• 대한조선학회논문집
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• 제54권6호
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• pp.501-514
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• 2017

In the previous research, a study was carried out to estimate the actual performance such as the propeller Revolution Per Minute (RPM) and engine power of a Liquefied Natural Gas Carrier (LNGC) using the full-scale measurement data. After the predicted RPM and engine power were verified by comparing those with the measured values, the suggested method was regarded to be acceptable. However, there was a limitation to apply the method on the prediction of the RPM and engine power of a ship. Since the information of route, speed, and environmental conditions required for estimating the RPM and engine power is generally regarded as the intellectual property of a shipping company, it is difficult to secure the information on a shipyard. In this paper, the RPM and engine power of the 151K LNGC was estimated using the combination of Automatic Identification System (AIS) and European Centre for Medium-Range Weather Forecasts (ECMWF) database in order to replace the full-scale measurement. The simulation approach, which was suggested in the previous research, was identically applied to the prediction of RPM and engine power. After the results based on the AIS and ECMWF database were compared with those obtained from the full-scale measurement data, the feasibility was briefly reviewed.

• 대한원격탐사학회지
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• 제34권6_1호
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• pp.857-868
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• 2018

특정 지점에서 대기 에어러솔의 미세물리적 특성과 시공간적 분포는 에어로솔 입자의 광학특성을 파악하기 위한 중요한 변수이다. 이러한 에어러솔의 광학특성값에 대한 정확한 산출은 복사전달 모의 과정에서 정확한 값을 제공함으로 중요한 역할을 가지게 된다. 따라서 본 연구는 사용자가 요구하는 시공간적 조건에서 정확한 에어로솔 모델을 산출하기 위한 방법으로서 재분석 자료와 광학 특성 데이터 베이스를 이용한 시너지 에어로솔 모델을 산출하는 방법을 제시하였다. 제안된 시너지 에어로솔 모델은 에어로솔의 주요 성분별 광학두께(Aerosol Optical Depth; AOD)값에 의하여 가중치가 적용된 혼합 에어러솔 형태의 광학 모델을 산출하기 위함이며, $40{\mu}m$까지의 파장영역에서 광학특성값을 제공한다. 주요 에어로솔 이벤트 사례에 대하여, 시너지적 에어러솔 모델(Synergy Aerosol Model; SAM)은 기존의 복사전달 모델에서 사용되고 있는 표준 에어러솔 모델과는 차별적인 결과를 보여주었으며, 지상관측 Aerosol Robotic Network(AERONET) inversion 산출물과의 비교를 통하여 오차범위 내의 정량적인 결과를 가지고 있는 것을 보였다. 따라서, 복사전달 모의에 있어 시너지 에어로솔 모델의 사용은 실제 대기 중 에어러솔에 의한 영향을 정량적으로 평가하는데 도움을 줄 수 있을 것이며, 개선된 복사 모의 결과를 얻을 수 있을 것이다.

• 대기
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• 제28권1호
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• pp.15-24
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• 2018

Sea ice plays an important role in modulating surface conditions at high and mid-latitudes. It reacts rapidly to climate change, therefore, it is a good indicator for capturing these changes from the Arctic climate. While many models have been used to study the predictability of climate variables, their performance in predicting sea ice was not well assessed. This study examines the predictability of the Arctic sea ice extent from ensemble prediction systems. The analysis is focused on verification of predictability in each model compared to the observation and prediction in particular, on lead time in Sub-seasonal to Seasonal (S2S) scales. The S2S database now provides quasi-real time ensemble forecasts and hindcasts up to about 60 days from 11 centers: BoM, CMA, ECCC, ECMWF, HMCR, ISAC-CNR, JMA, KMA, Meteo France, NCEP and UKMO. For multi model comparison, only models coupled with sea ice model were selected. Predictability is quantified by the climatology, bias, trends and correlation skill score computed from hindcasts over the period 1999 to 2009. Most of models are able to reproduce characteristics of the sea ice, but they have bias with seasonal dependence and lead time. All models show decreasing sea ice extent trends with a maximum magnitude in warm season. The Arctic sea ice extent can be skillfully predicted up 6 weeks ahead in S2S scales. But trend-independent skill is small and statistically significant for lead time over 6 weeks only in summer.