• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.107-120
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• 2011

Several MODIS AQUA products have been compared with shipboard data to assess the possibility of using remote sensing to estimate particulate organic carbon (POC) concentration in the surface waters of the East Sea. A total of 30 POC profiles obtained in spring and summer seasons of the years of 2006~2010 were compared with remote sensing reflectance at various wavelengths and diffuse attenuation coefficient at 490 nm observed by MODIS AQUA. The algorithm thus established was $POC=266.85^*[R_{rs}(488)/R_{rs}(555)]^{-1.447}$ ($R^2=0.924$) with root mean square error of 20.9 mg $m^{-3}$. Remotely sensed POC contents derived using our algorithm appeared also not to be affected by the presence of non-POC component in suspended particulate matter. Therefore this algorithm could be applied to obtain POC concentration over the East Sea using MODIS Aqua observation.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.425-439
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• 2020

Through the operations of advanced geostationary meteorological satellite such as Himawari-8 and GK2A, higher resolution and frequency of AOD (Aerosol Optical Depth) data have become available. In this study, we analyzed the characteristics of Himawari-8/AHI (Advanced Himawari Imager) aerosol properties using the recent 4 years (2016~2019) of Sun photometer data observed at the five stations(Seoul National University, Yonsei University, Hankuk University of Foreign Studies, Gwangju Institute of Science and Technology, Anmyon island) which is a part of the AERONET (Aerosol Robotic Network). In addition, we analyzed the causes for the AOD differences between Himawari AOD and Sun photometer AOD. The results showed that the two AOD data are very similar regardless of geographic location, in particular, for the clear condition (cloud amount < 3). However, the quality of Himawari AOD data is heavily degraded compared to that of the clear condition, in terms of bias (0.05 : 0.21), correlation (0.74 : 0.64) and RMSE (Root Mean Square Error; 0.21 : 0.51), when cloud amount is increased. In general, the large differences between two AOD data are mainly related to the cloud amount and relative humidity. The Himawari strongly overestimates the AOD at all five stations when cloud amount and relative humidity are large. However, the wind speed, precipitable water, height of cloud base and Angstrom Exponent have been shown to have no effect on the AOD differences irrespective of geographic location and cloud amount. The results suggest that caution is required when using Himawari AOD data in cloudy conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.333-339
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• 2015

A thermal time-based phenology model of Kimchi cabbage was developed by using the field observed growth and temperature data for the purpose of accurately predicting heading and harvest dates among diverse cropping systems. In this model the lifecycle of Kimchi cabbage was separated into the growth stage and the heading stage, while the growth amount of each stage was calculated by optimal mathematical functions describing the response curves for different temperature regimes. The parameter for individual functions were derived from the 2012-2014 crop status report collected from seven farms with different cropping systems located in major Kimchi cabbage production area of South Korea (i.e., alpine Gangwon Province for the summer cultivation and coastal plains in Jeonnam Province for the autumn cultivation). For the model validation, we used an independent data set consisting of local temperature data restored by a geospatial correction scheme and observed harvest dates from 17 farms. The results showed that the root mean square error averaged across the location and time period (2012-2014) was 5.3 days for the harvest date. This model is expected to enhance the utilization of the Korea Meteorological Administration's daily temperature data in issuing agrometeorological forecasts for developmental stages of Kimchi cabbage grown widely in South Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.281-289
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• 2015

Information on sunshine duration and solar radiation are indispensable to the understanding of crop growth and development. Yet, relevant variables are not available in the Korea Meteorological Administration's (KMA) digital forecast. We proposed the methods of estimating sunshine duration and solar radiation based on the 'sky condition' of digital forecast products and validated using the observed data. The sky condition values (1 for clear, 2 for partly cloudy, 3 for cloudy, and 4 for overcast) were collected from 22 weather stations at 3-hourly intervals from August 2013 to July 2015. According to the observed relationship, these data were converted to the corresponding amount of clouds on the 0 to 10 scale (0 for clear, 4 for partly cloudy, 7 for cloudy, and 10 for overcast). An equation for the cloud amount-sunshine duration conversion was derived from the 3-year observation data at three weather stations with the highest clear day sunshine ratio (i.e., Daegwallyeong, Bukgangneung, and Busan). Then, the estimated sunshine hour data were used to run the Angstrom-Prescott model which was parameterized with the long-term KMA observations, resulting in daily solar radiation for the three weather stations. Comparison of the estimated sunshine duration and solar radiation with the observed at those three stations showed that the root mean square error ranged from 1.5 to 1.7 hours for sunshine duration and from 2.5 to $3.0MJ\;m^{-2}\;day^{-1}$ for solar radiation, respectively.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.10 no.1
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• pp.1-11
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• 2012

The equation of the step-drawdown test "$s_w=BQ+CQ^p$" written by Rorabaugh (1953) is suitable for drawdown increased non-linearly in the fractured rocks. It was found that value of root mean square error (RMSE) between observed and calculated drawdowns was very low. The calculated $C$ (well head loss coefficient) and $P$ (well head loss exponent) value of well head losses ($CQ^p$) ranged $3.689{\times}10^{-19}{\sim}5.825{\times}10^{-7}$ and 3.459~8.290, respectively. It appeared that the deeper depth in pumping well the larger drawdowns due to pumping rate increase. The well head loss in the fractured rocks, unlike that in porous media, is affected by properties of fractures (fractures of aperture, spacing, and connection) around pumping well. The $C$ and $P$ value in the well head loss is very important to interpret turbulence interval and properties of high or low permeability of fractured rock. As a result, regression analysis of $C$ and $P$ value in the well head losses identified the relationship of turbulence interval and hydraulic properties. The relationship between $C$ and $P$ value turned out very useful to interpret hydraulic properties of the fractured rocks.

