• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.381-381
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• 2020

자료동화(Data Assimilation) 기법은 실시간 수문학적 예측에 있어 정확도 향상을 위해 필수적인 과정이다. 가장 대중적으로 사용되는 기법들 중 하나가 모델 상태변수와 매개변수를 동시에 업데이트할 수 있는 이중 앙상블 칼만 필터(Dual Ensemble Kalman Filter)이다. 이 방법은 정확도 개선 및 적용의 용이성 때문에 많은 연구 분야에서 사용되어져 왔지만, 앙상블을 생성하는 과정에서 상당시간이 소요되는 단점이 존재한다. 본 연구에서는 상태변수와 매개변수를 동시에 업데이트 하면서 홍수 예측의 정확성을 보장할 뿐만 아니라, 앙상블 생성에 있어 계산 효율을 크게 향상시킬 수 있는 기법을 제안한다. Polynomial Chaos Expansion(PCE) 기법을 사용하여 앙상블 칼만 필터를 모방(mimic)할 수 있는 새로운 대체필터(Surrogate Filter)를 개발하는 것을 목표로 한다. 구체적으로 대체필터를 구성하기 위한 다양한 필터를 설계하였다. 첫째 시간에 대해서 PCE가 변화하지 않는 '불변 필터'(즉, 전체 예측기간에 대해 하나의 필터를 사용하여 자료동화할 수 있는 대체필터)와, 매 시간마다 PCE가 변화하는 '시변 필터'(즉, 예측하는 매 시간마다 새로운 필터를 생성해야 하는 대체필터)를 설계하여 적용성, 정확성, 예측성 등을 비교하였다. 또한, PCE의 하이퍼 매개변수를 최적화하기 위한 최적의 프레임 워크가 제안되어, 대체필터를 구축하는 데 효율을 높이고 PCE의 과적합(overfitting) 현상을 피할 수 있도록 하였다. 본 연구에서 제안된 기법은 기존 단일 및 이중 앙상블 칼만 필터(EnKF)의 결과와 비교 검증하였으며, 그 결과는 다음과 같다. (1) 대체필터의 대부분은 원래 EnKF와 비슷한 정도의 불확실성을 설명할 수 있음; (2) 모든 대체 필터는 선행시간이 짧은 경우의 예측에 있어 우수한 결과를 제공하며, 시변 필터가 불변 필터보다 더 정확한 예측 결과를 제공함; (3) 대체필터는 원래 앙상블 칼만필터보다 최대 500배 빠른 속도로 성능을 향상시킬 수 있음. 제안된 대체필터는 자료동화를 수행하는 기존필터와 비슷한 정도의 정확성, 매우 향상된 효율성을 보장함을 확인할 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.274-274
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• 2021

토양수분은 가뭄, 홍수, 산불 및 산사태 등 자연재해 발생에 직간접적으로 영향을 미치기 때문에, 시·공간적으로 연속적인 토양수분 관측이 필요하다. 과거에는 TDR (Time Domain Reflectometry) 관측 장비를 설치하여 토양수분의 변화를 관측하였으나, 이러한 지점관측의 경우 하나의 관측지점에서 토양수분을 관측하기 때문에 공간적인 토양수분 변화를 나타내지 못한다. 최근 이러한 문제를 해결하기 위하여 인공위성 이미지 자료를 이용한 토양수분 산정에 관한 연구가 활발히 수행되고 있다. 그러나 SMOS (Soil Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive)와 같은 다양한 위성에서 관측된 토양수분은 낮은 공간해상도로 인한 불확실성이 커지는 단점이 있다. 최근 이러한 한계를 극복하기 위하여 광학위성영상과 달리 날씨의 영향을 받지 않으며 고해상도 이미지자료를 제공하는 Sentinel-1A/B 위성을 활용하여 토양수분을 관측하는 연구가 진행되고 있다. Sentinel-1은 10m의 높은 공간해상도를 제공하지만, 1~2주 주기로 영상취득이 가능하기 때문에 재방문시기와 같은 시간해상도 문제가 발생한다. 따라서 본 연구에서는 Sentinel-1A/B SAR 기반 후방산란계수와 농촌진흥청에서 제공하는 TDR 기반 토양수분 실측값을 이용하여 우리나라 토양수분 공간분포를 산정하였다. 산정된 Sentinel-1A/B 기반 토양수분과 토양수분자료동화기법을 연계하여 토양의 수리학적 매개변수를 추출하였으며, 추출된 매개변수와 기상자료를 이용하여 장기간(2001~2018) 일별 토양수분 공간분포를 산정하였다.

