• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.1-8
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• 2016

The Korea Meteorological Administration has operated the Automatic Weather Stations, of the average 13 km horizontal resolution, to observe rainfall. However, an additional RADAR network also has been operated in all-weather conditions, because AWS network could not observed rainfall over the sea. In general, the rain rate is obtained by estimating the relationship between the radar reflectivity (Z) and the rainfall (R). But this empirical relationship needs to be optimized on the rainfall over the Korean peninsula. This study was carried out to optimize the Z-R relationship in the summer of 2014 using a parallel Micro Genetic Algorithm. The optimized Z-R relationship, $Z=120R^{1.56}$, using a micro genetic algorithm was different from the various Z-R relationships that have been previously used. However, the landscape of the fitness function found in this study looked like a flat plateau. So there was a limit to the fine estimation including the complex development and decay processes of precipitation between the ground and an altitude of 1.5km.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.835-848
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• 2021

In Jeju Island which has peculiarity for its geological features and hydrology system, hydrological factor analysis for the effective water management is necessary. Because in-situ hydro-meteorological data is affected by surrounding environment, the in-situ dataset could not be the spatially representative for the study area. For this reason, remote sensing data may be used to overcome the limit of the in-situ data. In this study, applicability assessment of MOD16 evapotranspiration data, Globas Land Data Assimilation System (GLDAS) based evapotranspiration/soil moisture data, and Advanced SCATterometer (ASCAT) soil moisture product which were evaluated their applicability on other study areas was conducted. In the case of evapotranspiration, comparison with total precipitation and flux-tower based evapotranspiration were conducted. And for soil moisture, 6 in-situ data and ASCAT soil moisture product were compared on each site. As a result, 57% of annual precipitation was calculated as evapotranspiration, and the correlation coefficient between MOD16 evapotranspiration and GLDAS evapotranspiration was 0.759, which was a robust value. The correlation coefficient was 0.434, indicating a relatively low fit. In the case of soil moisture, in the case of the GLDAS data, the RMSE value was less than 0.05 at all sites compared to the in-situ data, and a statistically significant result was obtained as a result of the significance test of the correlation coefficient. However, for satellite data, RMSE over than 0.05 were found at Wolgak and there was no correlation at Sehwa and Handong points. It is judged that the above results are due to insufficient quality control and spatial representation of the evapotranspiration and soil moisture sensors installed in Jeju Island. It is estimated as the error that appears when adjacent to the coast. Through this study, the necessity of improving the existing ground observation data of hydrometeorological factors is emphasized.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.65-81
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• 2021

In this study, soil moisture and evapotranspiration were calculated throughout South Korea using the Korea Land Data Assimilation System(KLDAS) of the Korea-Land Surface Information System(K-LIS) built on the basis of the Land Information System (LIS). The hydrometeorological data sets used to drive K-LIS and build KLDAS are MERRA-2(Modern-Era Retrospective analysis for Research and Applications, version 2) GDAS(Global Data Assimilation System) and ASOS(Automated Synoptic Observing System) data. Since ASOS is a point-based observation, it was converted into grid data with a spatial resolution of 0.125° for the application of KLDAS(ASOS-S, ASOS-Spatial). After comparing the hydrometeorological data sets applied to KLDAS against the ground-based observation, the mean of R² ASOS-S, MERRA-2, and GDAS were analyzed as temperature(0.994, 0.967, 0.975), pressure(0.995, 0.940, 0.942), humidity (0.993, 0.895, 0.915), and rainfall(0.897, 0.682, 0.695), respectively. For the hydrologic output comparisons, the mean of R² was ASOS-S(0.493), MERRA-2(0.56) and GDAS (0.488) in soil moisture, and the mean of R² was analyzed as ASOS-S(0.473), MERRA-2(0.43) and GDAS(0.615) in evapotranspiration. MERRA-2 and GDAS are quality-controlled data sets using multiple satellite and ground observation data, whereas ASOS-S is grid data using observation data from 103 points. Therefore, it is concluded that the accuracy is lowered due to the error from the distance difference between the observation data. If the more ASOS observation are secured and applied in the future, the less error due to the gridding will be expected with the increased accuracy.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.60-60
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• 2019

