• 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.

• Ocean and Polar Research
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• v.37 no.2
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• pp.127-140
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• 2015

A continuous series of 60 snow samples was collected at a 2.5-cm interval from a 1.5-m snow pit at a site on the Styx Glacier Plateau in Victoria Land, Antarctica, during the 2011/2012 austral summer season. Various chemical components (${\delta}D$, ${\delta}^{18}O$, $Na^+$, $K^+$, $Mg^{2+}$, $Ca^{2+}$, $Cl^-$, $SO_4{^2-}$, $NO_3{^-}$, $F^-$, $CH_3SO_3{^-}$, $CH_3CO_2{^-}$ and $HCO_2{^-}$) were determined to understand the highly resolved seasonal variations of these species in the coastal atmosphere near the Antarctic Jang Bogo station. Based on vertical profiles of ${\delta}^{18}O$, $NO_3{^-}$and MSA, which showed prominent seasonal changes in concentrations, the snow samples were dated to cover the time period from 2009 austral winter to 2012 austral summer with a mean accumulation rate of $226kgH_2Om^{-2}yr^{-1}$. Our snow profiles show pronounced seasonal variations for all the measured chemical species with a different pattern between different species. The distinctive feature of the occurrence patterns of the seasonal variations is clearly linked to changes in the relative strength of contributions from various natural sources (sea salt spray, volcanoes, crust-derived dust, and marine biogenic activities) during different short-term periods. The results allow us to understand the transport pathways and input mechanisms for each species and provide valuable information that will be useful for investigating long-term (decades to century scale periods) climate and environmental changes that can be deduced from an ice core to be retrieved from the Styx Glacier Plateau in the near future.

• Journal of the Korean association of regional geographers
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• v.21 no.1
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• pp.98-113
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• 2015

In this study, spatial and temporal patterns and changes in seasonal multi-day cumulative extreme precipitation events defined by maximum 1~5 days cumulative extreme precipitation observed at 61 weather stations in the Republic of Korea for the recent 40 years(1973~2012) are examined. It is demonstrated that the magnitude of multi-day cumulative extreme precipitation events is greatest in summer, while their sensitivity relative to the variations of seasonal total precipitation is greatest in fall. According to analyses of linear trends in the time series data, the most noticeable increases in the magnitude of multi-day cumulative extreme precipitation events are observable in summer with coherences amongst 1~5 days cumulative extreme precipitation events. In particular, the regions with significant increases include Gyeonggi province, western Gangwon province and Chungcheong province, and as the period for the accumulation of extreme precipitation increases from 1 day to 5 days, the regions with significantly-increasing trends are extended to the Sobaek mountain ridge. It is notable that at several scattered stations, the increases of 1~2 days cumulative extreme precipitation events are observed even in winter. It is also observed that most distinct increasing tendency of the ratio of these multi-day cumulative extreme precipitation to seasonal total precipitation appears in winter. These results indicate that proactive actions are needed for spatial and temporal changes in not only summer but also other seasonal multi-day cumulative extreme precipitation events in Korea.

• Fisheries and Aquatic Sciences
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• v.1 no.1
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• pp.48-62
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• 1998

Seasonal volume transport on the Texas-Louisiana continental shelf is investigated in terms of objectively fitted transport streamfunction fields based on the current meter data of the Texas­Louisiana Shelf Circulation and Transport Processes Study. Adopted here for the objective mapping is a method employing a two-dimensional truncated Fourier representation of the streamfunction over a domain, with the amplitudes determined by least square fit of the observation. The fitting was done with depth-averaged flow rather than depth-integrated flow to reduce the root-mean-square error. The fitting process filters out $11\%$ of the kinetic energy in the monthly mean transport fields. The shelf-wide pattern of streamfunction fields is similar to that of near-surface velocity fields over the region. The nearshore transport, about 0.1 to 0.3 Sv $(1 Sv= 10^6\;m^3/sec)$, is well correlated with the seasonal signal of along-shelf wind stress. The spring transport is weak compared to other seasons in the inner shelf region. The transport along the shelf break is large and variable. In the southwestern shelf break, transport amounts up to 4.7 Sv, which is associated with the activities of the encroaching of energetic anticyclonic eddies originated in Loop Current of the eastern Gulf of Mexico. The first empirical orthogonal function (EOF) of streamfunction variability contains $67.3\%$ of the variance and shows a simple, shelf-wide, along-shelf pattern of transport. The amplitude evolution of the first EOF is highly correlated (correlation coefficient: 0.88) with the evolution of the along-shelf wind stress. This provides strong evidence that the large portion of seasonal variation of the shelf transport is wind-forced. The second EOF contains $23.7\%$ of the variance and shows eddy activities at the southwestern shelf break. The correlation coefficient between the amplitudes of the second EOF and wind stress is 0.42. We assume that this mode is coupled a periodic inner shelf process with a non-periodic eddy process on the shelf break. The third EOF (accounting for $7.2\% of the variance) shows several cell structures near the shelf break associated with the variability of the Loop Current Eddies. The amplitude time series of the third EOF show little correlation with the along-shelf wind.

