• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.705-716
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• 2008

The objectives of this study are the detection and forecast of climate change signal in the annual mean of surface temperature data, which are generated by MRI/JMA CGCM over the Korean Peninsula. MRI/JMA CGCM outputs consist of control run data(experiment with no change of $CO_2$ concentration) and scenario run data($CO_2$ 1%/year increase experiment to quadrupling) during 142 years for surface temperature and precipitation. And ECMWF reanalysis data during 43 years are used as observations. All data have the same spatial structure which consists of 42 grid points. Two statistical models, the Bayesian fingerprint method and the regression model with autoregressive error(AUTOREG model), are separately applied to detect the climate change signal. The forecasts up to 2100 are generated by the estimated AUTOREG model only for detected grid points.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.465-480
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• 2020

Numerical simulations of the storm surge and the wave induced by the Typhoon Sanba incident on the south coast of Korea in 2012 are conducted using the JMA-MSM forecast weather field, NCEP-CFSR reanalysis weather field, ECMWF-ERA5 reanalysis weather field, and the pressure and wind fields obtained using the best track information provided by JTWC. The calculated surge heights are compared with the time history observed at harbors along the coasts of Korea. For the waves the calculated significant wave heights are compared with the data measured using the wave buoys and the underwater pressure type wave gauge. As a result the JMA-MSM and the NCEP-CFSR weather fields give the highest reliability. The ECMWF-ERA5 gives in general surge and wave heights weaker than the measured. The ECMWF-ERA5, however, reproduces the best convergence belt formed in front of the typhoon. The weather field obtained using JTWC best track information gives the worst agreement.

• 한국지구과학회:학술대회논문집
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• 2003.09a
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• pp.104-105
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• 2003

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.543-551
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• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.27-27
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• 2017

신뢰성 있는 수개월 선행시간의 댐 유입량 예측은 가뭄 상황으로 진입하는 시점에서 효율적인 댐 운영을 위해 필수적이다. 최근 기후변화로 인한 강수량의 경년 및 계절 내 변동성이 증가됨에 따라서 기존의 과거 통계치를 이용한 댐 운영 의사결정은 많은 도전을 받고 있다. 최근 엘리뇨-남방진동(ENSO) 등의 전구기후지수와 지역수문기후와의 원격상관성을 활용하여 수개월 이후에 대한 수문조건을 통계적으로 예측하기 위한 연구가 시도되고 있다. 또한 매월 제공되는 역학적 예측모형으로부터 생산된 월단위 예측정보를 유량예측을 위한 유역모형에 활용하기 위하여 편이보정 및 상세화 기법이 개발되어 활용되고 있다. 본 연구에서는 댐 유입량 예측을 위해 SWAT 모형을 선정하였고 최장 6개월 선행 강수량 및 기온의 예측을 위해서 하이브리드 계절예측 시스템을 활용하였다. 이 시스템은 전지구역학적 예측모형의 자료를 편이보정을 거쳐 직접적으로 사용하는 단순 편이보정(Simple Bias Correction, SBC) 방법에 회귀모형을 이용하여 통계적인 방법으로 예측자료를 생산하는 전구기후지수 기반의 Climate Index Regression (CIR), 실시간 재분석자료 기반의 Observation-based Moving Window Regression (MWR-Obs), 역학적 예측모형의 예측자료 기반의 Moving Window Regression (MWR) 방법을 통합하여 사용하고 있다. 충주댐을 대상으로 우선 관측자료를 이용하여 SWAT 모형을 검 보정한 후, 관측기간에 대하여 하이브리드 시스템에 의한 예측 기상자료를 적용하여 모의된 댐 유입량과 관측 유입량과의 비교를 통해 예측성을 평가하였다. 본 연구는 다양한 기후정보를 활용하여 댐 유입량 예측에 있어서 예측성을 높이고자 시도되었으며, 도출된 결과는 향후 충주댐 운영에 유용한 정보를 제공할 수 있는 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.121-121
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• 2023

다수의 서로 다른 해상도의 자료를 병합(Merge)하는 것은 강수 자료 사용에 중요한 절차 중 하나이다. 강수 자료는 다수의 소스(관측소, 레이더, 위성 등)에서 관측 자료를 제공한다. 연구자들은 각 원본 자료의 장점을 취하고 단점을 보완하기 위해 다중소스 기반의 재분석 강수 자료를 제작하여 사용하고 있다. 기존의 방법은 자료를 병합하기 위해 서로 다른 공간적 특성을 갖는 자료들을 공간적으로 동일한 위치로 보간(Interpolation) 하는 과정이 필요하다. 하지만 보간 절차는 원본자료에 인위적인 변형을 주기 때문에 많은 오차(Error)를 발생시키는 것으로 알려져 있다. 따라서 본 연구는 병합 과정에서 보간 절차를 제외하고 원본 해상도 자료를 그대로 입력하기 위해 머신 러닝 방법의 하나인 Super resolution convolutional neural network(SRCNN)에 기반한 병합 방법을 제안하고자 한다. 이 방법은 원본 자료의 영향을 모델이 직접 취사선택하여 최종 자료에 도달하기 때문에 병합 과정의 오류를 줄일 수 있을 것으로 기대된다.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.139-147
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• 2012

