• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.11
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• pp.557-560
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• 2005

This paper proposes the demand-side management(DSM) monitoring system of high efficient end-use equipments considering demand-side bidding (DSB). The effect on the market penetration of high efficient equipments by demand side bidding is analysed. Seasonal peak demand forecasting and penetration capacity of these equipments . are analysed.

• Atmosphere
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• v.30 no.3
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• pp.209-220
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• 2020

Seasonal predictability and variability of tropical storms (TCs) simulated in the Global Seasonal Forecast System version 5 (GloSea5) of the Korea Meteorological Administration (KMA) is assessed in Northern Hemisphere in 1996~2009. In the KMA, the GloSea5-Global Atmosphere version 3.0 (GloSea5-GA3) that was previously operated was switched to the GloSea5-Global Coupled version 2.0 (GloSea5-GC2) with data assimilation system since May 2016. In this study, frequency, track, duration, and strength of the TCs in the North Indian Ocean, Western Pacific, Eastern Pacific, and North Atlantic regions derived from the GloSea5-GC2 and GloSea5-GA3 are examined against the best track data during the research period. In general, the GloSea5 shows a good skill for the prediction of seasonally averaged number of the TCs in the Eastern and Western Pacific regions, but underestimation of those in the North Atlantic region. Both the GloSea5-GA3 and GC2 are not able to predict the recurvature of the TCs in the North Western Pacific Ocean (NWPO), which implies that there is no skill for the prediction of landfalls in the Korean peninsula. The GloSea5-GC2 has higher skills for predictability and variability of the TCs than the GloSea5-GA3, although continuous improvements in the operational system for seasonal forecast are still necessary to simulate TCs more realistically in the future.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• International Journal of Industrial Entomology and Biomaterials
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• v.8 no.1
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• pp.17-26
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• 2004

Pest monitoring through field surveys and surveillance helps in forecasting the population build up of pest. It reduces the load of pesticides application and forms the basis of Integrated Pest Management in sericulture. Common sampling techniques for quantifying pest populations and damage caused by them are reviewed emphasizing the need for quick and simple sampling methods. Various direct and indirect sampling methods for establishing pest populations are discussed and methods have been discussed to use indirect sampling method under IPM programme in sericulture. The use of pheromone lures and traps forms one of the important ingredients of integrated pest management, which calls for integration of all available methods in a cost effective and environmental friendly manner offering consistent efficacy. Silk-worms feed on the variety of silk host plants and spin cocoons. Each silk host plant is attacked in the field by number of insect pest species. Several pests are common to mulberry, tasar, oak tasar, muga and eri host plant but pest status and seasonal abundance differs from each crop. The key pests are serious perennially occurring persistent species which cause considerable yield loss every year on large areas and require control measure. Regular occurrence of minor pest is noticed but sudden increase in its population is not known. The occasional pests are sporadic but potential causing sufficient damage. Silk losses due to attack of all the pests have not been calculated. However, information on pest biology and ecology, and control practices being practiced is available but the period of outbreak of major pests and predators on silkworms and its host plant needs to be reinvestigated. Pest and predators forecasting based on surveillance information may provide an opportunity to minimize the losses, particularly to reduce expenditure involved in pest management.

• Ocean and Polar Research
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• v.40 no.3
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• pp.99-114
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• 2018

In the present study, we assess the GloSea5 (Global Seasonal Forecasting System version 5) near-surface ocean current forecasts using globally observed surface drifter dataset. Annual mean surface current fields at 0-day forecast lead time are quite consistent with drifter-derived velocity fields, and low values of root mean square (RMS) errors distributes in global oceans, except for regions of high variability, such as the Antarctic Circumpolar Current, Kuroshio, and Gulf Stream. Moreover a comparison with the global high-resolution forecasting system, HYCOM (Hybrid Coordinate Ocean Model), signifies that GloSea5 performs well in terms of short-range surface-current forecasts. Predictions from 0-day to 4-week lead time are also validated for the global ocean and regions covering the main ocean basins. In general, the Indian Ocean and tropical regions yield relatively high RMS errors against all forecast lead times, whilst the Pacific and Atlantic Oceans show low values. RMS errors against forecast lead time ranging from 0-day to 4-week reveal the largest increase rate between 0-day and 1-week lead time in all regions. Correlation against forecast lead time also reveals similar results. In addition, a strong westward bias of about $0.2m\;s^{-1}$ is found along the Equator in the western Pacific on the initial forecast day, and it extends toward the Equator of the eastern Pacific as the lead time increases.

• 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.

