• Atmosphere
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• v.15 no.1
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• pp.1-16
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• 2005

Land-surface and soil processes significantly affect mesoscale local weather systems as well as global/regional climate. In this study, characteristics of land-surface models (LSMs) and soil models (SMs) that are frequently coupled into mesoscale meteorological models are investigated. In addition, detailed analyses on three LSMs, employed by the PSU/NCAR MM5, are provided. Some impacts of LSMs on heavy rainfall prediction are also discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1237-1238
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• 2005

• Atmosphere
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• v.15 no.2
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• pp.141-148
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• 2005

Currently several important observation programs are planned or being performed both domestically and internationally. In this paper, a brief introduction is provided on international programs such as THORPEX, ARGO and GEOSS as well as a domestic program KEOP. In addition, discussions on various issues related to observations and future visions are provided.

• Atmosphere
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• v.14 no.4
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• pp.19-23
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• 2004

Progresses in numerical prediction of weather and climate have been in parallel with those of computing resources, especially the development of supercomputers. Advanced techniques in numerical modeling, computational schemes, and data assimilation cloud not have been practically achieved without the aid of supercomputers. With such techniques and computing powers, the accuracy of numerical forecasts has been tremendously improved. Supercomputers are also indispensible in constructing and executing the synthetic Earth system models. In this study, a brief overview on numerical weather / climate prediction, Earth system modeling, and the values of supercomputing is provided.

• Atmosphere
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• v.16 no.4
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• pp.387-393
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• 2006

In this study, journal selection processes of the Science Citation Index (SCI) and the SCOPUS are investigated aiming at making the Journal of the Korean Meteorological Society (JKMS) an SCI journal. In addition, some characteristic features of the SCI journals in the field of atmospheric sciences published in Asian countries are examined. Some domestic journals in the related disciplines that are recently listed in the SCI and SCOPUS are also analyzed in terms of strategic approaches. Results of this study may provide fundamental strategic information in pursuing the JKMS to be listed in the SCI in the near future.

• Atmosphere
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• v.14 no.2
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• pp.7-10
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• 2004

A short review on TOTO (TOtable Tornado Observatory), one of the earliest in-situ observing systems for tornado, is provided. TOTO was outfitted with sensors for measuring wind, pressure and humidity, and storm researchers, in mid-1980's, tried to put it inside tornadoes for detailed studies on tornado, but failed. However, the accumulated knowledge and experience with TOTO lead to a successful field program in mid-1990's. A story about DOROTHY, a parody of TO TO in the movie "Twister!", is also provided.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.627-631
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• 2006

본 연구에서는 도시화가 도시지역의 수문기상변화에 미치는 영향을 분석하고자 하였으며, 토지이용의 변화로 인한 국지 수문기상의 변화를 모의하여 장기적인 변화 특성을 파악하여 이를 평가하고자 하였다. 이를 위해 도시화로 인한 우리나라 강우의 변화 특성을 파악하고자 하였으며, 도시화와 기후변화로 인한 수문환경의 변화를 반영한 대안의 수립 및 설계방안을 제시하였다. 특히, 본 연구에서는 도시화에 따른 수문기상변화의 예측 가능한 모형을 개발하여 다양한 상황에 대한 모의를 실시하고자 하였다. 이를 위해 도시화의 영향 정도를 시공간적으로 정량화 할 수 있는 모형을 개발하고 서울지역에 직접 적용 및 평가하였으며, 이를 통해 도시화로 인한 기상수문학적인 인자의 시공간적 영향을 분석 및 일반화하고, 도시화에 따른 기상수문학적 영향을 최소화할 수 있는 방안을 검토하고자 하였다. 서울 지역 장기 관측자료에 대한 분석을 통해 도시화의 진전에 따라 기온, 습도, 강수량이 증가하며, 일조 시간 및 잠재증발량은 감소함을 알 수 있었다. 이는 도시화와 기후변화로 인해 기온이 증가하고 도시화의 특성에 따라 습도 증가 및 일조시간과 잠재증발량의 감소가 나타나며, 이것이 큰 폭의 강수량 증가로 이어진 것으로 판단되었다. 또한, 모형을 통해 분석한 결과 토양수분과 실제증발량 모두 증가하는 것으로 나타났으며, 이로 인해 내부증발 강수량이 증가하는 것으로 분석되었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.112-115
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Proceedings of the KSRS Conference
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• v.2
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• pp.989-992
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• 2006

The new approach to the problem of oil spill detection consisting in combined use of all available quasiconcurrent satellite information (AVHRR NOAA, TOPEX/Poseidon, Jason-1, MODIS Terra/Aqua, QuikSCAT) is suggested. We present the results of the application of the proposed approach to the operational monitoring of seawater condition and pollution in the coastal zone of northeastern Black Sea conducted in 2006. This monitoring is based on daily receiving, processing and analysis of data different in nature (microwave radar images, optical and infrared data), resolution and surface coverage. These data allow us to retrieve information on seawater pollution, sea surface and air-sea boundary layer conditions, seawater temperature and suspended matter distributions, chlorophyll a concentration, mesoscale water dynamics, near-surface wind and surface wave fields. The focus is on coastal seawater circulation mechanisms and their impact on the evolution of pollutants.