• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.442-442
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• 2022

고해상도 시공간적 격자 형태의 레이더 강수는 돌발홍수(flash flood)와 같은 기상재해에 대비하기 위하여 실시간 예측정보로 활용된다. 그러나 대부분의 레이더 강수는 과소 추정되는 경향이 있어 정량적인 보정 과정인 QPE (Quantitative Precipitation Estimation)가 필요하다. 일반적으로 레이더 강수자료 보정은 지점 관측자료를 활용하지만, 본 연구에서는 지상 강수량 기반의 고해상도 격자 강수자료를 생산하여 레이더 강수자료와 직접적으로 비교하고자 한다. 이에 고도와 지형적 특성을 고려한 PRISM(Precipitation-elevation Regressions on Independent Slopes Model) 방법을 사용하여 고해상도 격자기반의 자료를 생성하였다. PRISM 방법은 고도와 지리정보를 독립변수로 갖는 회귀모형 기반의 기후인자 추정 모형이다. 생산된 고해상도 격자 강수자료와 레이더 강수자료를 QPF (Quantitative Precipitation Forecast) 모델의 입력자료로 사용하여 예측결과를 비교하였다. 해당 QPF 모델은 이류(advection)와 확률론적 섭동(stochastic perturbation)을 기반으로 하며, 강수 앙상블 자료를 생산한다. QPF 모델에 대해 투 트랙(two-track) 방법으로 생산된 예측정보를 통해 레이더 강수자료의 격자별 후처리 보정이 가능할 것으로 판단된다.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.351-362
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• 2020

Numerical simulations of the storm surge and waves induced by the Typhoon Maemi incident on the south sea of Korea in 2003 are performed using the JMA-MSM forecast weather field, NCEP-CFSR reanalysis weather field, ECMWF-ERA5 reanalysis weather field, and the pressure and wind fields obtained using the best track information provided by JTWC. The calculated surge heights are compared with the time history observed at harbours along the coasts of Korea. For the waves occurring coincidentally with the storm surges the calculated significant wave heights are compared with the measured data. Based on the comparison of surge and wave heights the assessment of the reliability of various weather fields is performed. As a result the JMA-MSM weather fields gives the highest reliability, and the weather field obtained using JTWC best track information gives also relatively good agreement. The ECMWF-ERA5 gives in general surge and wave heights weaker than the measured. The reliability of NCEP-CFSR turns out to be the worst for this special case of Typhoon Maemi. Based on the results of this study it is found that the reliable weather fields are essential for the accurate simulation of storm surges and waves.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.327-331
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• 2007

23 tropical cyclones of tropical storm(TS) intensity or higher formed in the western North Pacific and the South China Sea in 2006. The total number is less than the 30-year $(1971{\sim}2000)$ average frequency of 26.7, Out of 23, 15 cyclones reached typhoon(TY) intensity, three severe tropical storm(STS) intensity, and five TS intensity. The tropical cyclone season in 2006 began in May with the formation of CHANCHU(0601). While convective activity was slightly inactive around the Philippines from late June to early August. In addition, subtropical high was more enhanced than normal over the south of Japan from May to early August. Consequently, most tropical cyclones formed over the sea east of the Philippines after late June, and many of them moved westwards to China. CHANCHU(0601), BILIS(0604), KAEMI(0605), PRAPIROON(0606) and SAOMI(0608) brought damage to China, the Philippines, and Vietnam. On the other hand, EWINIAR(0603) moved northwards and hit the Republic of Korea, causing damage to the country From late August to early September, convective activity was temporarily inactive over the sea east of the Philippines. However, it turned active again after late September. Subtropical high was weak over the south of Japan after late August. Therefore, most tropical cyclones formed over the sea east of the Philippines and moved northwards. WUKONG(0610) and SHANSHAN(0613) hit Japan to bring damage to the country. On the other hand, XANGSANE(0615) and CIMARON(0619) moved westwards in the South China Sea, causing damage to the Philippines, Thailand, and Vietnam. In addition, IOKE(0612) was the first namded cyclone formed in the central North Pacific and moved westwards across longitude 180 degrees east after HUKO(0224).

