• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.334-337
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• 2003

The results of this numerical model is usable to analysis for the phenomena of precipitation during the periods of a rainy season in the Northeastern Asia. Case l(start of rainy season) dominates over precipitation by the processing of convection from the equator region through the East China region, and then the most of water vapor is transported by the processing of advection from the India-monsoon region to this study region. Case 2(heavy rainy season) faints precipitation by the processing of convection in the Korean peninsula, but dominates precipitation by the processing of microphysics. the water vapor originates from the India-monsoon region.

• Journal of Environmental Science International
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• v.26 no.3
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• pp.289-302
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• 2017

During the research period, error analysis of the amount of daily precipitation was performed with data obtained from 2DVD, Parsivel, and AWS, and from the results, 79 days were selected as research days. According to the results of a synoptic meteorological analysis, these days were classified into 'LP type, CF type, HE type, and TY type'. The dates showing the maximum daily precipitation amount and precipitation intensity were 'HE type and CF type', which were found to be attributed to atmospheric instability causing strong ascending flow, and leading to strong precipitation events. Of the 79 days, most days were found to be of the LP type. On July 27, 2011 the daily precipitation amount in the Korean Peninsula reached over 80 mm (HE type). The leading edge of the Northern Pacific high pressure was located over the Korean Peninsula with unstable atmospheric conditions and inflow of air with high temperature and high humidity caused ascending flow, 120 mm/h with an average precipitation intensity of over 9.57 mm/h. Considering these characteristics, precipitation in these sample dates could be classified into the convective rain type. The results of a precipitation scale distribution analysis showed that most precipitation were between 0.4-5.0 mm, and 'Rain' size precipitation was observed in most areas. On July 9, 2011, the daily precipitation amount was recorded to be over 80 mm (CF type) at the rainy season front (Jangma front) spreading across the middle Korean Peninsular. Inflow of air with high temperature and high humidity created unstable atmospheric conditions under which strong ascending air currents formed and led to convective rain type precipitation.

• Journal of Korea Water Resources Association
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• v.35 no.1
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• pp.109-124
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• 2002

This study is an effort to develop a stochastic model of precipitation series that preserves the pattern of occurrence of precipitation events throughout the year as well as several characteristics of the duration, amount, and intensity of precipitation events. In this study an event cluster model is used to describe the occurrence of precipitation events. A logarithmic negative mixture distribution is used to describe event duration and separation. The number of events within each cluster is also described by the Poisson cluster process. The duration of each event within a cluster and the separation of events within a single cluster are described by a logarithmic negative mixture distribution. The stochastic model for hourly precipitation occurrence process is fitted to historical precipitation data by estimating the model parameters. To allow for seasonal variations in the precipitation process, the model parameters are estimated separately for each month. an analysis of thirty-four years of historical and simulated hourly precipitation data for Seoul indicates that the stochastic model preserves many features of historical precipitation. The seasonal variations in number of precipitation events in each month for the historical and simulated data are also approximately identical. The marginal distributions for event characteristics for the historical and simulated data were similar. The conditional distributions for event characteristics for the historical and simulated data showed in general good agreement with each other.

• Journal of the Korean Geographical Society
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• v.42 no.1 s.118
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• pp.1-14
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• 2007

This study aimed to evaluate weather forecast accuracies of minimum temperature, maximum temperature, precipitation and sky cover by the localized land forecast areas over South Korea Average forecast accuracy score of precipitation was the lowest while that of sky cover was the highest during the study period Overall forecast accuracy scores for Gangwon-do was the lowest while those for Gyeongsangnam-do and Gyeongsangbuk-do were higher than other areas. The frequencies of perfect forecast(eight points) by seasons, were the highest during winter and the lowest during summer. pressure pattern analyses for days when forecast accuracy scores were poor, showed that precipitation forecast accuracy scores were lower due to the movement of the stationary fronts during summers. When continental polar air masses expanded, forecast accuracy of temperature became greatly lower during autumns and winters As the migratory anticyclone pattern rapidly moved, forecast accuracy became lower during springs and autumns. Forecast accuracies were compared by wind directions at 850hPa for the Young-dong region where forecast accuracy was the lowest. Forecast accuracy scores on minimum and maximum temperatures were low when winds were westerlies and forecast accuracy scores of precipitation were low when winds were easterlies.