• Journal of the Korean association of regional geographers
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• v.19 no.2
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• pp.352-361
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• 2013

This paper aims to analyze the root mean square errors of MODIS LST data and inland water temperature measurement data in order to use MODIS LST data as an input of numerical weather prediction model. MODIS LST data from July 2011 to June 2012 were compared to water temperature measurement data in the automated water quality measurement network. MODIS data have two composites: day-time and night-time. Monthly errors of day-time and night-time LST range $2{\sim}8^{\circ}C$ and $3{\sim}12^{\circ}C$, respectively. Temporally, monthly errors of day-time LST are less in fall and those of night-time LST are less in summer. Spatially, on the four major rivers including the Han, Nakdong, Geum, and Yeongsan rivers, the errors of Yeongsan river were the smallest, which location is the south-most among them. In this study, the errors of MODIS LST as an input of numerical weather prediction model were analyzed and the results can be used as an error level of MODIS LST data for inaccessible areas such as North Korea.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.121-131
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• 2016

The determination of feasible design flood is the most important to control flood damage in river management. Concentration time and storage constant in the Clark unit hydrograph method mainly affects magnitude of peak flood and shape of hydrograph. Model parameters should be calibrated using observed discharge but due to deficiency of observed data the parameters have been adopted by empirical formula. This study is to suggest concentration time and storage constant based on the observed rainfall-runoff data at GongDo stage station in the Ansung river basin. To do this, five criteria have been suggested to compute root mean square error(RMSE) and residual of oserved value and computed one. Once concentration time and storage constant have been determined from three rainfall-runoff event selected at the station, the five criteria based on observed hydrograph and computed hydrograph by the Clark model have been computed to determine the value of concentration time and storage constant. A criteria has been proposed to determine concentration time and storage constant based on the results of the observed hydrograph and the Clark model. It has also been shown that an exponent value of concentration time-cumulative area curve should be determined based on the shape of watershed.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.102-106
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• 2008

This study is to evaluated the impact of varying spatial resolutions of DEM (2 m, 10 m, and 30 m), land use (QuickBird, 1/25,000 and Landsat), and soil data (1/25,000 and 1/50,000) on the uncertainty of Soil and Water Assessment Tool (SWAT) predicted streamflow, sediment, T-N, and T-P transport in a small agricultural watershed ($1.21\;km^2$). SWAT model was adopted and the model was calibrated for a $255.4\;km^2$ watershed using 30 m DEM, Landsat land use, and 1/25,000 soil data. The model was run with the combination of three DEM, land use, and soil map respectively. The SWAT model was calibrated for 2 years (1999-2000) using daily streamflow and monthly water quality (SS, T-N, T-P) records from 1999 to 2000, and verified for another 2 years (2001-2002). The average Nash and Sutcliffe model efficiency was 0.59 for streamflow and the root mean square error were 2.08, 4.30 and 0.70 tons/yr for sediment, T-N and T-P respectively. The hydrological results showed that output uncertainty was biggest by spatial resolution of land use. Streamflow increase the watershed average CN value of QucikBird land use was 0.4 and 1.8 higher than those of 1/25,000 and Landsat land use caused increase of streamflow.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.77-87
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• 2018

Traffic Density is the most important of the three primary macroscopic traffic stream parameters, because it is most directly related to traffic demand(Traffic Engineering, 2004). It is defined as the number of existing vehicles within a given distance at a certain time. However, due to weather, road conditions, and cost issues, collecting density directly on the field is difficult. This makes studies of density less actively than those of traffic volume or velocity. For these reasons, there is insufficient attempts on divers collecting methods or researches on the accuracy of measured values. In this paper, we used the 'Density Measuring System' based on the synthesise technology of several camera images as a method to measure density. The collected density value by the 'Density Mesuring System' is selected as the true value based on the density define, and this value was compared with the density calculated by the traditional measurement methods. As a result of the comparison, the density value using the fundamental equation method is the closest to the true value as RMSE shows 1.8 to 2.5. In addition, we investigated some issues that can be overlooked easily such as the collecting interval to be considered on collecting density directly by calculating the moment density and the average density. Despite the actual traffic situation of the experiment site is LOS B, it is difficult to judge the real traffic situation because the moment density values per second are observed max 16.0 (veh/km) to min 2.0 (veh/km). However, the average density measured for 15 minutes at 30-second intervals was 8.3-7.9 (veh/km) and it indicates precisely LOS B.