• 한국농공학회논문집
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• 제66권4호
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• pp.1-15
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• 2024

In recent years, climate change has been responsible for unusual weather patterns on a global scale. Droughts, natural disasters triggered by insufficient rainfall, can inflict significant social and economic consequences on the entire agricultural sector due to their widespread occurrence and the challenge in accurately predicting their onset. The frequency of drought occurrences in South Korea has been rapidly increasing since 2000, with notably severe droughts hitting regions such as Incheon, Gyeonggi, Gangwon, Chungbuk, and Gyeongbuk in 2015, resulting in significant agricultural and social damage. To prepare for future drought occurrences resulting from climate change, it is essential to develop long-term drought predictions and implement corresponding measures for areas prone to drought. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report outlines a climate change scenario under the Shared Socioeconomic Pathways (SSPs), which integrates projected future socio-economic changes and climate change mitigation efforts derived from the Coupled Model Intercomparison Project 6 (CMIP6). SSPs encompass a range of factors including demographics, economic development, ecosystems, institutions, technological advancements, and policy frameworks. In this study, various drought indices were calculated using SSP scenarios derived from 18 CMIP6 global climate models. The SSP5-8.5 scenario was employed as the climate change scenario, and meteorological drought indices such as the Standardized Precipitation Index (SPI), Self-Calibrating Effective Drought Index (scEDI), and Standardized Precipitation Evapotranspiration Index (SPEI) were utilized to analyze the prediction and variability of future drought occurrences in South Korea.

• 한국농공학회논문집
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• 제57권2호
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• pp.1-13
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• 2015

The Coupled Model Intercomparison Project Phase 5 (CMIP5), coordinated by the World Climate Research Programme in support of the Intergovernmental Panel on Climate Change (IPCC) AR5, is the most recent, provides projections of future climate change using various global climate models under four major greenhouse gas emission scenarios. There is a wide selection of climate models available to provide projections of future climate change. These provide for a wide range of possible outcomes when trying to inform managers about possible climate changes. Hence, future agrometeorological indicators estimation will be much impacted by which global climate model and climate change scenarios are used. Decision makers are increasingly expected to use climate information, but the uncertainties associated with global climate models pose substantial hurdles for agricultural resources planning. Although it is the most reasonable that quantifying of the future uncertainty using climate change scenarios, preliminary analysis using reasonable factors for selecting a subset for decision making are needed. In order to narrow the projections to a handful of models that could be used in a climate change impact study, we could provide effective information for selecting climate model and scenarios for climate change impact assessment using maximum/minimum temperature, precipitation, reference evapotranspiration, and moisture index of nine Representative Concentration Pathways (RCP) scenarios.

• Ocean and Polar Research
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• 제35권2호
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• pp.123-125
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• 2013

According to the Intergovernmental Panel on Climate Change (IPCC), ocean warming and acidification are accelerating as a result of the continuous increase in atmospheric $CO_2$. This may affect the function and structure of marine ecosystems. Recently, changes in marine environments/ecosystems have been observed (increase in SST, decrease in the pH of seawater, northward expansion of subtropical species, etc.) in Korean waters. However, we still don't understand well how climate change affects these changes and what can be expected in the future. In order to answer these questions with regard to Korean waters, the project named 'Assessment of the impact of climate change on marine ecosystems in the South Sea of Korea' has been supported for 5 years by the Ministry of Oceans and Fisheries and is scheduled to end in 2013. This project should provide valuable information on the current status of marine environments/ecosystems in the South Sea of Korea and help establish the methodology and observation/prediction systems to better understand and predict the impact of climate/marine environment changes on the structure and function of marine ecosystems. This special issue contains 5 research and a review articles that highlight the studies carried out during 2012-2013 through this project.

• Ocean Systems Engineering
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• 제7권4호
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• Ocean Systems Engineering
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• 제14권1호
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• pp.85-99
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• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.

• Journal of Astronomy and Space Sciences
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• 제41권1호
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• pp.25-33
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• 2024

This paper evaluates the influence of rainfall on propagated signal at different time exceedance percentages of an average year, over the climate zones of the country. Specifically, it demonstrates critical and non critical signal fade or signal outage time exceedance (0.001% to 1%) for Ku, K, and Ka-band systems in an average year. The study was carried out using meteorological data made available by the Nigerian Meteorological Agency (NiMet) over a period of 10 years (2009-2018). The four climate zones in the country were represented by five (5) locations; Maidugiri (warm desert climate), Sokoto (tropical dry climate), Port Harcourt (tropical monsoon climate), Abuja and Enugu (tropical savanna climate). The parameters were simulated into the International Telecommunications Union Recommended (ITU-R) models for rain attenuation over the tropics and results presented using MatLab and Origin Lab. Results of Ku band propagations showed that only locations in the tropical savanna and tropical monsoon climates experienced total signal outage for time percentage exceedance equal to or below 0.01% for both horizontal and vertical polarizations. At K band propagations, the five locations showed to have experienced signal outage at time exceedance equal to and below 0.01%, almost same was recorded for the Ka-band propagation. It was also observed that horizontal and vertical polarization of signal had slightly different rain attenuation values for the studied bands at the five locations, with horizontal polarization having higher values than vertical polarization.