• Journal of Korea Water Resources Association
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• v.45 no.9
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• pp.875-885
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• 2012

In this study, the total precipitation amount in Jeju Island was estimated with the simulated precipitation for ungauged stations missing precipitation data using the spatial precipitation analysis. The missing data were generated through the modified multiple linear regression in this study, and the analysis of spatial precipitation was conducted with the PRISM(Parameter-elevation Regression on Independent Slope Model). The generated data with modified multiple linear regression model have similar pattern with original data. Thus, the model in this study shows good applicability to estimate the missing data. The difference of annual average precipitation between Case 1 (original data) and Case 2 (modified data) appears very small ratio which is about 1.5%. However, the difference of annual average precipitation according to elevation shows the large ratio up to 37.4%. As the results, the method of estimating missing data in this study would be useful to calculate the total precipitation amount at the low station density area and the places with the high spatial variation of precipitation.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.992-996
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• 2008

In this study, changes in precipitation across South Korea during snow seasons (November-April) and their potential are examined. Current (1973/74-2006/07) and future (2081-2100) time series of snow indices including snow season, snow-to-precipitation ratio, and snow impossible day are extracted from observed snow and precipitation data for 61 weather stations as well as observed and modeled daily temperature data. Analyses of linear trends reveal that snow seasons have shortened by 3-13 days/decade; that the snow-to-precipitation ratio (the percentage of snow days relative to precipitation days) has decreased by 4-8 %/decade. These changes are associated with pronounced formations of a positive pressure anomaly core over East Asia during the positive Arctic Oscillation winter years since the late 1980s. A snow-temperature statistical model demonstrates that the warming due to the positive core winter intensifies changes from snow to rain at the rate of $4.7cm/^{\circ}C$. The high pressure anomaly pattern has also contributed to decreases of air-sea thermal gradient which are associated with the reduction of snow could formation. Modeled data predict that a fingerprint of wintertime global warming causing changes from snow to rain will continue to be observed over the 21st century.

• The Journal of Engineering Geology
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• v.18 no.1
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• pp.1-6
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• 2008

Precipitation and groundwater level data sets from Kum river watershed were analyzed and compared. The correlation between groundwater level and the moving average of precipitation was analyzed. Moving averaging technique is stochastic method and that was used to consider the effect of precipitation events on groundwater level fluctuation. Groundwater level generally follows seasonal precipitation pattern and low level occurs from early December to late April. Relatively high groundwater level is appeared in wet spell (July and August). The correlation between groundwater level and the moving average of precipitation to consider precedent precipitation events was analyzed with minimum two-year data sets. When the precipitation and groundwater level data set pair was selected the precipitation gauge station is closely located to groundwater level gauge station in the upstream direction to minimize the non-homogeneous precipitation distribution effect. The maximum correlation was occurred when the averaging periods were from 10 days to 150 days with Kum river watershed data. The correlation coefficients are influenced by data quality, missing data periods, or snow melt effect, etc. The maximum coefficient was 0.8886 for Kum river watershed data.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.4
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• pp.83-91
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• 2010

Generally, the GCM (General Circulation Model) data by IPCC climate change scenarios are used for future weather prediction. IPCC GCM models predict well for the continental scale, but is not good for the regional scale. This paper tried to generate future temperature and precipitation of 8 scattered meteorological stations in South Korea by using the MIROC3.2 hires GCM data and applying LARS-WG downscaling method. The MIROC3.2 A1B scenario data were adopted because it has the similar pattern comparing with the observed data (1977-2006) among the scenarios. The results showed that both the future precipitation and temperature increased. The 2080s annual temperature increased $3.8{\sim}5.0^{\circ}C$. Especially the future temperature increased up to $4.5{\sim}7.8^{\circ}C$ in winter period (December-February). The future annual precipitation of 2020s, 2050s, and 2080s increased 17.5 %, 27.5 %, and 39.0 % respectively. From the trend analysis for the future projected results, the above middle region of South Korea showed a statistical significance for winter precipitation and south region for summer rainfall.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• 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.