• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.11-16
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• 2015

Spatial and temporal information on near-surface air temperature is important for understanding global warming and climate change. In this study, the estimation algorithm of near-surface air temperature in Korea was developed by using spatial homogeneous surface information obtained from satellite remote sensing observations. Based on LST(Land Surface Temperature), NDWI(Normalized Difference Water Index) and NDVI(Normalized Difference Vegetation Index) as independent variables, the multiple regression model was proposed for the estimation of near-surface air temperature. The different regression constants and coefficients for warm and cold seasons were calculated for considering regional climate change in Korea. The near-surface air temperature values estimated from the multiple regression algorithm showed reasonable performance for both warm and cold seasons with respect to observed values (approximately $3^{\circ}C$ root mean-square error and nearly zero mean bias). Thus;the proposed algorithm using remotely sensed surface observations and the approach based on the classified warm and cold seasons may be useful for assessment of regional climate temperature in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.299-303
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• 2008

In this study, an uncertainty assessment for surface air temperature(T2m) and precipitation(PCP) over East Asia is carried out. The data simulated by the intergovermental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Atmosphere-Ocean coupled general circulation Model (AOGCM) are used to assess the uncertainty. Examination of the seasonal uncertainty of T2m and PCP variabilities shows that spring-summer cold bias and fall warm bias of T2m are found over both East Asia and the Korea peninsula. In contrast, distinctly summer dry bias and winter-spring wet bias of PCP over the Korea peninsula is found. To investigate the PCP seasonal variability over East Asia, the cyclostationary empirical orthogonal function(CSEOF) analysis is employed. The CSEOF analysis can extract physical modes (spatio-temporal patterns) and their undulation (PC time series) of PCP, showing the evolution of PCP. A comparison between spatio-temporal patterns of observed and modeled PCP anomalies shows that positive PCP anomalies located in northeastern China (north of Korea) of the multi-model ensemble(MME) cannot explain properly the contribution to summer monsoon rainfalls across Korea and Japan. The uncertainty of modeled PCP indicates that there is disagreement between observed and MME anomalies. The spatio-temporal deviation of the PCP is significantly associated with lower- and upper-level circulations. In particular, lower-level moisture transports from the warm pool of the western Pacific and corresponding moisture convergence significantly contribute to summer rainfalls. These lower- and upper-level circulations physically consistent with PCP give a insight of the reason why differences between modeled and observed PCP occur.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.5
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• pp.103-113
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• 1996

The properties of the deposited film depend on the deposition condition and these, in turn depend critically on the morphology and crystal orientation of the films. Therefore, it is important to clarify the nucleation occurrence and growth stage of the morphology and orientation of the film affected by deposition parameters, e.g. the gas pressure and bias voltage etc. In this work, magnesium thin flims were prepared on cold-rolled steel substrates by a thermo-eletron activation ion plating technique. The influence of nitrogen gas pressure and substrate bias voltage on their crystal orientation and morphology of the coated films were investigated by scanning electron microscopy (SEM) and X-ray diffraction, respectively. The diffraction peaks of magnesium film became less sharp and broadened with the increase of nitrogen gas pressure. With an increase in nitrogen gas pressure, flim morphology changed from colum nar to granular structure, and surface crystal grain-size decreased. The morphology of films depended not only on gas pressure but also on bias voltage, i.e., the effect of increasing bias voltage was similar to that of decreasing gas pressure. The effect of crystal orientation and morphology of magnesium films on corrosion behaviors was estimated by measuring anodic polarization curves in deaerated 3%NaCl solution. Magnesium, in general, has not a good corrosion resistance in all environments. However, these magnesium films prepared by changing nitrogen gas pressure showed good corrosion resistance. Among the films, magnesium films which exhibited granular structure had the highest corrosion resistance. The above phenomena can be explained by applying the effects of adsorption, occlusion and ion sputter of nitrogen gas.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.6
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• pp.615-631
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• 2012

This study provides an evaluation for capability of Integrated Climate and Air quality Modeling System (ICAMS) on future regional scale climate projection. Temperature and precipitation are compared between ground-level observation data and results of regional models (MM5) for the past 30 years over the Korean peninsula. The ICAMS successfully simulates the local-scale spatial/seasonal variation of the temperature and precipitation. The probability distribution of simulated daily mean and minimum temperature agree well with the observed patterns and trends, although mean temperature shows a little cold bias about $1^{\circ}C$ compared to observations. It seems that a systematic cold bias is mostly due to an underestimation of maximum temperature. In the case of precipitation, the rainfall in winter and light rainfall are remarkably simulated well, but summer precipitation is underestimated in the heavy rainfall phenomena of exceeding 20 mm/day. The ICAMS shows a tendency to overestimate the number of washout days about 7%. Those results of this study indicate that the performance of ICAMS is reasonable regarding to air quality predication over the Korean peninsula.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.901-911
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• 2005

With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.

