• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.40-40
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• 2022

기상 데이터의 사용은 수문 모의에 있어서 필수적인 요소이다. 본 연구에서는 다양한 기상 자료를 사용하여 남한 전체를 대상으로 수문 모의를 진행하여 기상 데이터의 적합성을 판단하고자 한다. 이를 위하여 본 연구에서는 그리드 기반의 기상 자료로 강수 자료와 온도자료를 사용하였으며 그 적합성을 살펴보았으며 특히 강수자료는 총 11개로 6개의 위성기반 자료 (CMORPH, MSWEP, MERRA, TRMM 및 TRMM-RT)와 5개의 재분석 기반 자료 (ERA5, JRA-55, CPC-U, NCEP-DOE 및 K-Hidra)를 살펴보았고 온도자료는 4개의 자료 (MERRA, ERA5, CPC-U 및NCEP-DOE)를 선정하여 살펴보았다. 남한 전체를 대상으로 수문 모형을 구축하기 위하여 본 연구에서는 Variable Infiltration Capacity (VIC) 모형을 사용하였으며 총 44개의 조합 (강수자료 11개 × 온도자료 4개)에 비교 결과를 토대로 서로 다른 조합의 앙상블을 생성하여 수문 모의 결과를 비교하였다. 결과적으로 온도자료는 상대적으로 그 자료마다 모의 결과의 차이가 적게 나타났지만 강수자료는 자료에 따라 수문 모의 결과에 큰 차이를 보여 그 영향력이 큰 것으로 확인하였다. 이를 통하여 본 연구에서는 기상 자료가 수문모형의 성능에 영향을 미치기 때문에 적합한 기상 자료를 선정하는 것이 수문 모델링을 진행하기 전 단계로 필수적인 과정이라는 것을 확인했다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.351-362
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• 2020

Numerical simulations of the storm surge and waves induced by the Typhoon Maemi incident on the south sea of Korea in 2003 are performed 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 harbours along the coasts of Korea. For the waves occurring coincidentally with the storm surges the calculated significant wave heights are compared with the measured data. Based on the comparison of surge and wave heights the assessment of the reliability of various weather fields is performed. As a result the JMA-MSM weather fields gives the highest reliability, and the weather field obtained using JTWC best track information gives also relatively good agreement. The ECMWF-ERA5 gives in general surge and wave heights weaker than the measured. The reliability of NCEP-CFSR turns out to be the worst for this special case of Typhoon Maemi. Based on the results of this study it is found that the reliable weather fields are essential for the accurate simulation of storm surges and waves.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.687-701
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• 2014

Sea ice is generally accepted as an important factor to understand the process of earth climate changes and is the basis of earth system models for analysis and prediction of the climate changes. To continuously monitor sea ice changes at kilometer scale, it is demanded to create more accurate grid data from the current, limited sea ice data. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Sea Ice Concentration (SIC) from 10 km to 1 km resolution using a weighting scheme of sea ice days ratio derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sea ice cover product that has a high correlation with the SIC. In a case study for Okhotsk Sea, the sea ice areas of both data (before and after downscaling) were identical, and the monthly means and standard deviations of SIC exhibited almost the same values. Also, Empirical Orthogonal Function (EOF) analyses showed that three kinds of SIC data (ERA-Interim, original AMSR2, and downscaled AMSR2) had very similar principal components for spatial and temporal variations. Our method can apply to downscaling of other continuous variables in the form of ratio such as percentage and can contribute to monitoring small-scale changes of sea ice by providing finer SIC data.

• Journal of the Korean earth science society
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• v.33 no.7
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• pp.583-595
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• 2012

The potential for tropical cyclogenesis in a given oceanic and atmospheric environments can be represented by genesis potential index (GPI). Using the 18 Coupled Model Inter Comparison Project phase 5 (CMIP5) models, the annual cycle of GPI and interannual variability of GPI are analyzed in this study. In comparison, the annual cycle of GPI calculated from reanalysis data is revisited. In particular, GPI differences between CMIP5 models and reanalysis data are compared, and the possible reasons for the GPI differences are discussed. ENSO (El Nino and Southern Oscillation) has a tropical phenomenon, which affects tropical cyclone genesis and its passages. Some dynamical interpretations of tropical cyclogenesis are suggested by using the fact that GPI is a function of four large-scale parameters. The GPI anomalies in El Nino or La Nina years are discussed and the most contributable factors are identified in this study. In addition, possible dynamics of tropical cyclogenesis in the Northern Hemisphere Pacific region are discussed using the large-scale factors.

• Atmosphere
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• v.26 no.4
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• pp.523-540
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• 2016

The effects of satellite observations on large-scale atmospheric circulations in the reanalysis data are investigated by comparing the latest Japanese Meteorological Association's reanalysis data (JRA-55) and its family data, JRA-55 Conventional (JRA-55C). The latter is identical to the former except that satellite observations are excluded during the data assimilation process. Only conventional datasets are assimilated in JRA-55C. A simple comparison revealed a considerable difference in temperature and zonal wind fields in both the stratosphere and troposphere. Such differences are particularly large in the Southern Hemisphere and whole stratosphere where conventional ground-based measurements are limited. The effects of satellite observations on the zonal-mean tropospheric circulations are further examined in terms of the Hadley cell, eddy-driven jet, and mid-latitude storm tracks. In both hemispheres, JRA-55C exhibits slightly weaker and narrower Hadley cell than JRA-55. This is consistent with a weaker diabatic heating in JRA-55C. The eddy-driven jet shows a small difference in its latitudinal location only in the Southern Hemisphere. Likewise, while the Northern-Hemisphere storm tracks are quantitatively similar in the two datasets, Southern-Hemisphere storm tracks are relatively weaker in JRA-55C than in JRA-55. Their difference is comparable to the uncertainty between reanalysis datasets, indicating that satellite data assimilation could yield significant corrections in the zonal-mean circulation in the Southern Hemisphere.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.535-546
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• 2011

This study develops statistical models for the binary forecast of Asian dust days over South Korea in the winter season. For this study, we used three kinds of data; the rst one is the observed Asian dust days for a period of 31 years (1980 to 2010) as target values, the second one is four meteorological factors(near surface temperature, precipitation, snowfall, ground wind speed) in the source regions of Asian dust based on the NCEP reanalysis data and the third one is the large-scale climate indices. Four kinds of statistical models(multiple regression models, logistic regression models, decision trees, and support vector machines) are applied and compared based on skill scores(hit rate, probability of detection and false alarm rate).

• Journal of the Korean Geographical Society
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• v.46 no.1
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• pp.36-49
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• 2011

This paper aims to investigate the frequency and intensity variations of Okhotsk high pressure system focused on the Korean Peninsula. Weather chart (00UTC), daily weather data and reanalysis data were used. The first occurrence date of Okhotsk high pressure system tends to be earlier in those years that surrounding land air temperature in April is high. The frequency of Okhotsk high has recently decreased, and its intensity tends to be stronger when the difference between sea surface temperature and surrounding land air temperature is big. The frequency of Okhotsk high in April, May, June and July increases when surrounding land air temperature is high, and its intensity grows when the difference between surrounding land air temperature and sea surface temperature is big. The frequency of Okhotsk high may increase and its intensity may increase when the first occurrence date comes earlier. In June, however, the reverse may apply.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1261-1272
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• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.