Mongolia's solar-meteorological resources map has been developed using satellite data and reanalysis data. Solar radiation was calculated using solar radiation model, in which the input data were satellite data from SRTM, TERA, AQUA, AURA and MTSAT-1R satellites and the reanalysis data from NCEP/NCAR. The calculated results are validated by the DSWRF (Downward Short-Wave Radiation Flux) from NCEP/NCAR reanalysis. Mongolia is composed of mountainous region in the western area and desert or semi-arid region in middle and southern parts of the country. South-central area comprises inside the continent with a clear day and less rainfall, and irradiation is higher than other regions on the same latitude. The western mountain region is reached a lot of solar energy due to high elevation but the area is covered with snow (high albedo) throughout the year. The snow cover is a cause of false detection from the cloud detection algorithm of satellite data. Eventually clearness index and solar radiation are underestimated. And southern region has high total precipitable water and aerosol optical depth, but high solar radiation reaches the surface as it is located on the relatively lower latitude. When calculated solar radiation is validated by DSWRF from NCEP/NCAR reanalysis, monthly mean solar radiation is 547.59 MJ which is approximately 2.89 MJ higher than DSWRF. The correlation coefficient between calculation and reanalysis data is 0.99 and the RMSE (Root Mean Square Error) is 6.17 MJ. It turned out to be highest correlation (r=0.94) in October, and lowest correlation (r=0.62) in March considering the error of cloud detection with melting and yellow sand.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.189-204
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• 2018

This study has developed a forest fire occurrence probability model for inaccessible areas such as North Korea and Demilitarized Zone and we have developed a real-time forest fire danger rating system that can be used in fire-related works. There are limitations on the research that it is impossible to conduct site investigation for data acquisition and verification for forest fire weather index model and system development. To solve this problem, we estimated the fire spots in the areas where access is impossible by using MODIS satellite data with scientific basis. Using the past meteorological reanalysis data(5㎞ resolution) produced by the Korea Meteorological Administration(KMA) on the extracted fires, the meteorological characteristics of the fires were extracted and made database. The meteorological factors extracted from the forest fire ignition points in the inaccessible areas are statistically correlated with the forest fire occurrence and the weather factors and the logistic regression model that can estimate the forest fires occurrence(fires 1 and non-fores 0). And used to calculate the forest fire weather index(FWI). The results of the statistical analysis show that the logistic models(p<0.01) strongly depends on maximum temperature, minimum relative humidity, effective humidity and average wind speed. The logistic regression model constructed in this study showed a relatively high accuracy of 66%. These findings may be beneficial to the policy makers in Republic of Korea(ROK) and Democratic People's Republic of Korea(DPRK) for the prevention of forest fires.

• Atmosphere
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• v.30 no.1
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• pp.91-102
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• 2020

MERRA-2 ozone and atmospheric data are utilized to test the usefulness of reanalysis-based tracer transport analysis for ozone in the tropical tropopause layer (TTL). Transport and mixing processes related to the seasonal variation of TTL ozone are examined using the tracer transport equation based on the transformed Eulerian mean, and the results are compared to previously proposed values from model analyses. The analysis shows that the seasonal variability of TTL ozone is mainly determined by two processes: vertical mean transport and horizontal eddy mixing of ozone, with different contributions in the Northern and Southern Hemispheres. The horizontal eddy mixing process explains the major portion of the seasonal cycle in the northern TTL, while the vertical mean transport dominates in the southern TTL. The Asian summer monsoon likely contributes to this observed difference. The ozone variability and related processes in MERRA-2 reanalysis show qualitatively similar features with satellite- and model-based analyses, and it provides advantages of fine-scale analyses. However, it still shows significant quantitative biases in ozone budget analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.93-93
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• 2021

아날로그 기법은 대표적인 일기도분류 기반의 공간상세화 기법으로써 과거 기상 현상이 미래 재현된다는 가정 하에 공간상세화를 수행하는 방법이다. 대규모 공간범위에 대한 아날로그 기법 적용 시에 지역 구분을 기반으로 적용하는 것이 바람직하다고 알려져 있으며, 기상 변수 간의 선형 상관성을 기반으로 지역구분을 수행하는 기법이 제안된 바 있다. 다만 기존 방법은 아날로그 시점을 찾는 범위가 지나치게 넓어지거나, 공간적으로 불연속적인 구간이 발생할 수 있다. 따라서 지역 간 기후변동성이 크고 도서가 다수 위치한 아시아 지역에서는 부적합한 방법이다. 본 연구에서는 아시아 지역에 대해 지역별 기후특성을 반영할 수 있는 아날로그 공간상세화 기법(BCIA)을 제안하고 평가하고자 한다. 본 연구에서는 쾨펜 기후구분과 ETCCDI 지수를 활용하여 기후특성을 고려한 지역구분을 수행하였으며, 이를 기반으로 아날로그 상세화를 수행하고 평가하였다. 평가결과 BCIA는 기존 아날로그 기법에 비해 기후 특성을 재현하는데 효과적인 것으로 나타났으며, 특히 극치 계열의 기후 지수, 강수일수와 관련된 기후 지수의 재현성이 우수한 것을 확인하였다. 본 연구에서는 기존 일부 지역에서만 시도되었던 지역별 아날로그 적용 방법론을 아시아 지역에 맞게 새롭게 제안하였고 이에 대한 활용성을 검증하였다는 점에서 가치가 있다.