• Atmosphere
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• v.31 no.2
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• pp.215-227
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• 2021

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.3
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• pp.474-481
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• 2002

Fouling deposits due to surface-coated particles have been calculated using CFD techniques. The sticking probabilities of the surface-coated particles are also evaluated on the basis of an energy balance. The sticking probabilities of the deposit surface are also included in the prediction of the deposition occurring through the multiple impaction of particles with the deposit surface. The sticking probability of an impacting particle is expressed in terms of such parameters as particle viscosity, surface tension, impact velocity, impact angle and the thickness of the sticky layer on a particle. Particulate behavior around a tube in cross flow was studied using the Lagrangian approach. Three important parameters i.e. impact velocity, impact angle, and particulate concentration, were used in the prediction of deposition rate. The computational predictions were found to be in good agreement with the experimental data.

• Journal of the Korean Geotechnical Society
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• v.30 no.11
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• pp.17-23
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• 2014

In this study, the prognostic model for the prediction of the road surface temperature is developed using the surface energy balance theory. This model not only has a detailed micro meteorological physical attribute but also is able to accurately represent each surface energy budget. To verify the performance, the developed model output was compared with the German Weather Service (DWD)'s Energy Balance Model (EBM) output, which is based on the energy budget balance theory, and the observations. The simulated results by using both models are very similar to each other and are compatible with the observed data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

• Proceedings of the Korean Geotechical Society Conference
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• 1997.10a
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• pp.141-150
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• 1997

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 1999.05a
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• pp.30-31
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• 1999

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2000.11a
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• pp.83-84
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• 2000

• Atmosphere
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• v.32 no.4
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• pp.277-295
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• 2022

The global weather prediction model, Korean Integrated Model (KIM), has been in operation since April 2020 by the Korea Meteorological Administration. This study assessed the performance of heat waves (HWs) in Korea in 2020. Case experiments during 2018-2020 were conducted to support the reliability of assessment, and the factors which affect predictability of the HWs were analyzed. Simulated expansion and retreat of the Tibetan High and North Pacific High during the 2020 HW had a good agreement with the analysis. However, the model showed significant cold biases in the maximum surface temperature. It was found that the temperature bias was highly related to underestimation of downward shortwave radiation at surface, which was linked to cloudiness. KIM tended to overestimate nighttime clouds that delayed the dissipation of cloud in the morning, which affected the shortage of downward solar radiation. The vertical profiles of temperature and moisture showed that cold bias and trapped moisture in the lower atmosphere produce favorable conditions for cloud formation over the Yellow Sea, which affected overestimation of cloud in downwind land. Sensitivity test was performed to reduce model bias, which was done by modulating moisture mixing parameter in the boundary layer scheme. Results indicated that the daytime temperature errors were reduced by increase in surface solar irradiance with enhanced cloud dissipation. This study suggested that not only the synoptic features but also the accuracy of low-level temperature and moisture condition played an important role in predicting the maximum temperature during the HWs in medium-range forecasts.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.13-20
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• 2023

In this paper, we propose a water temperature prediction method using feature extraction and reconstructed data based on LSTM-Autoencoder. We used multivariate time series data such as sea surface water temperature in the Naksan area of the East Sea where the cold water zone phenomenon occurred, and wind direction and wind speed that affect water temperature. Using the LSTM-Autoencoder model, we used three types of data: feature data extracted through dimensionality reduction of the original data combined with multivariate data of the original data, reconstructed data, and original data. The three types of data were trained by the LSTM model to predict sea surface water temperature and evaluated the accuracy. As a result, the sea surface water temperature prediction accuracy using feature extraction of LSTM-Autoencoder confirmed the best performance with MAE 0.3652, RMSE 0.5604, MAPE 3.309%. The result of this study are expected to be able to prevent damage from natural disasters by improving the prediction accuracy of sea surface temperature changes rapidly such as the cold water zone.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.80-83
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• 2008

The characteristics of the spread of a forest fire are generally related to the attributes of combustibles, geographical features, and meteorological conditions, such as wind conditions. The most common methodology used to create a prediction model for the spread of forest fires, based on the numerical analysis of the development stages of a forest fire, is an analysis of heat energy transmission by the stage of heat transmission. When a forest fire breaks out, the analysis of the transmission velocity of heat energy is quantifiable by the spread velocity of flame movement through a physical and chemical analysis at every stage of the fire development from flame production and heat transmission to its termination. In this study, the formula used for the 1-dimensional surface forest fire behavior prediction model, derived from a numerical analysis of the surface flame spread rate of solid combustibles, is introduced. The formula for the 1-dimensional surface forest fire behavior prediction model is the estimated equation of the flame spread velocity, depending on the condition of wind velocity on the ground. Experimental and theoretical equations on flame duration, flame height, flame temperature, ignition temperature of surface fuels, etc., has been applied to the device of this formula. As a result of a comparison between the ROS(rate of spread) from this formula and ROSs from various equations of other models or experimental values, a trend suggesting an increasing curved line of the exponent function under 3m/s or less wind velocity condition was identified. As a result of a comparison between experimental values and numerically analyzed values for fallen pine tree leaves, the flame spread velocity reveals has a error of less than 20%.