• 한국실내디자인학회논문집
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• 제15권6호
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• pp.238-246
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• 2006

Recent trends of interior design have a focus on creation of more various meanings rather than past ideology which sought after the compatibility to the function of modem design. These trends requires integral understanding of social and cultural ideologies with a sens of values for a certain periods. In addition, they also require creativity which able to read, find and solve consumer's diverse demand and desire. Considering the effort of trend forecasting in Korea is still heavily rely on the foreign trend shows, it is natural to attempt to study the analytical forecasting methodology based upon more systematic principles which lead to more objective outcome, when the understanding, forcasting and analysis of interior decoration trend are required. In this thesis, the analysis and forecasting of interior decoration trend are studied by means of verbal image code process which involves the induction of design concept through data extraction, classification and analysis, in order to understanding and satisfying the diversified consumer's demand and trend. The coding process of verbal image is understanding as general concept. by extracting common elements from abstract and individual image, and/or specific concept. Therefore, it is proposed that the database building and data mining process of verbal Image, and subsequent development of programming skill can be applied as more efficient tool for various verbal image process.

• 대기
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• 제28권3호
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• pp.233-245
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• 2018

This study explores the Subseasonal-to-Seasonal (S2S) prediction skills of the Northern Hemisphere mid-latitude geopotential height in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment. The prediction skills are quantitatively verified for the period of 1991~2010 by computing the Anomaly Correlation Coefficient (ACC) and Mean Square Skill Score (MSSS). GloSea5 model shows a higher prediction skill in winter than in summer at most levels regardless of verification methods. Quantitatively, the prediction limit diagnosed with ACC skill of 500 hPa geopotential height, averaged over $30^{\circ}N{\sim}90^{\circ}N$, is 11.0 days in winter, but only 9.1 days in summer. These prediction limits are primarily set by the planetary-scale eddy phase errors. The stratospheric prediction skills are typically higher than the tropospheric skills except in the summer upper-stratosphere where prediction skills are substantially lower than upper-troposphere. The lack of the summer upper-stratospheric prediction skill is caused by zonal mean error, perhaps strongly related to model mean bias in the stratosphere.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• 대기
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• 제21권4호
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• pp.497-506
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• 2011

Korean mid- and upper-level aviation turbulence guidance (KTG) system is developed using the unified model (UM)-based regional data assimilation and prediction system (RDAPS) of the Korea Meteorological Administration. The KTG system includes three steps. First, the KTG system calculates a suite of diagnostics in the UM-RDAPS domain. Second, component diagnostics that have different units and numerical magnitudes are normalized into the values between 0 and 1, according to their own thresholds in the KTG system. Finally, normalized diagnostics are combined into one KTG predictor by measuring the weighting scores based on the probability of detection, which is calculated using the observed pilot reports (PIREPs) exclusively of moderate-or-greater (MOG) and null (NIL) intensities. To investigate the optimal performance of the KTG system, two types (RD-KTG and UM-KTG) of the KTG systems are developed and evaluated using the PIREPs over Korea and East Asia. Component diagnostics and their thresholds in the RD-KTG are founded on the 8-yrs (2002.12-2010.11) MM5-based RDAPS (previous version of the RDAPS; ${\Delta}x$ = 30 km) and PIREPs data, while those in the UM-KTG are based on the 6 months (2010.12-2011.5) UM-based RDAPS (${\Delta}x$ = 12 km) and PIREPs data. In comparison between the RD-KTG and UM-KTG, overall performance of the UM-KTG (0.815) is better than that of the RD-KTG (0.79) during the recent 6 months, because forecasting skill for the upper-level wind is higher in the UM-RDAPS than in the MM5-RDAPS. It is also found that the UM-KTG is more efficient than the RD-KTG according to the statistical evaluations and sensitivity tests to the number of component diagnostics.

• 대기
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• 제32권4호
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• pp.395-409
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• 2022

A new soil moisture initialization scheme is applied to the Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6). It is designed to ingest the microwave soil moisture retrievals from Soil Moisture Active Passive (SMAP) radiometer using the Local Ensemble Transform Kalman Filter (LETKF). In this technical note, we describe the procedure of the newly-adopted initialization scheme, the change of soil moisture states by assimilation, and the forecast skill differences for the surface temperature and precipitation by GloSea6 simulation from two preliminary experiments. Based on a 4-year analysis experiment, the soil moisture from the land-surface model of current operational GloSea6 is found to be drier generally comparing to SMAP observation. LETKF data assimilation shows a tendency toward being wet globally, especially in arid area such as deserts and Tibetan Plateau. Also, it increases soil moisture analysis increments in most soil levels of wetness in land than current operation. The other experiment of GloSea6 forecast with application of the new initialization system for the heat wave case in 2020 summer shows that the memory of soil moisture anomalies obtained by the new initialization system is persistent throughout the entire forecast period of three months. However, averaged forecast improvements are not substantial and mixed over Eurasia during the period of forecast: forecast skill for the precipitation improved slightly but for the surface air temperature rather degraded. Our preliminary results suggest that additional elaborate developments in the soil moisture initialization are still required to improve overall forecast skills.