• Asia pacific journal of information systems
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• v.30 no.2
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• pp.397-419
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• 2020

Business analytics is a management tool for achieving significant business performance improvements. Many organizations fail to or only partially achieve their business objectives and goals from business analytics. Business analytics adoption is a multi-stage complex activity consisting of evaluation, adoption, and assimilation stages. Several research papers have been published in the field of business analytics, but the research on multi-stage BA adoption is fewer in number. This study contributes to the scant literature on the multi-stage adoption model by identifying the critical themes for evaluation, adoption, and assimilation stages of business analytics. This study uses the thematic content analysis of peer-reviewed published academic papers as a research technique to explore the key themes of business analytics adoption. This study links the critical themes with the popular theoretical foundations: Resource-Based View (RBV), Dynamic Capabilities, Diffusion of Innovations, and Technology-Organizational-Environmental (TOE) framework. The study identifies twelve major factors categorized into three key themes: organizational characteristics, innovation characteristics, and environmental characteristics. The main organizational factors are top management support, organization data environment, centralized analytics structure, perceived cost, employee skills, and data-based decision making culture. The major innovation characteristics are perceived benefits, complexity, and compatibility, and information technology assets. The environmental factors influencing BA adoption stages are competition and industry pressure. A conceptual framework for the multi-stage BA adoption model is proposed in this study. The findings of this study can assist the practicing managers in developing a stage-wise operational strategy for business analytics adoption. Future research can also attempt to validate the conceptual model proposed in this study.

• Atmosphere
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• v.21 no.2
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• pp.173-183
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• 2011

Production of reanalysis data is important since it contributes to develop all fields of atmospheric sciences and to profit national economy. The developed countries such as USA, EU, and Japan have manufactured the global reanalysis data since the 1990s, but their data present a lack of detailed regional climates. For those who need to analyze the regional climate in/around Korea, a high-resolution reanalysis data should essentially be made. This study reviewed the existing reanalysis data and the applications, and the available observations for the data production. We also investigated the opinions and needs of the potential data users in Korea. We suggest the specifications of the data to have the domain of 55-5N, 80-150E (which includes Mongolia and most Southeast Asian countries), the spatial resolution of 10-20 km, and the period of most recent 30 years. With the specifications and climate models operated in KMA, this study argues that production of the reanalysis data with functional climate information is feasible in both technical and economic aspects. Finally, for successful data production, the framework of the future reanalysis data project was suggested.

• Proceedings of the KSRS Conference
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• v.1
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• pp.142-145
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• 2006

The synergism of active and passive microwave techniques for hurricane ocean wind remote sensing is explored. We performed the analysis of Windsat data for Atlantic hurricanes in 2003-2005. The polarimetric third Stokes parameter observations from the Windsat 10, 18 and 37 GHz channels were collocated with the ocean surface winds from the Holland wind model, the NOAA HWind wind vectors and the Global Data Assimilation System (GDAS) operated by the National Center for Environmental Prediction (NCEP). The collocated data were binned as a function of wind speed and wind direction, and were expanded by sinusoidal series of the relative azimuth angles between wind and observation directions. The coefficients of the sinusoidal series, corrected for atmospheric attenuation, have been used to develop an empirical geophysical model function (GMF). The Windsat GMF for extreme high wind compares very well with the aircraft radiometer and radar measurements.

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.631-641
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• 2012

In this study, satellite data (MTSAT-1R), a numerical weather prediction model, RDAPS (Regional Data Assimilation and Prediction System) output, ground weather station data, and artificial neural networks were used to improve the accuracy of summer rainfall forecasts. The developed model was applied to the Seoul station to forecast the rainfall at 3, 6, 9, and 12-hour lead times. Also to reflect the different weather conditions during the summer season which is related to the frontal precipitation and the cyclonic precipitation such as Jangma and Typhoon, the neural network models were formed for two different periods of June-July and August-September respectively. The rainfall forecast model was trained during the summer season of 2006 and 2008 and was verified for that of 2009 based on the data availability. The results demonstrated that the model allows us to get the improved rainfall forecasts until lead time of 6 hour, but there is still a large room to improve the rainfall forecast skill.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1611-1623
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• 2021

Thisstudy demonstratessoil moisture and evapotranspiration performance using Korea Land Data Assimilation System (KLDAS) under Korea Land Information System (KLIS). Spin-up was repeated 8 times in 2018. In addition, low-resolution and high-resolution meteorological data were generated using meteorological data observed by Korea Meteorological Administration (KMA), Rural Development Administration (RDA), Korea Rural Community Corporation (KRC), Korea Hydro & Nuclear Power Co.,Ltd. (KHNP), Korea Water Resources Corporation (K-water), and Ministry of Environment (ME), and applied to KLDAS. And, to confirm the degree of accuracy improvement of Korea Low spatial resolution (hereafter, K-Low; 0.125°) and Korea High spatial resolution (hereafter, K-High; 0.01°), soil moisture and evapotranspiration to which Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and ASOS-Spatial (ASOS-S) used in the previous study were applied were evaluated together. As a result, optimization of the initial boundary condition requires 2 time (58 point), 3 time (6 point), and 6 time (3 point) spin-up for soil moisture. In the case of evapotranspiration, 1 time (58 point) and 2 time (58 point) spin-ups are required. In the case of soil moisture to which MERRA-2, ASOS-S, K-Low, and K-High were applied, the mean of R² were 0.615, 0.601, 0.594, and 0.664, respectively, and in the case of evapotranspiration, the mean of R² were 0.531, 0.495, 0.656, and 0.677, respectively, indicating the accuracy of K-High was rated as the highest. The accuracy of KLDAS can be improved by securing a large number of ground observation data through the results of this study and generating high-resolution grid-type meteorological data. However, if the meteorological condition at each point is not sufficiently taken into account when converting the point data into a grid, the accuracy is rather lowered. For a further study, it is expected that higher quality data can be produced by generating and applying grid-type meteorological data using the parameter setting of IDW or other interpolation techniques.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.13 no.4
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• pp.179-183
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• 1980

