• International conference on construction engineering and project management
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• 2005.10a
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• pp.1124-1128
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• 2005

The duration of a construction project is not only a key element for taking a new order, but also a strict yardstick to determine certain project successful or not. However, since construction project is basically outdoor job and most of the activities are proceeded out-air, no matter how the schedule plan has been established accurately, actual project proceeds due to the weather condition, beyond anyone's control. In this paper, the functional relationship between work productivity rate and weather elements is suggested by regression analysis. Difference of the relationship and influence of weather due to the seasonal group are also revealed. With these results, by simulating actual weather data and generating weather forecast through historical data, more accurate schedule would be obtained.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.143-154
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• 2024

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.29 no.2
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• pp.143-149
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• 1996

Forecasts of the monthly catches of anchovy in Korea were carried out by the seasonal Autoregressive Integrated Moving Average (ARIMA) model and spectral analysis. The seasonal ARIMA model is as follows: $$(1-0.431B)(1-B^{12})Z_t=(1-0.882B^{12})e_t$$ where: $Z_t=value$ at month $t;\;B^{p}$ is a backward shift operator, that is, $B^pZ_t=Z_{t-p};$ and $e_t=error$ term at month t, which is to forecast 24 months ahead the anchovy catches in Korea. The prediction error by the Box-Cox transformation on monthly anchovy catches in Korea was less than that by the logarithmic transformation. The equation of the Box-Cox transformation was $Y'=(Y^{0.58}-1)/0.58$. Forecasts of the monthly anchovy catches for $1991\~1992$, which were compared with the actual catches, had an absolute percentage error (APE) range of $1.0\~63.2\%$. Total observed annual catches in 1991 and 1992 were 170,293 M/T and 168,234 M/T respectively, while the predicted catches were 148,201 M/T and 148,834 M/T $(API\;13.0\%\;and\;11.5\%,\;respectively)$. The spectrum analysis of the monthly catches of anchovy showed some dominant fluctuations in the periods of 2.2, 6.1, 10.2 12.0 and 14.7 months. The spectrum analysis was also useful for selecting the ARIMA model.

• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.2
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• pp.122-130
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• 2014

Seasonal variations and long term linear trends of SST (Sea Surface Temperature) at Yeosu Coast ($127^{\circ}37.73^{\prime}E$, $34^{\circ}37.60^{\prime}N$) in Korea were studied performing the harmonic analysis and the regression analysis of the monthly mean SST data of 46 years (1965-2010) collected by the Fisheries Research and Development Institute in Korea. The mean SST and the amplitude of annual SST variation show $15.6^{\circ}C$ and $9.0^{\circ}C$ respectively. The phase of annual SST variation is $236^{\circ}$. The maximum SST at Yeosu Coast occurs around August 26. Climatic changes in annual mean SST have had significant increasing tendency with increase rate $0.0305^{\circ}C/Year$. The warming trend in recent 30 years (1981-2010) is more pronounced than that in the last 30 years (1966-1995) and the increasing tendency of winter SST dominates that of the annual SST. The time series model that could be used to forecast the SST on a monthly basis was developed applying Box-Jenkins methodology. $ARIMA(1,0,0)(2,1,0)_{12}$ was suggested for forecasting the monthly mean SST at Yeosu Coast in Korea. Mean absolute percentage error to measure the accuracy of forecasted values was 8.3%.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.42-42
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• 2023