• 대기
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• 제31권5호
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.343-346
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• 2002

Toward more accurate determination of the water cycle in association with climate variability and change as well as baseline data on the impacts of this variability on water resources, the Coordinated Enhanced Observing Period (CEOP) was launched on July 1,2001. The preliminary data period, EOP-1, was implemented from July to September in 2001. The first annual enhanced observing period, EOP-3, is going to start on October 1,2002. CEOP is seeking to achieve a database of common measurements from both in situ and satellite remote sensing, model output, and four-dimensional data analyses (4DDA; including global and regional reanalyses) for a specified period. In this context a number of carefully selected reference stations are linked closely with the existing network of observing sites involved in the GEWEX Continental Scale Experiments, which are distributed across the world. The initial step of CEOP is to develop a pilot global hydro-climatological dataset with global consistency under the climate variability that can be used to help validate satellite hydrology products and evaluate, develop and eventually predict water and energy cycle processes in global and regional models. Based on the dataset, we will address the studies on the inter-comparison and inter-connectivity of the monsoon systems and regional water and energy budget, and a path to down-scaling from the global climate to local water resources, as the second step.

• 한국수소및신에너지학회논문집
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• 제34권6호
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• pp.773-784
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• 2023

In this study, a comparative analysis between an electric heat pump cooling system and a hybrid desiccant cooling system is conducted. Desiccant cooling is a thermal driven system with potentially lower electric power consumption than electric heat pump. Hybrid desiccant cooling system simulation includes components such as a desiccant rotor, direct and indirect evaporative coolers, heat exchangers, fans, and a heat pump system. Using dynamic simulations by climate conditions, house cooling temperatures and power consumption for both systems are analyzed for 16 days period in the summer season under climate scenarios for the year 2100 prediction. The results reveal that the hybrid desiccant cooling system exhibits a 5-18% reduction in electric consumption compared to the heat pump system.

• Spatial Information Research
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• 제19권6호
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• pp.101-109
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• 2011

기후변화는 지구환경 시스템을 구성하는 모든 권역에 영향을 미치며 각 권역은 서로 비선형적인 상호작용을 통해 다시 기후변화에 영향을 미친다. 따라서 기후와 지구환경 시스템 사이의 피드백 과정을 종합적으로 분석하고, 변화특성을 진단하여 예측할 수 있는 통합적인 연구가 필요하다. 그러나 현재까지는 기후변화에 따른 지구환경 시스템의 특정 권역의 변화에 대해서만 연구를 진행하고 있어 기후 환경 상호간의 연계 연구 지원이 미흡한 실정이다. 따라서 본 연구에서는 기후 환경 변화를 종합적으로 분석하고 예측하기 위해 자료 저장, 관리 및 배포를 지원하는 GIS기반의 통합DB 관리시스템을 개발하였다. 통합DB 관리시스템은 VB.NET 2005와 지도 기반의 공간 표현을 위한 ArcObjects 컴포넌트를 이용하여 개발하였다. 먼저 기후 환경 전문가의 요구사항을 고려한 연구 대상 및 자료를 선정하였고, 자료 관리 및 활용 방법을 정의하였다. 또한 연구 자료의 효율적인 검색을 위하여 데이터를 표준화하였으며, 이를 적용한 데이터 모델링을 통하여 기후 환경 DB와 자료의 이해도를 높이고, 공간적 상관관계 분석을 위한 GIS DB를 구축하였다. 구축된 DB를 기반으로 사용자에게 DB의 다양한 검색 및 접근을 통해 자료에 대한 세부적인 정보를 제공하고 자료의 배포가 가능한 프로토 타입의 통합DB 관리시스템을 개발하였다. 이러한 GIS기반의 기후 환경 통합DB 관리시스템은 효율적인 자료 관리는 물론 자료를 배포하는 환경을 제공 할 수 있으며, 미래의 기후변화를 종합적으로 진단 및 예측 하는데 기여가 클 것으로 판단된다.