• Journal of Environmental Science International
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• v.25 no.3
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• pp.361-371
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• 2016

Ministry of Environment has been operating water quality monitoring network in order to obtain the basic data for the water environment policies and comprehensively understand the water quality status of public water bodies such as rivers and lakes. The observed water quality data is very important to analyze by applying statistical methods because there are seasonal fluctuations. Typically, monthly water quality data has to analyze that the transition comprise a periodicity since the change has the periodicity according to the change of seasons. In this study, trends, SOM and RDA analysis were performed at the Mulgeum station using water quality data for temperature, BOD, COD, pH, SS, T-N, T-P, Chl-a and Colon-bacterium observed from 1989 to 2013 in the Nakdong River. As a result of trends, SOM and RDA, the Mulgeum station was found that the water quality is improved, but caution is required in order to ensure safe water supply because concentrations in water quality were higher in the early spring(1~3 month) the most.

• Health Policy and Management
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• v.27 no.3
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• pp.241-246
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• 2017

Background: The purpose of this study is to investigate the trends on the induced abortion in Korea using social big-data and confirm whether there was time series trends and seasonal characteristics in induced abortion. Methods: From October 1, 2007 to October 24, 2016, we used Naver's data lab query, and the search word was 'induced abortion' in Korean. The average trend of each year was analyzed and the seasonality was analyzed using the cosinor model. Results: There was no significant changes in search volume of abortion during that period. Monthly search volume was the highest in May followed by the order of June and April. On the other hand, the lowest month was December followed by the order of January, and September. The cosinor analysis showed statistically significant seasonal variations (amplitude, 4.46; confidence interval, 1.46-7.47; p< 0.0036). The search volume for induced abortion gradually increased to the lowest point at the end of November and was the highest at the end of May and declined again from June. Conclusion: There has been no significant changes in induced abortion for the past nine years, and seasonal changes in induced abortion have been identified. Therefore, considering the seasonality of the intervention program for the prevention of induced abortion, it will be effective to concentrate on the induced abortion from March to May.

• Atmosphere
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• v.31 no.2
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• pp.215-227
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• 2021

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.253-271
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• 2005

We have analyzed surface water temperature and seasonal stratification of lakes in the Han river system using time-series Landsat images and in situ measurement data. Using NASA equation, at-satellite temperature is derived from 29 Landsat-5 TM and Landsat-7 ETM+ images obtained from 1994 to 2004, and was compared with in situ surface temperature on river-type dam lakes such as Paro, Chuncheon, Euiam, Chongpyong, Paldang, and with 10m-depth temperature on lake-type dam lake Soyang. Although the in situ temperature at the time of satellite data acquisition was interpolated from monthly measurements, the number of images with standard deviation of temperature difference (at-satellite temperature - in situ interpolated temperature) less than $2^{\circ}C$ was 24 on which a novel statistical atmospheric correction could be applied. The correlation coefficient at Lake Soyang was 0.915 (0.950 after correction) and 0.951-0.980 (0.979-0.997 after correction) at other lakes. This high correlation implies that there exist a mixed layer in the shallow river-like dam lakes due to physical mixing from continuous influx and efflux, and the daily and hourly temperature change is not fluctuating. At Lake Soyang, an anomalous temperature difference was observed from April to July where at-satellite temperature is $3-5^{\circ}C$ higher than in situ interpolated temperature. Located in the uppermost part of the Han river system and its influx is governed only by natural precipitation, Lake Soyang develops stratification during this time with rising sun elevation and no physical mixture from influx in this relatively dry season of the year.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Water for future
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• v.27 no.3
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• pp.45-54
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• 1994

Basic theory of fractal dimension is introduced and performed for the generated time series using the water balance model. The water balance equation over a large area is analyzed at seasonal time scales. In the generation and modification of mesoscale circulation local recycling of precipitation and dynamic effects of soil moisture are explicitly included. Time delay is incorporated in the analysis. Depending on the parameter values, the system showed different senarios in the evolution such as fixed point, limit cycle, and chaotic types of behavior. The stochastic behavior of the generated time series is due to deterministic chaos which arises from a nonlinear dynamic system with a limited number of equations whose trajectories are highly sensitive to initial conditions. The presence of noise arose from the characterization of the incoming precipitation, destroys the organized structure of the attractor. The existence of the attractor although noise is present is very important to the short-term prediction of the evolution. The implications of this nonlinear dynamics are important for the interpretation and modeling of hydrologic records and phenomena.