최근 IPCC 6차 보고서에서는 전 지구의 온도가 0.5℃가 증가할 때마다 기상학적 가뭄 지역이 증가하며, 인위적 강제력은 가뭄 현상의 강도와 빈도를 증가하는 것으로 밝혔다. 봄철(3월-5월) 동남아시아(남중국, 필리핀 등)에 비해 상대적으로 건조한 동북아시아(동중국, 한반도, 일본) 지역은 가뭄에 취약하며 기후 변화에 따라 가뭄으로 인한 피해가 커질 것으로 전망된다. 그러므로 이 지역은 봄철 가뭄으로 인한 피해를 완화하기 위해 봄철 강수량에 대한 신뢰할 만한 계절적 예보 기술이 꼭 필요하다. 본 연구에서는 1992-2022년 봄철의 Standardized Precipitation Index(SPI) 값을 기준으로 2001년과 2011년 동북아시아 가뭄이 발생한 것을 확인하였으며, 각 해의 3월에 관측된 기상학적 초기 조건으로부터 다중 기후 예보 모델들의 봄철 강수량의 계절적 예측성을 정량적으로 평가하였다. 관측자료로부터 2001년 가뭄은 동북아시아 대기 상층의 저기압성 순환의 강화로 인한 제트류(Jet stream)의 강화와 연관되어 있었으며, 2011년 가뭄은 제트류 강화와 함께 태평양 열대 지역 기류 강화가 동반되어 발생하였음을 알 수 있었다. North American Multi-Model Ensemble 기후 예보 모델들은 2011년 가뭄에 비해 2001년 가뭄에 대한 예측성이 높았으며, 그 이유로는 대기 상층 순환의 예측성과 연관이 있음을 밝혔다. 또한, 봄철 대기-해양 상호 패턴을 관측과 유사하게 재현한 GFDL-SPEARS 모델이 가뭄 해의 대기 상층 저기압성 순환과 강수 예측성이 가장 높은 것을 보였다. 본 연구의 결과들을 통해 동북아시아 봄철 가뭄과 같은 극한 기상의 강수량 예측성 향상에 있어서 기후 예보 모델들의 현실적인 대기-해양 결합 과정 모사 능력의 중요성을 밝혔다. 본 연구에서 제안된 방안들은 기후 예측 모델 개선을 위한 전략적인 정보를 제공할 것으로 보인다.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.401-409
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• 2014

This study deals with capacity expansion planning of airport infrastructure in view of economic validation that reflect construction costs and social benefits according to the reduction of passengers' delay time. We first forecast the airport peak-demand which has a seasonal and cyclical feature with ARIMA model that has been one of the most widely used linear models in time series forecasting. A discrete event simulation model is built for estimating actual delay time of passengers that consider the passenger's dynamic flow within airport infrastructure after arriving at the airport. With the trade-off relationship between cost and benefit, we determine an economic quantity of conveyor that will be expanded. Through the experiment performed with the case study of Incheon international airport, we demonstrate that our approach can be an effective method to solve the airport expansion problem with seasonal passenger arrival and dynamic operational aspects in airport infrastructure.

• Atmosphere
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• v.19 no.3
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• pp.269-287
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• 2009

CAT (clear-air turbulence) forecasting algorithm, the Graphical Turbulence Guidance (GTG) system developed at NCAR (national center for atmospheric research), is evaluated with available observations (e.g., pilot reports; PIREPs) reported in South Korea during the recent 5 years (2003-2008, excluding 2005). The GTG system includes several steps. First, 44 CAT indices are calculated in the domain of the Regional Data Assimilation and Prediction System (RDAPS) analysis data with 30 km horizontal grid spacing provided by KMA (Korean Meteorological Administration). Second, 10 indices that performed ten best forecasting scores are selected. Finally, 10 indices are combined by measuring the score based on the probability of detection, which is calculated using PIREPs exclusively of moderate-or-greater intensity. In order to investigate the best performance of the GTG system in Korea, various statistical examinations and sensitivity tests of the GTG system are performed by yearly and seasonally classified PIREPs. Performances of the GTG system based on yearly distributed PIREPs have annual variations because the compositions of indices are different from each year. Seasonal forecasting is generally better than yearly forecasting, because selected CAT indices in each season represent meteorological condition much more properly than applying the selected CAT indices to all seasons. Wintertime forecasting is the best among the four seasonal forecastings. This is likely due to that the GTG system consists of many CAT indices related to the jet stream, and turbulence associated with the jet stream can be activated mostly in wintertime under strong jet magnitude. On the other hand, summertime forecasting skill is much less than other seasons. Compared with current operational CAT prediction system (KITFA; Korean Integrated Turbulence Forecasting System), overall performance of the GTG system is better when CAT indices are selected seasonally.

