• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.2
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• pp.119-126
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• 2002

Recently, the flux community in Korea established a new regional flux network, so-called KoFlux, which will provide an infrastructure for collecting, synthesizing, and analysing long-term measurements of energy and mass exchange between the atmosphere and the various vegetated surfaces. KoFlux requires the collection of long time series of raw data, and a large amount of data are expected to accumulate due to continuous flux observations at each KoFlux sites. Therefore, we need a systematic and efficient tool to manage these raw data. As a part of this effort, a computer program far processing raw data measured from micrometeorological field experiments was developed for the flux community in Korea. In this paper, we introduce this program for processing raw data to estimate fluxes and other turbulent statistics and explain the micrometeolological processes coded in this data-processing program. Also, we show some examples on how to run the program and handle the outputs for the unique purpose of research interest.

• Spatial Information Research
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• v.20 no.4
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• pp.47-55
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• 2012

Normalized Difference Vegetation Index (NDVI) has been used to measure and monitor plant growth, vegetation cover, and biomass from multispectral satellite data. It is also a valuable index in forest applications, providing forest resource information. In this research, an approach for monitoring forest change using MODIS NDVI time series data is explored. NDVI difference-based approaches for a specific point in time have possible accuracy problems and are lacking in monitoring long-term forest cover change. It means that a multi-time NDVI pattern change needs to be considered. In this study, an efficient methodology to consider long-term NDVI pattern is suggested using a harmonic model. The suggested method reconstructs MODIS NDVI time series data through application of the harmonic model, which corrects missing and erroneous data. Then NDVI pattern is analyzed based on estimated values of the harmonic model. The suggested method was applied to 49 NDVI time series data from Aug. 21, 2009 to Sep. 6, 2011 and its usefulness was shown through an experiment.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.2
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• pp.38-45
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• 2001

Three lines from $36_{\circ}$ N, $124_{\circ}$ E, and $132_{\circ}$ E of the East Sea and the Yellow Sea were chosen to extract spectra of normalized water leaving radiances. Comparative analysis of the OCTS algorithm and SeaWiFS(OC-2) algorithms was presented here. OCTS algorithm have more overestimate than SeaWiFS(OC-2 algorithm) for detecting chlorophyll concentration. Atmospheric correction algorithm that is excluded the effect of SS in the case 2 water need for long term ocean environmental monitoring of the East Sea and the Yellow Sea. And, considered the effect of CDOM and SS, bio-optical algorithm have to be developed in this research.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.93-98
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• 2016

Recently, the project related to the smart grid are being actively studied around the developed world. In particular, the long-term stabilization measures distributed power supply problem has been highlighted. In this paper, we propose a three-dimensional numerical weather prediction models to compare the error rate information which combined with the physical models and statistical models to predict the output of distributed power. Proposed model can predict the system for a stable power grid-can improve the prediction information of the distributed power. In performance evaluation, proposed model was a generation forecasting accuracy improved by 4.6%, temperature compensated prediction accuracy was improved by 3.5%. Finally, the solar radiation correction accuracy is improved by 1.1%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.666-671
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• 2019

The flight controller(FC) used in small-sized drone was developed as simple structure does not perform complex operations because it uses different MCU with large-sized drone. Also, the balance control of small-sized drone should be simpler than Kalman filter using complex filter and the method using Complementary filter has relatively more operations. So, the method to realize the balance control on small-sized drone effectively using two-track control operating as proper method for above is suggested in this research. This method is a system maintaining effective balance with simple structure and less operations by operating adaptively for the unbalance of the drone with the acceleration sensor with the advantage which performing accurate correction by data processing for long term change and gyroscope sensor maintaining the balance of the drone by data processing for short term change. It is confirmed that stable operation was performed mostly based on the test result for repeatable test more than 100 times using two-track control and it maintained normal state operation more than 98% excluding the difficulty of maintaining normal operation when meets sudden and rapid wind yet.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1301-1314
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• 2022