• Journal of Korea Port Economic Association
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• v.25 no.1
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• pp.1-14
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• 2009

This study predicted Port trade volume by considering Korea's export to China and import Com China separately using ARIMA model (Multiplicative Seasonal ARIMA Model). We predicted monthly Port trade volumes for 27 months from October 2008 to December 2010 using monthly data from September 2008 to January 2001 using monthly data. As a result of prediction, we found that the export volume decreased in January, February, August and September while the import volume decreased in February, March, August and September. As the decrease period was clearly differentiated, it was possible to predict export and import volumes. Therefore, it is believed that the results of this study will generate useful basic data for policy makers or those working for export and import enterprises when they set up policies and management plans. And to improve competitive power of Port trade, this study suggests privatization of Port, improvement of information capability, improvement of competitive power of Port management companies, support for Port distribution companies, plans for active encouragement of transshipment, and management of added value creation policy.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.241-241
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• 2017

기후변화로 인하여 강우의 불확실성이 가중되고 홍수, 가뭄 등 물 관련 재해의 발생빈도 및 강도가 증가함에 따라 안정적인 용수공급 등 수자원 관리 및 운영에 어려움을 겪고 있어 예측기반의 수자원 계획 및 운영이 요구되고 있는 실정이다. 우리나라 기상청에서는 2010년 6월 영국기상청과 장기 계절예측시스템의 구축 및 운영에 관한 협정을 체결하였으며 2014년부터 전지구 계절예측시스템 GloSea5(Global seasonal forecasting system version 5)을 현업에 활용하고 있다. GloSea5 모델은 대기(UM), 지면(JULES), 해양(NEMO), 해빙(CICE) 모델이 커플러(OASIS)에 의해 결합된 통합 시스템으로 일단위 자료로 제공된다. 현재 수자원 분야에서는 장기예보자료가 제공되고 있음에도 불구하고 장기예보자료의 불확실성 및 수문 모형 입력자료로의 활용 어려움, 예측자료의 검증 미흡 등으로 기상청에서 제공하는 장기예보를 참고할 뿐 실제로는 과거 관측자료를 기반한 빈도해석 결과를 활용하여 댐 운영 계획을 수립하고 있는 실정이다. 따라서, 본 연구에서는 GloSea5모델에서 제공되는 일 단위 예측 강수량을 수자원 장기이수계획 및 관리에 활용하고자 GloSea5모델의 예측력을 평가하고 수치모델이 가지는 시스템 에러에 대하여 편의보정 및 지점 상세화를 수행하였다. 본 연구의 분석결과는 향후, 저수지 운영계획 및 증가하는 물수요와 불확실한 공급에 대한 의사결정 지원, 가뭄 대비를 위한 물 공급 제한 등에 활용 가능할 것으로 판단된다.

• Atmosphere
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• v.32 no.4
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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.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.2
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• pp.306-320
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• 2018

Emission inventory is the essential component for improving the performance of air quality forecasting system. This study evaluated the simulated daily mean $PM_{2.5}$ concentrations in South Korea and China for 1-year period (Sept. 2016~Aug. 2017) using air quality forecasting system which was applied by the emission inventory of E2015 (predicted CAPSS 2015 for South Korea and KORUS 2015 v1 for the other regions). To identify the impacts of emissions on the simulated $PM_{2.5}$, the emission inventory replaced by E2010 (CAPSS 2010 and MIX 2010) were also applied under the same forecasting conditions. These results showed that simulated daily mean $PM_{2.5}$ concentrations had generally suitable performance with both emission data-sets for China (IOA>0.87, R>0.87) and South Korea (IOA>0.84, R>0.76). The impacts of the changes in emission inventories on simulated daily mean $PM_{2.5}$ concentrations were quantitatively estimated. In China, normalized mean bias (NMB) showed 5.5% and 26.8% under E2010 and E2015, respectively. The tendency of overestimated concentrations was larger in North Central and Southeast China than other regions under both E2010 and E2015. Seasonal differences of NMB were higher in non-winter season (28.3% (E2010)~39.3% (E2015)) than winter season (-0.5% (E2010)~8.0% (E2015)). In South Korea, NMB showed -5.4% and 2.8% for all days, but -15.2% and -11.2% for days below $40{\mu}g/m^3$ to minimize the impacts of long-range transport under E2010 and E2015, respectively. For all days, simulated $PM_{2.5}$ concentrations were overestimated in Seoul, Incheon, Southern part of Gyeonggi and Daejeon, and underestimated in other regions such as Jeonbuk, Ulsan, Busan and Gyeongnam, regardless of what emission inventories were applied. Our results suggest that the updated emission inventory, which reflects current status of emission amounts and spatio-temporal allocations, is needed for improving the performance of air quality forecasting.