• Atmosphere
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• v.23 no.4
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• pp.527-538
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• 2013

In this study, climate over Korea based on the Historical scenario induced by HadGEM2-AO is simulated by WRF. For this purpose, a system that can be used be for numerical integration over the Far East Asian area of the center of the Korean Peninsula with 12.5 km-horizontal resolution was set-up at "Haebit", the early portion of KMA Supercomputer Unit-3. Using the system, the downscaling experiments were conducted for the period 1979-2010. The simulated results of HadGEM2-AO and WRF are presented in terms of 2 m-temperature and precipitation during boreal summer and winter of Historical for the period 1981~2005, compared with observation. As for the mean 2 m-temperature, the general patterns of HadGEM2-AO and WRF are similar with observation although WRF showed lower values than observation due to the systematic bias. WRF reproduced a feature of the terrain-following characteristics reasonably well owing to the increased horizontal resolution. Both of the models simulated the observed precipitation pattern for DJF than JJA reasonably, while the rainfall over the Korean Peninsula in JJA is less than observation. HadGEM2-AO in DJF 2 m-temperature and JJA precipitation has warm and dry biases over the Korean Peninsula, respectively. WRF showed cold bias over JJA 2 m-temperature and wet bias over DJF precipitation. The larger bias in WRF was attributed to the addition of HadGEM2-AO's bias to WRF's systematic bias. Spatial correlation analysis revealed that HadGEM2-AO and WRF had above 0.8 correlation coefficients except for JJA precipitation. In the EOF analysis, both models results explained basically same phase changes and variation as observation. Despite the difference in mean and bias fields for both models, the variabilities of the two models were almost similar with observation in many respects, implying that the downscaled results can be effectively used for the study of regional climate around the Korean Peninsula.

• Atmosphere
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• v.17 no.1
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• pp.55-68
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• 2007

Future climate changes over East Asia are projected by anthropogenic forcing of greenhouse gases and aerosols using ECHO-G/S (ECHAM4/HOPE-G). Climate simulation in the 21st century is conducted with three standard SRES scenarios (A1B, B1, and A2) and the model performance is assessed by the 20th Century (20C3M) experiment. From the present climate simulation (20C3M), the model reproduced reliable climate state in the most fields, however, cold bias in temperature and dry bias of summer in precipitation occurred. The intercomparison among models using Taylor diagram indicates that ECHO-G/S exhibits smaller mean bias and higher pattern correlation than other nine AOGCMs. Based on SRES scenarios, East Asia will experience warmer and wetter climate in the coming 21st century. Changes of geographical patterns from the present to the future are considerably similar through all the scenarios except for the magnitude difference. The temperature in winter and precipitation in summer show remarkable increase. In spite of the large uncertainty in simulating precipitation by regional scale, we found that the summer (winter) precipitation at eastern coast (north of $40^{\circ}N$) of East Asia has significantly increased. In the 21st century, the warming over the continents of East Asia showed much more increase than that over the ocean. Hence, more enhanced (weakened) land-sea thermal contrast over East Asia in summer (winter) will cause strong (weak) monsoon. In summer, the low pressure located in East Asia becomes deeper and the moisture from the south or southeast is transported more into the land. These result in increasing precipitation amount over East Asia, especially at the coastal region. In winter, the increase (decrease) of precipitation is accompanied by strengthening (weakening) of baroclinicity over the land (sea) of East Asia.

• Ocean and Polar Research
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• v.28 no.3
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• pp.245-258
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• 2006

We investigated seasonal variations of the upper ocean temperature and the mixed layer depth (MLD) in an eddy-permitting global ocean general circulation model (OGCM) to assess the OGCM perfermance. The OGCM is based on the GFDL MOM3 which has a horizontal resolution of 0.5 degree and 30 vertical levels. The OGCM was integrated for 68 years using a monthly-mean climatological wind stress forcing. The model sea surface temperature (SST) and sea surface salinity were restored to the Levitus climatology with a time scale of 30 days. Annual-mean model SST shows a cold bias $(<\;-2^{\circ}C)$ in the summer hemisphere and a warm bias $(>\;1^{\circ}C)$ in the winter hemisphere mainly due to the restoring boundary condition of temperature. The model MLD captures well the observed features in most areas, with a slightly deep bias. However, in the Ross Sea and Weddell Sea, the model shows significantly deeper MLD than the climatology-mainly due to weak salinity stratifications in the model. For amplitude of seasonal variation, the model SST is smaller $(1{\sim}3^{\circ}C)$ than the observation largely due to the restoring surface boundary condition while the model MLD has larger seasonal variation $({\sim}50m)$. It is suggested that for more realistic simulation of the upper ocean structure in the present eddy-permitting ocean model, more refinements in the surface boundary condition for the thermohaline forcing and parameterization for vertical mixing are required, together with the incorporation of a sea-ice model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.218-233
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• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.