• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.90-96
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• 2018

This paper proposed data driven techniques to forecast the time point of water management of the water reservoir without measuring manganese concentration with the empirical data as Juam Dam of years of 2015 and 2016. When the manganese concentration near the surface of water goes over the criteria of 0.3mg/l, the water management should be taken. But, it is economically inefficient to measure manganese concentration frequently and regularly. The water turnover by the difference of water temperature make manganese on the floor of water reservoir rise up to surface and increase the manganese concentration near the surface. Manganese concentration and water temperature from the surface to depth of 20m by 5m have been time plotted and exploratory analyzed to show that the water turnover could be used instead of measuring manganese concentration to know the time point of water management. Two models for forecasting the time point of water turnover were proposed and compared as follow: The regression model of CR20, the consistency ratio of water temperature, between the surface and the depth of 20m on the lagged variables of CR20 and the first lag variable of max temperature. And, the Box-Jenkins model of CR20 as ARIMA (2, 1, 2).

• Journal of Environmental Science International
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• v.28 no.9
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• pp.765-784
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• 2019

The synoptic structural characteristics associated with heavy snowfall (Bukgangneung: 31.3 cm) that occurred in the Yeongdong area on 20 January 2017 was investigated using surface and upper-level weather charts, European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data, radiosonde data, and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product. The cold dome and warm trough of approximately 500 hPa appeared with tropopause folding. As a result, cold and dry air penetrated into the middle and upper levels. At this time, the enhanced cyclonic potential vorticity caused strong baroclinicity, resulting in the sudden development of low pressure at the surface. Under the synoptic structure, localized heavy snowfall occurred in the Yeongdong area within a short time. These results can be confirmed from the vertical analysis of radiosonde data and the characteristics of the MODIS cloud product.

• Atmosphere
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• v.31 no.1
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• pp.73-83
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• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• Atmosphere
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• v.16 no.3
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• pp.247-257
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• 2006

The Yeong-dong heavy snowfall forecast supporting system has been developed during the last several years. In order to construct the conceptual model, we have examined the characteristics of heavy snowfalls in the Yeong-dong region classified into three precipitation patterns. This system is divided into two parts: forecast and observation. The main purpose of the forecast part is to produce value-added data and to display the geography based features reprocessing the numerical model results associated with a heavy snowfall. The forecast part consists of four submenus: synoptic fields, regional fields, precipitation and snowfall, and verification. Each offers guidance tips and data related with the prediction of heavy snowfalls, which helps weather forecasters understand better their meteorological conditions. The observation portion shows data of wind profiler and snow monitoring for application to nowcasting. The heavy snowfall forecast supporting system was applied and tested to the heavy snowfall event on 28 February 2006. In the beginning stage, this event showed the characteristics of warm precipitation pattern in the wind and surface pressure fields. However, we expected later on the weak warm precipitation pattern because the center of low pressure passing through the Straits of Korea was becoming weak. It was appeared that Gangwon Short Range Prediction System simulated a small amount of precipitation in the Yeong-dong region and this result generally agrees with the observations.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.6
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• pp.851-862
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• 2006

Satellites have been valuable tool for global/regional scale atmospheric environment monitoring as well as emission source detection. In this study, we present the results of application of satellite remote sensing data for air quality forecast in Seoul metropolitan area. AOT (Aerosol Optical Thickness) data from TERRA/MODIS (Moderate Resolution Imaging Spectre-radiometer) satellite were compared to ground based $PM_{10}$ mass concentrations, and used to estimate the possibility of the aerosol forecasting in Seoul metropolitan area. Although correlation coefficient (${\sim}0.37$) between MODIS AOT products and surface $PM_{10}$ concentration data was relatively low, there was good correlation between MODIS AOT and surface PM concentration under certain atmospheric conditions, which supports the feasibility of using the high-resolution MODIS AOT for air quality forecasting. The MODIS AOT data with trajectory forecasts also can provide information on aerosol concentration trend. The success rate of the 24 hour aerosol concentration trend forecast result was about 75% in this study. Finally, application of satellite remote sensing data with ground-based air quality observations could provide promising results for air quality monitoring and more exact trend forecast methodology by high resolution satellite data and verification with long term measurement dataset.