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.995-1009
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• 2015

Weather radar rainfall data has been recognized for making valuable contributions to short-term flood forecasting and management over the past decades. There are several advantages to better monitoring rainfall in ungauged area compared to ground-based rain gauges with which spatial patterns of the rainfall are not effectively identified. Hence, this study aims to develop a new scheme to forecast spatio-temporal rainfall field. The proposed model was based on an advection scheme to track wind patterns and velocity. The results showd a promising forecasting skill with quantitative and qualitative measures. It was confirmed that the forecasted rainfall may be effectively used an input data for a distributed hydrological model.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.21-28
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• 2010

Air pollution monitoring has attracted a lot of interests because it affects directly to the human life quality. The most of the current air pollution monitoring stations use the expensive and bulky instruments and are only installed in the specific area. Therefore, it is difficult to install them to as many places as people need. In this work, we constructed a low price and small size Radio Frequency(RF) sensor system to solve this problem. This system also had the measurement range similar to the ones used in the air pollution forecast systems. This system had the sensor unit to measure the air quality, the central processing unit for air quality data acquisition, the power unit to supply the power to every units, and the RF unit for the wireless transmission and reception of the data. This system was easy to install in the field. We also added a GPS unit to track the position of the RF-sensor in real time by wireless communication. For the various measurements of the air pollution, we used CO, $O_3$, $NO_2$ sensors as gas sensors and also installed a dust sensor.

• Journal of the Korean earth science society
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• v.38 no.2
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• pp.105-114
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• 2017

Two dimensional finite element method with quadrilateral basis functions was applied to the spherical high order filter on the spherical surface limited area domain. The basis function consists of four shape functions which are defined on separate four grid boxes sharing the same gridpoint. With the basis functions, the first order derivative was expressed as an algebraic equation associated with nine point stencil. As the theory depicts, the convergence rate of the error for the spherical Laplacian operator was found to be fourth order, while it was the second order for the spherical Laplacian operator. The accuracy of the new high order filter was shown to be almost the same as those of Fourier finite element high order filter. The two-dimension finite element high order filter was incorporated in the weather research and forecasting (WRF) model as a hyper viscosity. The effect of the high order filter was compared with the built-in viscosity scheme of the WRF model. It was revealed that the high order filter performed better than the built in viscosity scheme did in providing a sharper cutoff of small scale disturbances without affecting the large scale field. Simulation of the tropical cyclone track and intensity with the high order filter showed a forecast performance comparable to the built in viscosity scheme. However, the predicted amount and spatial distribution of the rainfall for the simulation with the high order filter was closer to the observed values than the case of built in viscosity scheme.

• Atmosphere
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• v.31 no.3
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• pp.311-326
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• 2021

To improve the predictability of high-impact weather phenomena around Seoul, where a larger number of people are densely populated, KMA conducted the intensive observation from 22 June to 20 September in 2020 over the Seoul area. During the intensive observation period (IOP), the dropsonde from NIMS Atmospheric Research Aircraft (NARA) and the radiosonde from KMA research vessel Gisang1 were observed in the Yellow Sea, while, in the land, the radiosonde observation data were collected from Icheon and Incheon. Therefore, in this study, the effects of radiosonde and dropsonde data during the IOP were investigated by Observing System Experiment (OSE) based on Korean Integrated Model (KIM). We conducted two experiments: CTL assimilated the operational fifteen kinds of observations, and EXP assimilated not only operational observation data but also intensive observation data. Verifications over the Korean Peninsula area of two experiments were performed against analysis and observation data. The results showed that the predictability of short-range forecast (1~2 day) was improved for geopotential height at middle level and temperature at lower level. In three precipitation cases, EXP improved the distribution of precipitation against CTL. In typhoon cases, the predictability of EXP for typhoon track was better than CTL, although both experiments simulated weaker intensity as compared with the observed data.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.