• 대한원격탐사학회지
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• 제39권3호
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• pp.355-361
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• 2023

지구 표면에 대한 정보는 기상 및 대기 역학의 이해나 인간을 포함한 동물에 큰 영향을 미치는 극한 열현상에 대응함에 있어서 핵심적인 지구 물리량이다. 지구 영역에 대한 온도를 추정하기 위하여 위성에 탑재된 열적외 센서가 널리 활용되어 왔는데, 정밀한 활용을 위해서는 온도 추정 과정의 불확도에 대한 이해가 선행되어야 한다. 하지만 온도추정 불확도에 영향을 미치는 많은 요소 중에서 한반도 주변의 환경 하에서의 온도추정 알고리즘의 불확도 산정에 대한 연구는 미미하였다. 본 연구에서는 한반도 주변의 대기 및 해양 조건하에서 범용성이 높은 single-channel 알고리즘의 불확도를 추정하는 연구를 수행하였다. 알고리즘의 입력자료로 필요한 재분석자료(reanalysis)의 영향성을 평가하기 위하여 두 가지의 재분석자료, 즉 fifth generation of European Centre for Medium-Range Weather Forecasts reanalysis of the global climate and weather (ERA5)와 Modern-Era Retrospective analysis for Research and Applications-2 (MERRA-2)를 사용하였고, 복사전달모델은 MODerate resolution atmospheric TRANsmission (MODTRAN)을 사용하였다. MODTRAN 모의와 온도 추정 정확도 검증에 사용되는 현장 관측 수온은 한반도 인근 해역에 위치한 해양 기상 부이(buoy)로부터 획득했다. 실험 결과, 알고리즘 불확도는 대기 수증기량에 따라서 선형에 가깝게 증가하는 것을 확인하였고, 가장 건조한 조건에서는 약 0.35K 그리고 평균적으로 0.45K 가량의 불확도가 발생함을 확인하였다. 이러한 결과는 재분석자료의 종류에 상관없이 유사하게 도출되어 알고리즘이 가지는 순수한 불확도라고 추정할 수 있었다.

• Ocean and Polar Research
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• 제34권1호
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• pp.73-83
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• 2012

Antarctica is very sensitive to climate change but the number of stations is not sufficient to accurately analyze climate change in this regoin. Model reanalysis data supplements the lack of observation and can be used as long term data to verify climate change. In this study, the 20CR (Twentieth Century Reanalysis) Project data from NCEP/NCAR and monthly mean data (temperature, solar radiation and longwave radiation) from 1871 to 2008, was used to analyze the temperature trend and change in radiation. The 20CR data was used to validate the observation data from Antarctica since 1950 and the correlation coefficients between these data were determined to be over 0.95 at all stations. The temperature increased by approximately $0.23^{\circ}C$/decade during the study period and over $0.20^{\circ}C$/decade over all of the months. This increasing trend was observed throughout the Antarctica and a slight increase was observed in the Antarctic Peninsula. In addition, solar radiation (surface) and longwave radiation (surface and top of atmosphere) trends correlated with the increase in temperature. As a result, outgoing longwave radiation at the surface is attenuated by atmospheric water vapor or clouds and radiation at the top of the atmosphere was reduced. In addition, the absorbed energy in the atmosphere increases the temperature of the atmosphere and surface, and then the heated surface emits more longwave radiation. Eventually these processes are repeated in a positive feedback loop, which results in a continuous rise in temperature.

• 산업기술연구
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• 제35권
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• pp.95-102
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• 2015

Two extreme wind speed prediction models, the EWM(Extreme wind speed model) in IEC61400-1 and the Gumbel method were compared in this study. The two models were used to predict extreme wind speeds of six different sites in Korea and the results were compared with long term wind data. The NCAR reanalysis data were used for inputs to two models. Various periods of input wind data were tried from 1 year to 50 years and the results were compared with the 50 year maximum wind speed of NCAR wind data. It was found that the EWM model underpredicted the extreme wind speed more than 5 % for two sites. Predictions from Gumbel method overpredicted the extreme wind speed or underpredicted it less than 5 % for all cases when the period of the input data is longer than 10 years. The period of the input wind data less than 3 years resulted in large prediction errors for Gumbel method. Predictions from the EWM model were not, however, much affected by the period of the input wind data.

• 한국기후변화학회지
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• 제6권2호
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• pp.95-104
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• 2015

To discuss whether we have credible estimations about historical surface temperature evolution since industrial revolution or not, present study investigates consistencies and differences of averaged surface air temperature since 1900 between the multiple data sources: Hadley Center Climate Research Unit (HadCRU4) surface air temperature data, ECMWF 20 Century Reanalysis data (ERA20CR), and NCEP 20 Century Reanalysis data (NCEP20CR). Averaged surface temperatures are obtained for the global, polar (90S~60S, 60N~0N), midlatitude (60S~30S, 30N~60N), tropical (30S~30N) region, separately. From the analysis, we show that: 1) spatio-temporal inhomogenity and scarcity of HadCRU4 data are not major obstacles in the reliable estimation of global surface air temperature. 2) Globally averaged temperature variability is largely contributed by those of tropical and midlatitude, which occupy more than 70% of earth surface in area. 3) Both data show consistent temperature variability in tropical region. 4) ERA20CR does not capture warm period over Arctic region in early 1900s, which is obvious feature in HadCRU4 data. Discrepancies among datasets suggest that high-level caution is needed especially in the interpretation of large Arctic warming in the early 1900s, which is often regarded as a natural variability in the Arctic region.