Normalized Difference Vegetation Index (NDVI) is utilized as an indicator to represent the vegetation condition on the land surface in various applications such as land cover, crop yield, agricultural drought, soil moisture, and forest disaster. However, satellite optical sensors for visible and infrared rays cannot see through the clouds, so the NDVI of the cloud pixel is not a valid value for the land surface. This study proposed a real-time correction of the underestimation noise for GEO-KOMPSAT-2A (GK2A) daily NDVI and made sure its feasibility through the quantitative comparisons with Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and the qualitative interpretation of time-series changes. The underestimation noise was effectively corrected by the procedures such as the time-series correction considering vegetation phenology, the outlier removal using long-term climatology, and the gap filling using rigorous statistical methods. The correlation with MODIS NDVI was higher, and the difference was lower, showing a 32.7% improvement compared to the original NDVI product. The proposed method has an extensibility for use in other satellite products with some modification.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.321-334
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• 2021

A fixed reflectivity-rainfall relationship approach, such as the Marshall-Palmer relationship, for an entire year and different seasons, can be problematic in cases where the relationship varies spatially and temporally throughout a region. From this perspective, this study explores the use of long-term radar reflectivity for South Korea to obtain a nationwide calibrated Z-R relationship and the associated uncertainties within a Bayesian inference framework. A calibrated spatially structured pattern in the parameters exists, particularly for the wet season and parameter for the dry season. A pronounced region of high values during the wet and dry seasons may be partially associated with storm movements in that season. Overall, the radar rainfall fields based on the proposed modeling procedure are similar to the observed rainfall fields. In contrast, the radar rainfall fields obtained from the existing Marshall-Palmer relationship show a systematic underestimation. In the event of high impact weather, it is expected that the value of national radar resources can be improved by establishing an active watershed-level hydrological analysis system.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.101-101
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• 2021

본 연구에서는 금강 유역(9,645.5 km²)을 대상으로 SWAT(Soil and Water Assessment Tool)을 이용하여 장기간 지하수 양수와 산림 성장이 수문에 미치는 영향을 평가하였다. 1976년부터 2015년까지 40년의 지하수 이용량(GU)과 산림 성장(FG) 자료를 10년 단위(1980s; 1976~1985, 1990s; 1986~1995, 2000s; 1996~2005, 2010s; 2006~2015)로 구축하고, SWAT 입력자료와 증발산량 매개변수로 사용하여 연대별 지하수 양수와 산림 생장을 반영하였다. 금강 유역 내 위치한 2개의 다목적댐과 3개의 다기능 보에서 관측된 일별 유량으로 2006년부터 2015까지 10년을 검·보정하여 모델의 적용성을 검증하였다. 5지점에 대한 검보정 결과, NSE는 0.76에서 0.79, R²는 0.78에서 0.81, RMSE는 0.55 mm/day에서 1.96 mm/day, PBIAS는 -5.48%에서 8.56%로 통계적으로 유의한 수준으로 분석되었다. 적용성을 검증한 SWAT에 각 연대에 맞는 GU와 FG 정보를 입력하여 수문을 모의하였으며, 두 요인이 물순환에 미치는 영향을 평가하기 위하여 기상조건은 2010s로 고정하였다. 모의결과, GU와 FG 변화가 주는 영향으로 유역 출구의 총유출량은 7.8%(60.7 mm/year) 감소하였으며, 1980s과 2010s에서 각각 775.0 mm/year, 714.3 mm/year의 값을 보였다. 물순환 분석 결과, 지표유출, 중간유출, 기저유출, 침투량, 토양수분은 감소하는 경향을 보였고, 증발산량과 지하수 충전량(GWR)은 증가하는 경향을 보였다. GU의 증가로 인한 지하수위의 지속적인 감소가 GWR이 증가하는 원인으로 판단하였으며, 강우 기간에 포장용수량 이상의 토양수가 빠르게 흘러 지하수 충전량을 증가시켰다고 추론하였다. 또한, 유황 곡선을 분석한 결과, 유량이 지하수 양수와 산림 생장에 영향을 받은 지역에서 시간 흐름에 따라 감소하였고, 저유량 구간에서 상대적으로 많이 감소하였다.