• Atmosphere
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• v.28 no.1
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• pp.85-97
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• 2018

This study evaluates the performance of the Weather Research and Forecasting (WRF) model in reproducing the present-day (1981~2005) precipitation over Far East Asia and South Korea. The WRF model is configured with 25-km horizontal resolution within the context of the COordinated Regional climate Downscaling Experiment (CORDEX) - East Asia Phase 2. The initial and lateral boundary forcing for the WRF simulation are derived from European Centre for Medium-Range Weather Forecast Interim reanalysis. According to our results, WRF model shows a reasonable performance to reproduce the features of precipitation, such as seasonal climatology, annual and inter-annual variabilities, seasonal march of monsoon rainfall and extreme precipitation. In spite of such model's ability to simulate major features of precipitation, systematic biases are found in the downscaled simulation in some sub-regions and seasons. In particular, the WRF model systematically tends to overestimate (underestimate) precipitation over Far East Asia (South Korea), and relatively large biases are evident during the summer season. In terms of inter-annual variability, WRF shows an overall smaller (larger) standard deviation in the Far East Asia (South Korea) compared to observation. In addition, WRF overestimates the frequency and amount of weak precipitation, but underestimates those of heavy precipitation. Also, the number of wet days, the precipitation intensity above the 95 percentile, and consecutive wet days (consecutive dry days) are overestimated (underestimated) over eastern (western) part of South Korea. The results of this study can be used as reference data when providing information about projections of fine-scale climate change over East Asia.

• Atmosphere
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• v.27 no.1
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• pp.105-118
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• 2017

This study assesses the performance of the Weather Research and Forecasting (WRF) model in reproducing regional climate over CORDEX-East Asia Phase 2 domain with different cumulus parameterization schemes [Kain-Fritch (KF), Betts-Miller-Janjic (BM), and Grell-Devenyi-Ensemble (GD)]. The model is integrated for 27 months from January 1979 to March 1981 and the initial and boundary conditions are derived from European Centre for Medium-Range Weather Forecast Interim Reanalysis (ERA-Interim). The WRF model reasonably reproduces the temperature and precipitation characteristics over East Asia, but the regional scale responses are very sensitive to cumulus parameterization schemes. In terms of mean bias, WRF model with BM scheme shows the best performance in terms of summer/winter mean precipitation as well as summer mean temperature throughout the North East Asia. In contrast, the seasonal mean precipitation is generally overestimated (underestimated) by KF (GD) scheme. In addition, the seasonal variation of the temperature and precipitation is well simulated by WRF model, but with an overestimation in summer precipitation derived from KF experiment and with an underestimation in wet season precipitation from BM and GD schemes. Also, the frequency distribution of daily precipitation derived from KF and BM experiments (GD experiment) is well reproduced, except for the overestimation (underestimation) in the intensity range above (less) then $2.5mm\;d^{-1}$. In the case of the amount of daily precipitation, all experiments tend to underestimate (overestimate) the amount of daily precipitation in the low-intensity range < $4mm\;d^{-1}$ (high-intensity range > $12mm\;d^{-1}$). This type of error is largest in the KF experiment.

• Atmosphere
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• v.30 no.3
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• pp.293-309
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• 2020

There are lots of indices that define the intensity of East Asian summer monsoon (EASM) in climate systems. This paper assesses the prediction skill for EASM indices in a Global Seasonal Forecasting System (GloSea5) that is currently operating at KMA. Total 5 different types of EASM indices (WNPMI, EAMI, WYI, GUOI, and SAHI) are selected to investigate how well GloSea5 reproduces them using hindcasts with 12 ensemble members with 1~3 lead months. Each index from GloSea5 is compared to that from ERA-Interim. Hindcast results for the period 1991~2010 show the highest prediction skill for WNPMI which is defined as the difference between the zonal winds at 850 hPa over East China Sea and South China Sea. WYI, defined as the difference between the zonal winds of upper and lower level over the Indian Ocean far from East Asia, is comparatively well captured by GloSea5. Though the prediction skill for EAMI which is defined by using meridional winds over areas of East Asia and Korea directly affected by EASM is comparatively low, it seems that EAMI is useful for predicting the variability of precipitation by EASM over East Asia. The regressed atmospheric fields with EASM index and the correlation with precipitation also show that GloSea5 best predicts the synoptic environment of East Asia for WNPMI among 5 EASM indices. Note that the result in this study is limited to interpret only for GloSea5 since the prediction skill for EASM index depends greatly on climate forecast model systems.