• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.49-60
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• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Journal of the Korean earth science society
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• v.27 no.2
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• pp.188-197
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• 2006

The characteristics of variability of temperature and precipitation in Jeju were investigated using data observed in Jeju station for from 1924 to 2004. Annual mean temperature change for the last 81 years is $0.02^{\circ}C$ increase per year. After 1980, the increase is $0.05^{\circ}C$ per year, larger than the former. The increase of the minimum temperature is larger than that of the maximum temperature in Jeju and has resulted in the increase of mean temperature. The frequency of climate extreme occurrence of temperature and rainfall was also investigated. The temporal variation of frequency of the extremely higher temperature has increased in the 1980's with global warming. The appearance of the extremely lower minimum temperature has decreased during the summers and winters. The facts that the frequencies of rainy days has decreased and heavy rainfall days of more than 80 mm per day in precipitation has increased indicate the increase of rainfall intensity.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1379-1391
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• 2020

UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.

• Ocean and Polar Research
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• v.35 no.3
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• pp.259-272
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• 2013

One of the factors influencing the climate around Korea is the oceanic-atmospheric variability in the tropical region between the eastern Indian and the western Pacific Oceans. Lack of knowledge about the air-sea interaction in the tropical Indo-Pacific region continues to make it problematic forecasting the ocean climate in the East Asia. The 'Tropical Indo-Pacific water transport and ecosystem monitoring EXperiment (TIPEX)' is a program for monitoring the ocean circulation variability between Pacific and Indian Oceans and for improving the accuracy of future climate forecasting. The main goal of the TIPEX program is to quantify the climate and ocean circulation change between the Indian and the Pacific Oceans. The contents of the program are 1) to observe the mixing process of different water masses and water transport in the eastern Indian and the western Pacific, 2) to understand the large-scale oceanic-climatic variation including El Nino-Southern Oscillation (ENSO)/Warm Pool/Pacific Decadal Oscillation (PDO)/Indian Ocean Dipole (IOD), and 3) to monitor the biogeochemical processes, material flux, and biological changes due to the climate change. In order to effectively carry out the monitoring program, close international cooperation and the proper co-work sharing of tasks between China, Japan, Indonesia, and India as well as USA is required.

• Atmosphere
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• v.15 no.1
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• pp.69-74
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• 2005

In this short article, oceanic processes that could have strong effect on the climate have been explained while focusing on the oceanic thermohaline circulation (THC). First, the structure of THC is explained using a simple scaling law. Then, the thermohaline catastrophe, which is believed to be a cause of a rapid climate changes observed in paleoclimate records, and interdecadal variations in THC are explained. The interactions between the oceans and $CO_2$ are also mentioned briefly.

• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.27-33
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• 2020

The unsustainable human activities like increased use of automobiles, heavy industrialization and the use of large volumes of fertilizers, chemicals and pesticides in the agricultural land cause climate change problems in one way or another. Under normal circumstances, the heat radiations from the sun will be reflected back. An excessive volume of GHGs in the atmosphere would prevent these radiations from reflecting back. East Asia is facing severe climate change issues in recent times. A lot of climate change problems such as hurricanes and floods have been reported from this region in the last couple of decades. The study aimed at investigating the climate change in East Asia with changing Sea Surface Temperature (SST). The study adopted a quantitative research method with a case study research design where a deliberate focus was made on the East Asia Region. Secondary data was gathered and analyzed to yield both descriptive and inferential statistics. The study concluded that the impact of East Asia Climate variability was significant mainly for some extreme events. Also, the study concluded that there was a significant link between the change of the East Asia climate variability and that of the sea surface temperature. Further, the study concluded that a linear relationship existed between the sea surface temperature and the climate of East Asia. Hence, a linear regression was a significant predictor of the East Asia Climate (EAC) based on changing sea surface temperature. The model revealed that 37.4% of the variations in the climate change index were explained by the changes in the sea surface temperature. The climate was expected to change with a value of 49.48 for a unit change in the sea surface temperature.

• Ocean and Polar Research
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• v.35 no.2
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• pp.171-179
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• 2013

The long-term variability of sea surface temperature in the East China Sea was reviewed based mainly on published literatures. Though the quantitative results are not the same, it is generally shown that sea surface temperature is increasing especially in recent years with the rate of increase about $0.03^{\circ}C$/year. Other meaningful results presented in the literatures is that the difference of water properties between layers upper and lower than the thermocline in summer shows an increasing trend both in temperature and salinity, suggesting that the stratification has been intensified. As a mechanism by which to evaluate the wintertime warming trend in the region, the weakening of wind strength, which is related to the variation of sea level pressure and atmospheric circulation in the western North Pacific and northern Asian continent, is suggested in the most of related studies.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.3
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• pp.261-272
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• 2012

In this study, climate variability was predicted by the Weather Research and Forecasting (WRF) model under two different scenarios (current trends scenario; SC1 and managed scenario; SC2) for future urban growth over the Seoul metropolitan area (SMA). We used the urban growth model, SLEUTH (Slope, Land-use, Excluded, Urban, Transportation, Hill-Shade) to predict the future urban growth in SMA. As a result, the difference of urban ratio between two scenarios was the maximum up to 2.2% during 50 years (2000~2050). Also, the results of SLEUTH like this were adjusted in the Weather Research and Forecasting (WRF) model to analysis the difference of the future climate for the future urbanization effect. By scenarios of urban growth, we knew that the significant differences of surface temperature with a maximum of about 4 K and PBL height with a maximum of about 200 m appeared locally in newly urbanized area. However, wind speeds are not sensitive for the future urban growth in SMA. These results show that we need to consider the future land-use changes or future urban extension in the study for the prediction of future climate changes.

• Atmosphere
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• v.16 no.3
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• pp.215-223
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• 2006

Among observational, local-environmental, and large-scale factors causing significant changes in climate records, the site relocations and the replacement of the instruments are well-known nonclimatic factors for the analysis of climatic trends, climatic variability, and for the detection of anthropogenic climate change such as heat-island effect and global warming. Using dataset that were contaminated by these nonclimatic factors can affect seriously the assessment of climatic trends and variability, and the detection of the climatic change signal. In this paper, the inhomogeneities, which have been caused by relocation of the observation site, in the climate data of Korea Meteorological Administration (KMA) were examined using two-phase regression model. The observations of pan evaporation and wind speed are more sensitive to site relocations than those of other meteorological elements, such as daily mean, maximum and minimum temperatures, with regardless to region.