• Journal of Climate Change Research
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• v.2 no.4
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• pp.237-251
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• 2011

This study presents a review on the recent climate change over the Korean peninsula, which has experienced a significant change due to the human-induced global warming more strongly than other regions. The recent measurement of carbon dioxide concentrations over the Korean peninsula shows a faster rise than the global average, and the increasing trend in surface temperature over this region is much larger than the global mean trend. Recent observational studies reporting the weakened cold extremes and intensified warm extremes over the region support consistently the increase of mean temperature. Surface vegetation greenness in spring has also progressed relatively more quickly. Summer precipitation over the Korean peninsula has increased by about 15% since 1990 compared to the previous period. This was mainly due to an increase in August. On the other hand, a slight decrease in the precipitation (about 5%) during Changma period (rainy season of the East Asian summer monsoon), was observed. The heavy rainfall amounts exhibit an increasing trend particularly since the late 1970s, and a consecutive dry-day has also increased primarily over the southern area. This indicates that the duration of precipitation events has shortened, while their intensity became stronger. During the past decades, there have been more stronger typhoons affecting the Korean peninsula with landing more preferentially over the southeastern area. Meanwhile, the urbanization effect is likely to contribute to the rapid warming, explaining about 28% of total temperature increase during the past 55 years. The impact of El Nino on seasonal climate over the Korean peninsula has been well established - winter [summer] temperatures was generally higher [lower] than normal, and summer rainfall tends to increase during El-Nino years. It is suggested that more frequent occurrence of the 'central-Pacific El-Nino' during recent decades may have induced warmer summer and fall over the Korean peninsula. In short, detection and attribution studies provided fundamental information that needed to construct more reliable projections of future climate changes, and therefore more comprehensive researches are required for better understanding of past climate variations.

• Atmosphere
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• v.21 no.1
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• pp.17-33
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• 2011

A dynamical downscaling system for seasonal forecast has been constructed based on a regional climate model, and its predictability was investigated for 10 years' wintertime (December-January-February; DJF) climatology in East Asia. Initial and lateral boundary conditions were obtained from the operational seasonal forecasting data, which are realtime output of the Global Data Assimilation and Prediction System (GDAPS) at Korea Meteorological Administration (KMA). Sea surface temperature was also obtained from the operational forecasts, i.e., KMA El-Nino and Global Sea Surface Temperature Forecast System. In order to determine the better configuration of the regional climate model for East Asian regions, two sensitivity experiments were carried out for one winter season (97/98 DJF): One is for the topography blending and the other is for the cumulus parameterization scheme. After determining the proper configuration, the predictability of the regional forecasting system was validated with respect to 850 hPa temperature and precipitation. The results showed that mean fields error and other verification statistics were generally decreased compared to GDAPS, most evident in 500 hPa geopotential heights. These improved simulation affected season prediction, and then HSS was better 36% and 11% about 850 hPa temperature and precipitation, respectively.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1709-1722
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• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.