The efficiency of energy transfer by a population of the farmed pacific oyster, Crassostrea gigas was studied during culture period of 10 months July 1979-April 1980, in Geoje-Hansan Bay near Chungmu City. Energy use by the farmed oyster population was calculated from estimates of half-a-month unit age specific natural mortality rate and data on growth, gonad output, shell organic matter production and respiration. Total mortality during the culture period was estimated approximate $36\%$ from data on survivor individual number per cluster. Growth may be dual consisted of a curved line during the first half culture period (July-November) and a linear line in the later half period (December-April). The first half growth was approximated by the von Bertalanffy growth model; shell height, $SH=6.33\;(1-e^{0.2421(t+0.54)})$, where t is age in half-a-month unit. In the later half growth period shell height was related to t by SH=4.44+0.14t. Dry meat weight (DW) was related to shell height by log $DW=-2.2907+2.589{\cdot}log\;SH,\;(2, and/or log $DW=-5.8153+7.208{\cdot}log\;SH,\;(5. Size specific gonad output (G) as calculated by condition index of before and after the spawning season, was related to shell height by $G=0.0145+(3.95\times10^{-3}{\times}SH^{2.9861})$. Shell organic matter production (SO) was related to shell height by log $SO=-3.1884+2.527{\cdot}1og\;SH$. Size and temperature specific respiration rate (R) as determined in biotron system with controlled temperature, was related to dry meat weight and temperature (T) by log $R=(0.386T-0.5381)+(0.6409-0.0083T){\cdot}log\;DW$. The energy used in metabolism was calculated from size, temperature specific respiration and data on body composition. The calorie contents of oyster meat were estimated by bomb calorimetry based on nitrogen correction. The assimilation efficiency of the oyster estimated directly by a insoluble crude silicate method gave $55.5\%$. From the information presently available by other workers, the assimilation efficiency ranges between $40\%\;and\;70\%$. Twenty seven point four percent of the filtered food material expressed by energy value for oyster population was estimated to have been rejected as pseudofaeces : $17.2\%$ was passed as faeces; $35.04\%$ was respired and lost as heat; $0.38\%$ was bounded up in shell organics; $2.74\%$ was released as gonad output, $2.06\%$ was fell as meat reducing by mortality. The remaining $15.28\%$ was used as meat production. The net efficiency of energy transfer from assimilation to meat production (yield/assimilation) of a farm population of the oyster was estimated to be $28\%$ during culture period July 1979-April 1980. The gross efficiency of energy transfer from ingestion to meat production (yield/food filtered) is probably between $11\%\;and\;20\%$.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.889-902
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• 2020

This study was performed in Seolmacheon basin to evaluate the adequacy of GLDAS (Global Land Data Assimilation System) and GLEAM (Global Land Evaporation Amsterdam Model) evapotranspiration data. The verification data necessary for the evaluation of adequacy were calculated after processing the latent heat flux data produced in the Seolmacheon basin with the Koflux program. In order to gap-fill the empty period, alternative evapotranspiration was calculated in three ways: FAO-PM (Food and Agriculture Organization-Penman Monteith), MDV (Mean Diurnal Variation) and Kalman Filter. This study selected Kalman Filter method as the data gap-filling method because it showed the best Bias and RMSE among the three methods. The amount of GLDAS spatial evapotranspiration was calculated as Noah (version 2.1) with a time interval of 3 hours and a spatial resolution of 0.25°. The amount of GLEAM spatial evapotranspiration was calculated using GLEAM (version 3.1a). This study evaluated the spatial evapotranspiration of GLDAS and GLEAM as the evapotranspiration based on eddy covariance. As a result of evaluation, GLDAS spatial evapotranspiration showed better results than GLEAM. Accordingly, in this study, the GLDAS method was proposed as a method for calculating the amount of spatial evapotranspiration in the Seolmacheon basin.

• Atmosphere
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• v.17 no.3
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• pp.269-279
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• 2007

This study investigates the possibility of seasonal prediction for tropical cyclone activity in the western North Pacific by using a dynamical modeling approach. We use data from the SMIP/HFP (Seasonal Prediction Model Inter-comparison Project/Historical Forecast Project) experiment with the Korea Meteorological Administration's GDAPS (Global Data Assimilation and Prediction System) T106 model, focusing our analysis on model-generated tropical cyclones. It is found that the prediction depends primarily on the tropical cyclone (TC) detecting criteria. Additionally, a scaling factor and a different weighting to each ensemble member are found to be essential for the best predictions of summertime TC activity. This approach indeed shows a certain skill not only in the category forecast but in the standard verifications such as Brier score and relative operating characteristics (ROC).