• 대기
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• 제32권2호
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• pp.149-162
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• 2022

This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation for the period of 1991~2016. Additionally, we evaluated the prediction skill of those variables depending on the combination of the initial dates in the sub-seasonal scale for the dust source region affecting South Korea. The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) from GloSea5-ADAM were compared to that from Synoptic observation and European Centre for medium range weather forecasts reanalysis v5, respectively, based on Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC) as evaluation criteria. In general, the Asian dust and meteorological variables in the source region showed high ACC in the prediction scale within one month. For all variables, the use of the initial dates closest to the prediction month led to the best performances based on MBE, RMSE, and ACC, and the performances could be improved by adjusting the number of ensembles considering the combination of the initial date. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions.

• 한국수자원학회논문집
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• 제48권12호
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• pp.995-1009
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• 2015

초단기 홍수예보를 위한 주요자료로서 최근 기상레이더의 중요성이 크게 부각되고 있다. 기상레이더는 넓은 지역에 걸쳐 실시간으로 강우현상 감시가 가능하며 지상우량계로는 파악이 불가능한 미계측유역을 통과하는 강우장의 이동 및 변화 파악이 가능한 장점이 있다. 본 연구는 강우장의 공간적 분포와 레이더 강우세포를 추적하는 강우장 예측 해석방안을 수립하였다. 이를 위해 강우장의 공간적인 이동을 고려하기 위해 강우장의 바람장 이류(advection) 패턴을 추출하여 각 강우세포가 가지는 이동방향 및 속도를 고려한 강우장 추적기법을 통하여 강우장을 예측하였다. 본 연구를 통하여 개발된 기상레이더 강우장 상관분석 기법을 활용한 초단기강우예측 결과는 집중호우시 홍수 예 경보를 위한 수문모형의 입력자료로 활용이 가능할 것으로 사료된다.

• 대기
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• 제26권4호
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• pp.711-716
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• 2016

The dynamical model forecasts using state-of-art general circulation models (GCMs) have some limitations to simulate the real climate system since they do not depend on the past history. One of the alternative methods to correct model errors is to use the canonical correlation analysis (CCA) correction method. CCA forecasts at the present time show better skill than dynamical model forecasts especially over the midlatitudes. Model outputs are adjusted based on the CCA modes between the model forecasts and the observations. This study builds a canonical correlation prediction model for subseasonal (June) precipitation. The predictors are circulation fields over western North Pacific from the Global Seasonal Forecasting System version 5 (GloSea5) and observed snow cover extent over Eurasia continent from Climate Data Record (CDR). The former is based on simultaneous teleconnection between the western North Pacific and the East Asia, and the latter on lagged teleconnection between the Eurasia continent and the East Asia. In addition, we suggest a technique for improving forecast skill by applying the ensemble canonical correlation (ECC) to individual canonical correlation predictions.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2016년도 학술발표회
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• pp.236-236
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• 2016

현재 댐운영 계획 수립 시 매월 유지해야 하는 저수량의 범위를 나타낸 기준수위가 사용되고 있으며 매년 홍수기 말에 현재의 수문 상황과 장래의 전망을 통한 시기별 연간, 월간 댐운영 계획을 수립하고 있다. 물관리의 이수측면에서 댐수위 운영계획 수립과 홍수기 운영목표 수위를 결정하는데 활용하기 위해서는 계절단위, 연단위의 기상정보가 필요하다. 본 연구에서는 기상청에서 운영하고 제공하는 전지구 계절예측시스템 GloSea5(Global Seasonal forecasting system version 5)자료를 활용하여 금강유역에 적용하고자 하였다. GloSea5는 전지구계절예측시스템으로 대기(UM), 지면(JULES), 해양(NEMO), 해빙(CICE)모델이 서로 결합되어 하나의 시스템으로 구성되어 있으며 공간 수평해상도는 N216($0.83^{\circ}{\times}0.56^{\circ}$)으로 중위도에서 약60km이다. Hindcast자료는 유럽중기예보센터(ECMWF)에서 생산된 ERA-Interim 재분석장을 대기 모델의 초기장으로 사용하며 기간은 1996~2009년의 총 14년이다. 예보자료의 검증은 예보의 질을 결정하는 과정으로 Brier Skill Score (BSS), Reliability Diagrams, Relative Operating, Characteristics (ROC)등을 통해 정확성과 오차에 의한 예보의 성능을 검증하였다. 또한 Glosea5의 통계적 상세화를 수행하여 다양한 변수가 갖는 계통적인 지역 오차를 보정함으로써 자료의 신뢰도를 향상시키고자 하였으며 이는 이후 수문모델과의 연계 시 보다 정확하고 효율적인 댐운영에 활용할 수 있는 기후예측정보를 제공할 수 있을 것으로 판단된다.