal discharged coastal water model, ocean water quality model. In this process, we introduce GIS(Geographic Information System) concepts to reflect time and spatial characteristics of oceanic data.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.22 no.6
• /
• pp.387-396
• /
• 2010

A 3-D numeric model for the confined transient flow under sea ground have been developed. This is FDM model using Gauss-Seidel SOR (successive over-relaxation). This model shows the similar head distribution pattern to Theis analytic solution and MODFLOW simulation. The input flowrate to the aquifer and discharge of well have been compared. And it have been found that mass balance is influenced by the weight factor ${\alpha}$, i.e. fullyimplicit method (${\alpha}$=1) shows 5% error, but when ${\alpha}$ becomes to 0.5(Crank and Nicolson method) the mass balance becomes worse and the model result diverges. And the convergency of the model is not much different when $\lambda$ (over-relaxation factor)=0.8~1.5, but when $\lambda$>1.5, the model result diverges. The test-run shows that the well discharge becomes smaller when another well is near. This model can cover the isotropy$(Kx{\neq}Ky{\neq}Kz)$ and inhomogeneity, and can be used for the selection of well site, discharge calculation, and head prediction in case of the artificial recharge etc.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.30 no.5
• /
• pp.407-414
• /
• 2024

This study analyzed the spatiotemporal errors and limitations of LDAPS-based rainfall forecast data used in coastal ocean numerical models and verified their reliability by comparing them with the rainfall data for 2020 from three rain gauges located around Jinhae Bay, South Korea. The results indicated that although LDAPS-based rainfall data generally reproduced long-term trends, they exhibited significant discrepancies in short-term variations. The quantitative error in annual rainfall was 197.5 mm, which increased to 285.4 mm for summer, indicating that the difference in the cumulative rainfall prediction increases for seasons with high rainfall variability. Furthermore, the rainfall-time predictions exhibited a temporal delay of approximately 8 h, suggesting that temporal errors in the LDAPS-based rainfall data could significantly reduce the accuracy of coastal environment predictions. The indiscriminate use of these data, which fail to accurately reflect coastal rainfall, could lead to serious issues in predicting coastal environmental changes caused by pollutants or extreme rainfall events. Therefore, to appropriately use LDAPS-based rainfall data, it is necessary to improve their accuracy through comprehensive verifications and further refinements.