• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.325-329
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• 1999

The characteristic of East Asian summer monsoon is investigated using 8-year (March 1987-February 1995) - averaged monthly and 5-day mean 1 degree latitude-longitude gridded GMS high-cloud-amount data (HCA). An analysis of these data shows the convective zone (ITCZ) clouds which defined as the percentage of the total grid area covered by clouds with a cloud-top temperature below the 400 hPa-level climatological temperature. The HCA increased clearly over equatorial zone during December and January and 30-40 $^{\circ}$N during May and June. These HCA patterns are coincided with seasonal cycles of summer monsoon which is introduced in historical references. The relationship with the summer monsoon winds as climatological changing of wind direction is analyzed by ECMWF re-analysis 2.5-degree latitude-longitude grid surface data which is calculated with 8-year averaged from January 1987 to January 1995. In addition, the monsoon winds are showed by separated U, V-wind components far manifestation a tendency of onset and retreat data of seasonal monsoon.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.371-382
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• 2005

We present a processing system for the Atmospheric Infrared Sounder (AIRS) sounding suite onboard Aqua satellite. With its unprecedented 2378 channels in IR bands, AIRS aims at achieving the sounding accuracy of radiosonde (1 K in 1-km layer for temperature and $10\%$ in 2-km layer for humidity). The core of the processor is the International MODIS/AIRS Processing Package (IMAPP) that performs the geometric and radiometric correction for generation of Level 1 brightness temperature and Level 2 geophysical parameters retrieval. The processor can produce automatically from received raw data to Level 2 geophysical parameters. As we process the direct-broadcast data almost for the first time among the AIRS direct-broadcast community, a special attention is paid to understand and verify the Level 2 products. This processor includes sub-systems, that is, the near real time validation system which made the comparison results with in-situ measurement data, and standard digital information system which carry out the data format conversion into GRIdded Binary II (GRIB II) standard format to promote active data communication between meteorological societies. This processing system is planned to encourage the application of geophysical parameters observed by AIRS to research the aqua cycle in the Korean peninsula.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.685-689
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• 2002

This study focused the relationship between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration (ET) temporal changes. Especially, the interannual change of the NDVI and ET from 1982 to 2000 at regional to continental scales was highlighted mainly over Asia. Monthly global NDVI data were acquired from Pathfinder AVHRR Land (PAL) data (1$\times$1 degree resolution). The monthly ET was estimated from assimilated atmospheric data provided from National Centers for Environmental Prediction (NCEP) (2.5$\times$2.5 degree resolution), and gridded global precipitation data of CPC Merged Analysis of Precipitation (CMAP) (2.5$\times$2.5 degree resolution). Significant positive correlations were found between the NDVI and ET interannual changes in May and June over western Siberia. Moreover, it was revealed that the most of area in Asia has positive correlation coefficient in May and June. These results delineate that the vegetation activity significantly contributes to the ET interannual change over extensive areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.159-159
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• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.166-166
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• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.243-251
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• 2018

The gridded simulation of crop growth, which would be useful for shareholders and policy makers, often requires specialized computation tasks for preparation of weather input data and operation of a given crop model. Here we developed an automated system to allow for crop growth simulation over a region using the DSSAT (Decision Support System for Agrotechnology Transfer) model. The system consists of modules implemented using R and shell script languages. One of the modules has a functionality to create weather input files in a plain text format for each cell. Another module written in R script was developed for GIS data processing and parallel computing. The other module that launches the crop model automatically was implemented using the shell script language. As a case study, the automated system was used to determine the maximum soybean yield for a given set of management options in Illinois state in the US. The AgMERRA dataset, which is reanalysis data for agricultural models, was used to prepare weather input files during 1981 - 2005. It took 7.38 hours to create 1,859 weather input files for one year of soybean growth simulation in Illinois using a single CPU core. In contrast, the processing time decreased considerably, e.g., 35 minutes, when 16 CPU cores were used. The automated system created a map of the maturity group and the planting date that resulted in the maximum yield in a raster data format. Our results indicated that the automated system for the DSSAT model would help spatial assessments of crop yield at a regional scale.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.283-294
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• 2021

Solar radiation is an important meteorological factor in the agricultural sector. The ground exposed to sunlight is highly influenced by the surrounding terrains especially in South Korea where the topology is complex. The solar radiation on an inclined surface is estimated using a solar irradiance correction factor for the slope of the terrain along with the solar radiation on a horizontal surface. However, such an estimation method assumes that there is no barrier in surroundings, which blocks sunlight from the sky. This would result in errors in estimation of solar radiation because the effect of shading caused by the surrounding terrain has not been taken into account sufficiently. In this study, the shading effect was simulated to obtain the brightness value (BV), which was used as a correction factor. The shaded relief images, which were generated using a 30m-resolution digital elevation model (DEM), were used to derive the BVs. These images were also prepared using the position of the sun and the relief of the terrain as inputs. The gridded data where the variation of direct solar radiation was quantified as brightness were obtained. The value of cells in the gridded data ranged from 0 (the darkest value) to 255 (the brightest value). The BV analysis was performed using meteorological observation data at 22 stations installed in study area. The observed insolation was compared with the BV of each point under clear and cloudless condition. It was found that brightness values were significantly correlated with the solar radiation, which confirmed that shading due to terrain could explain the variation in direct solar radiation. Further studies are needed to accurately estimate detailed solar radiation using shaded relief images and brightness values.

• ETRI Journal
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• v.23 no.1
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• pp.23-32
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• 2001

Examination of the statistical variation of integrated passive components is crucial for designing and characterizing the performance of multichip module (MCM) substrates. In this paper, the statistical analysis of parallel plate capacitors with gridded plates manufactured in a multilayer low temperature cofired ceramic (LTCC) process is presented. A set of integrated capacitor structures is fabricated, and their scattering parameters are measured for a range of frequencies from 50 MHz to 5 GHz. Using optimized equivalent circuits obtained from HSPICE, mean and absolute deviation is calculated for each component of each device model. Monte Carlo Analysis for the capacitor structures is then performed using HSPICE. Using a comparison of the Monte Carlo results and measured data, it is determined that even a small number of sample structures, the statistical variation of the component values provides an accurate representation of the overall capacitor performance.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.3
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• pp.277-290
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• 2008

In order to apply UTM coordinates conversion in zones larger than $14^{\circ}$ wide, a new conversion formula, based on the 12th expansion of Taylor series, is derived which is shown to be an extension of Thomas' formula(1952). Some examples of coordinate conversion between WGS84 and UTM are presented and convergences of computational results are also tested according to the order of formula. The present conversion formula can be used to make rectangular coordinate grid systems for numerical models to compute long wave propagation such as tide or tsunami around Korea.