• 신재생에너지
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• 제17권4호
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• pp.1-8
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• 2021

To determine the wind turbine class in the offshore of the Korean Peninsula, the reference wind speed for a 50-y return period at the hub height of a wind turbine was estimated using the reanalysis data sets. The most recent reanalysis data, ERA5, showed the highest correlation coefficient (R) of 0.82 with the wind speed measured by the Southwest offshore meteorological tower. However, most of the reanaysis data sets except CFSR underestimated the annual maximum wind speed. The gust factor of converting the 1 h-average into the 10 min-average wind speed was 1.03, which is the same as the WMO reference, using several meteorological towers and lidar measurements. Because the period, frequency, and path of typhoons invading the Korean Peninsula has been changing owing to the climate effect, significant differences occurred in the estimation of the extreme wind speed. Depending on the past data period and length, the extreme wind speed differed by more than 30% and the extreme wind speed decreased as the data period became longer. Finally, a reference wind speed map around the Korean Peninsula was drawn using the data of the last 10 years at the general hub-height of 100 m above the sea level.

• 한국해양학회지:바다
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• 제20권2호
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• pp.102-118
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• 2015

본 연구에서는 국제 공동 해양 재분석 자료 비교 프로젝트 결과를 요약하였다. 다양한 재분석 자료 생산 시스템의 종류 및 특성을 소개하였으며, 대표적인 8가지 해양 변수(열용량, 열염분 높이, 해수면 높이, 표층 열속, 혼합층 깊이, 아표층염분, $20^{\circ}C$ 등온선 깊이, 해빙)에 대한 전지구 해양 자료 동화 모델 성능을 비교 분석하였다. 일반적으로 단일 재분석 자료 결과보다 앙상블 평균 값이 비교적 높은 성능을 나타내었으나, 검증 변수와 해역에 따라 서로 다른 특징을 보였다. 해양 변수 중에는 염분 및 해빙 변동이 모델간 가장 큰 편차를 보였다. 심층 해역, 남극해, 서안 경계 해역을 포함한 연안역에서는 공통적으로 객관 분석장과 동화 모델간의 편차가 크게 나타났다. 국내에서도 독립적으로 운영되고 있는 해양 자료 동화 모델간의 비교 분석 프로그램이 추진되어, 향후 관련된 국제 공동 연구에 활발히 참여할 수 있는 기회가 확대되기를 기대한다.

• Ocean and Polar Research
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• 제41권4호
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• pp.213-232
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• 2019

This study evaluates the accuracy of four satellite-composite (OSTIA, AVHRR, G1SST, FNMONC-S) and three model-reanalysis (HYCOM, JCOPE2, FNMOC-M) daily sea surface temperature (SST) data around the Korean Peninsula (KP) using ocean buoy data from 2011-2016. The results reveal that OSTIA has the lowest root mean square error (RMSE; 0.68℃) and FNMOC-S/M has the highest correction coefficients (r = 0.993) compared with observations, while G1SST, JCOPE2, and AVHRR have relatively larger RMSEs and smaller correlations. The large RMSEs were found in the western coastal regions of the KP where water depth is shallow and tides are strong, such as Chilbaldo and Deokjeokdo, while low RMSEs were found in the East Sea and open oceans where water depth is relatively deep such as Donghae, Ulleungdo, and Marado. We found that the main sources of the large RMSEs, sometimes reaching up to 5℃, in SST data around the KP, can be attributed to rapid SST changes during events of strong tidal mixing, upwelling, and typhoon-induced mixing. The errors in the background SST fields which are used in data assimilations and satellite composites and the missing in-situ observations are also potential sources of large SST errors. These results suggest that both satellite and reanalysis SST data, which are believed to be true observation-based data, sometimes, can have significant inherent errors in specific regions around the KP and thus the use of such SST products should proceed with caution particularly when the aforementioned events occur.

• 대기
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• 제21권4호
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• pp.439-446
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• 2011

NCEP reanalysis data were analyzed in order to provide distribution of global wind resource and wind speed in the surface layer for the years 2000-2009. Wind speed at 10 m above ground level (AGL) was converted to wind speed at 80 m above the ground level using the power law. The global average 80 m wind speed shows a maximum value of $13ms^{-1}$ at the storm track region. High wind speed over the land exists in Tibet, Mongolia, Central North America, South Africa, Australia, and Argentina. Wind speed over the ocean increased with a large value in the South China Sea, Southeast Asia, East Sea of the Korea. Sea surface wind in Western Europe and Scandinavia are suitable for wind farm with a value of $7-8ms^{-1}$. Areas with great potential for wind farm are also found in Eastern and Western coastal region of North America. Sea surface wind in Southern Hemisphere shows larger values in the high latitude of South America, South Africa and Australia. The distribution of low-resolution reanalysis data represents general potential areas for wind power and can be used to provide information for high-resolution wind resource mapping.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Journal of Mechanical Science and Technology
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• 제20권6호
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• pp.782-794
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• 2006

Classical finite element programs are not well suited to the design of composite structures, because they are primarily analysis tools and need much time for the data input and as well as for the interpretation of the results. The aim of this paper is to develop a program which allows very fast analyses and reanalyses for design process, thanks to a fast reanalysis method with changes of data and conditions. Speed in the analysis Is obtained by simplification of the analysed structure and limitations in its geometrical generality and improvements in numerical methods. The use of the program is made easy with interactive user-friendly facilities.