• Ocean and Polar Research
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• v.43 no.2
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• pp.53-63
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• 2021

Changes in a marine environment have a broad socioeconomic implication on fisheries and their relevant industries so that there has been a growing demand for the medium-range (months to years) prediction of the marine environment Using a medium-range ocean prediction model (Ocean Mid-range prediction System, OMIDAS) for the northwest Pacific, this study attempted to assess seasonal difference in the mid-range predictability of the sea surface temperature (SST), focusing on the Korea seas characterized as a complex marine system. A three-month re-forecast experiment was conducted for each of the four seasons in 2016 starting from January, forced with Climate Forecast System version 2 (CFSv2) forecast data. The assessment using relative root-mean-square-error was taken for the last month SST of each experiment. Compared to the CFSv2, the OMIDAS revealed a better prediction skill for the Korea seas SST, particularly in the Yellow sea mainly due to a more realistic representation of the topography and current systems. Seasonally, the OMIDAS showed better predictability in the warm seasons (spring and summer) than in the cold seasons (fall and winter), suggesting seasonal dependency in predictability of the Korea seas. In addition, the mid-range predictability for the Korea seas significantly varies depending on regions: the predictability was higher in the East Sea than in the Yellow Sea. The improvement in the seasonal predictability for the Korea seas by OMIDAS highlights the importance of a regional ocean modeling system for a medium-range marine prediction.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.779-789
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• 1999

To support daily ground-level $O_3$ forecasting in Seoul, a transfer function model(TFM) has been developed by using surface meteorological data and pollutant data(previous-day [$O_3$] and [$NO_2$]) from 1 May to 31 August in 1997. The forecast performance of the TFM was evaluated by statistical comparison with $O_3$ concentration observed during September it is shown that correlation coefficient(R), root mean squared error(RMSE), normalized mean squared error(NMSE) and mean relative error(MRE) were 0.73, 15.64, 0.006 and 0.101, respectively. The TFM appeared to have some difficulty forecasting very high $O_3$ concentrations. To compare with this model, multiple regression model(MRM) was developed for the same period. According to statistical comparison between the TFM and MRM. two models had similar predictive capability but TFM based on $O_3$ concentration higher than 60 ppb provided more accurate forecast than MRM. It was concluded that statistical model based on TFM can be useful for improving the accuracy of local $O_3$ forecast.

• Atmosphere
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• v.34 no.1
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• pp.23-34
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• 2024

The Korea Meteorological Administration has improved medium-range weather forecasts by implementing post-processing methods to minimize numerical model errors. In this study, we employ a statistical correction technique known as the minimum continuous ranked probability score (CRPS) to refine medium-range forecast guidance. This technique quantifies the similarity between the predicted values and the observed cumulative distribution function of the Unified Model Ensemble Prediction System for Global (UM EPSG). We evaluated the performance of the medium-range forecast guidance for surface air temperature and relative humidity, noting significant enhancements in seasonal bias and root mean squared error compared to observations. Notably, compared to the existing the medium-range forecast guidance, temperature forecasts exhibit 17.5% improvement in summer and 21.5% improvement in winter. Humidity forecasts also show 12% improvement in summer and 23% improvement in winter. The results indicate that utilizing the minimum CRPS for medium-range forecast guidance provide more reliable and improved performance than UM EPSG.

• Atmosphere
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• v.25 no.1
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• pp.67-83
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• 2015

Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

• Atmosphere
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• v.21 no.4
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• pp.481-495
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• 2011

This study is performed to consider the threshold values of heavy rain warning in Korea using 98 surface meteorological station data and 590 Automatic Weather System stations (AWSs), damage data of National Emergency Management Agency for the period of 2005 to 2009. It is in need to arrange new criteria for heavy rain considering concept of rainfall intensity and rainfall damage to reflect the changed characteristics of rainfall according to the climate change. Rainfall values from the most frequent rainfall damage are at 30 mm/1 hr, 60 mm/3 hr, 70 mm/6 hr, and 110 mm/12 hr, respectively. The cumulative probability of damage occurrences of one in two due to heavy rain shows up at 20 mm/1 hr, 50 mm/3 hr, 80 mm/6 hr, and 110 mm/12 hr, respectively. When the relationship between threshold values of heavy rain warning and the possibility of rainfall damage is investigated, rainfall values for high connectivity between heavy rain warning criteria and the possibility of rainfall damage appear at 30 mm/1 hr, 50 mm/3 hr, 80 mm/6 hr, and 100 m/12 hr, respectively. It is proper to adopt the daily maximum precipitation intensity of 6 and 12 hours, because 6 hours rainfall might be include the concept of rainfall intensity for very-short-term and short-term unexpectedly happened rainfall and 12 hours rainfall could maintain the connectivity of the previous heavy rain warning system and represent long-term continuously happened rainfall. The optimum combinations of criteria for heavy rain warning of 6 and 12 hours are 80 mm/6 hr or 100 mm/12 hr, and 70 mm/6 hr or 110 mm/12 hr.