OECD 발표에 의하면 물산업 관련 인프라 투자 전망은 전세계 GDP 대비 2010~2020년 약 1.01%에서 2020~2030년 약 1.03%로 확대될 전망으로 다른 통신, 전력, 철도 인프라 투자수요보다 많을 것으로 전망하고 있다(파이넨셜 뉴스, 2013.3.21.). 우리나라는 2005년 베트남 홍강종합개발사업을 시작으로 2015년 기준으로 세계 35개국에 진출하고 있다. 그러나 대부분의 물 산업 진출 대상 국가는 미계측 유역이 많고 지상에서 계측된 수문 자료가 부족한 실정이다. Namgung and Lee(2014)에 의하면 네팔의 수력발전소 건설에 관측된 강우량 자료가 없어 발전소 하류 10km 지점의 유하량 자료를 이용하여 자료의 정확도 검증을 대신하여 적용한 바 있다. 이와 같이 계측자료가 없거나 부족한 지역에 대하여 기상 위성을 이용하여 추정된 강수량 자료가 해당 지역의 강수 특성을 파악하는데 중요한 자료로 이용될 수 있다. 글로벌 위성 기반의 강수량 관측에 대한 역사는 1979년에 IR방법에 의해 위성으로부터 강우자료를 유도하는 개념이 도입된 이후 1987년 다중 채널의 마이크로파(MW) 복사계를 이용한 방법, 이후 두 IR과 MW를 혼합한 방법에서, 1997년 TRMM위성의 PR(Precpipitation Radar)의 레이더를 이용하는 방법, 그리고 2014년 GPM 핵심 위성(GPM Core Observatory)에 탑재된 Dual PR에 의한 방법으로 위성강수의 정확도를 매우 높여가고 있다. 본 연구는 대표적인 위성강수인 IMERG(Integrated MultisatellitE Retrievals for GPM)의 활용성을 높이기 위해 QGIS 기반의 위성강수 전처리 모듈을 개발하는 것을 목적으로 하고 있다. 위성강수를 활용하기 위해서는 위성강수의 정확도 평가가 선행되어야 한다. 본 연구를 통해 2017년 7월 중부지방 및 충청도 지방에 내린 강수자료를 비교한 결과 상관계수가 약 0.7정도로 상관성이 높은 것으로 분석되었고, 2018년 8월 9호 태풍 솔릭(Solik)에 대한 1시간의 시간해상도 분석 결과 상관계수 0.624로 위성강수의 활용성이 있음을 입증하였다. IMERG 위성강수의 활용성을 높이기 위하여 HDF5 포맷의 원시자료를 활용이 용이한 Tiff 로 변환하는 기능에서부터 특정범위 및 특정지점 추출 기능, Resampling 기능 등을 포함하는 전처리 모듈을 개발하였다.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1197-1208
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• 2019

Wildfire is a major natural disaster affecting socioeconomics and ecology. Remote sensing data have been widely used to estimate the wildfire danger with an advantage of higher spatial resolution. Among the several wildfire related indices using remote sensing data, Temperature Vegetation Dryness Index (TVDI) assesses wildfire danger based on both Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Although TVDI has physical advantages by considering both weather and vegetation condition, previous studies have shown TVDI does not performed well compare to other wildfire related indices over the Korean Peninsula. In this study we have attempted multiple modification to improve TVDI performance over the study region. In-situ measured air temperature was employed to increase accuracy, regression line was generated using monthly data to include seasonal effect, and TVDI was calculated at each province level to consider vegetation type and local climate. The modified TVDI calculation method was evaluated in wildfire cases and showed significant improvement in wildfire danger estimation.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.171-183
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• 2016

Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with $5.33^{\circ}C$. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with $2.34^{\circ}C$ and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

• Journal of the Korean earth science society
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• v.23 no.5
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• pp.424-435
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• 2002

This study analyzes the surface ozone and meteorological data in Daegu for a period from 1997 to 1999. It also investigates the meteorological characteristics of high ozone episodes. For this study the high ozone episode has been defined as a daily maximum ozone concentration higher than 100ppb in at least one station among six air quality monitoring stations in Daegu, Korea. The frequency of episodes is 13 days. The frequency is the highest in May and September. The average value of daily maximum ozone concentration is 81.6ppb, and 8-hour average ozone concentration is 58.6ppb for the high episodes. This shows that ozone pollution is continuous and wide-ranging in Daegu. The daily maximum ozone concentration is positively correlated to solar radiation and daily maximum temperature, but negatively correlated to relative humidity, wind speed and cloud amount. The maximal correlation coefficient to solar radiation is 0.45. The differences between high ozone episode day's daily mean meteorological value and monthly mean value are +1.58hPa for sea level pressure, +3.45${\circ}$C for maximum temperature, -5.69% for relative humidity, -0.46ms$^{-1}$ for wind speed, -1.79 for cloud amount, and +3.97MJm$^{-2}$ for solar radiation, respectively. This shows that strong solar radiation, low wind speed and no precipitation between 0700${\sim}$1100LST are favorite conditions for high ozone episodes. It is related to the morning stagnant condition.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.659-671
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• 2009

Fuzzy c-means clustering technique is applied to improve the accuracy of G/R ratio used for rainfall estimation by radar reflectivity. G/R ratio is computed by the ground rainfall records at AWS(Automatic Weather System) sites to the radar estimated rainfall from the reflectivity of Kwangduck Mt. radar station with 100km effective range. G/R ratio is calculated by two methods: the first one uses a single G/R ratio for the entire effective range and the other two different G/R ratio for two regions that is formed by clustering analysis, and absolute relative error and root mean squared error are employed for evaluating the accuracy of radar rainfall estimation from two G/R ratios. As a result, the radar rainfall estimated by two different G/R ratio from clustering analysis is more accurate than that by a single G/R ratio for the entire range.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.93-109
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• 2013

Geostatistical data or point-referenced data have the information on the monitoring stations of interest where the observations are measured. Practical geostatistical data are obtained from a wide variety of observational monitoring networks that are mainly operated by the Korean government. When we analyze geostatistical data and predict the expectations at unobservable locations, we can improve the reliability of the prediction by utilizing some relevant spatial data obtained from different observational monitoring networks and blend them with the measurements of our main interest. In this paper, we consider the hierarchical spatial linear model that enables us to link spatial variables from different resources but with similar patterns and guarantee the precision of the prediction. We compare the proposed model to a classical linear regression model and simple kriging in terms of some information criteria and one-leave-out cross-validation. Real application deals with Sulfur Dioxide($SO_2$) measurements from the urban air pollution monitoring network and wind speed data from the surface observation network.