• Journal of Korea Water Resources Association
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• v.31 no.5
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• pp.621-635
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• 1998

The feasibility of cloud seeding in Korea is presented from analyses of precipitation, cloud amount, satellite data, and upper air data. The daily mean precipitation over Dae-Kwan-Ryong is the largest(~4.5 mm/day), while the intensity of precipitation (amount of yearly rainfall divided by the frequency of rain days) over Southern area is above 14 mm/day, which shows the largest in Korea. Both the daily mean and the intensity of precipitation over Andong area are the smallest with values of ~2.7 mm/day and ~11 mm/day, respectively. In the meanwhile, the occurrence frequency of appropriate cloud top temperature (-10'~-30') for cloud seeding over the region has a large value (~130 days/year). The precipitation patterns of the region vary with wind direction and intensity calculated from 43 AWSs(Automatic Weather Station) and the additional 7 rain guages which were installed along Northern and Southern part of the Sobaek mountain. The Sc(Stratocumulus) cloud type over Andong is frequently observed, and Cirrus and Altostratus next. From the results, it is estimated that the feasibility of cloud seeding over the area would be high if a proper strategy of cloud seeding is set up. LCL (Lifting Condensation Level) and CCL (Convective Condensation Level) have the most frequency in 1000-950 hPa being occupied 4/9 of total analysis period and in 400-500 hPa, respectively, with both small variations from season to season. The correlation between vapor mixing ratio and CCL is the highest in Summer and the lowest in Winter. It means that the height of cumulus in Summer is high with an abundant water vapor but vice versa in Winter, and that the strategy of cloud seeding should be different with seasons.

• Atmosphere
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• v.30 no.3
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• pp.221-236
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• 2020

The Gangneung region has the complicated geographical characteristics being adjacent to East Sea and Taeback mountains, and thus sea breeze could play an important role in local weather in various aspects. This study aims to understand overall characteristics of sea breeze largely based on long-term (2009~2018) ground-based observation data. We also propose a selection criteria of sea breeze occurrence day; 1) daily precipitation is less than 10 mm, 2) surface wind direction is 0~110° (northerly to easterly) for more than 3 hours during the daytime, 3) wind direction is 110~360° for more than 3 hours during the nighttime, and 4) land and sea temperature difference is positive during the daytime, 5) sea and land sea-level pressure difference is more than 0.5 hPa. As a result, a total of 595 days was selected for the past 10 years. The occurrence of sea breeze is the highest in late Spring to early Summer (May to June). The passage time of sea breeze at the inland station (1.6 km farther inland) is one hour later than the coastal station. On the typical sea breeze event of April 12, 2019, the passage speed and duration of sea breeze was 15 km hr^-1 and about 9 hours, respectively, with its depth of about 500 m and its head swelling. The current results emphasize the critical role of sea breeze in forecasting surface temperature and wind, and contribute to relieve heat wave especially in summer in the Yeongdong region.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Atmosphere
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• v.16 no.1
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• pp.33-48
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• 2006

A classification of snowfall type based on development mechanism is proposed using previous snowfall studies, operational experiences, etc. Five types are proposed: snowfall caused by 1) airmass transformation (AT type), 2) terrain effects in a situation of expanding Siberian High (TE type), 3) precipitation systems associated with extratropical cyclones (EC type), 4) indirect effects of extratropical cyclones passing over the sea to the south of the Korean peninsula (ECS type), and 5) combined effects of TE and ECS types (COM type). Snowfall events during 1981-2001 are classified according to the 5 types mentioned above. For this, 118 events, with at least one station with daily snowfall depth greater than 20 cm, are selected. For the classification, synoptic weather charts, satellite images, and precipitation data are used. For TE and COM types, local sea-level pressure chart is also used to confirm the presence of condition for TE type (this is done for events in 1990 and thereafter). The classification shows that 109 out of 118 events can be classified as one of the 5 types. In the remaining 8 events, heavy snowfall occurred only in Ullung Island. Its occurrence may be due to one or more of the following mechanism: airmass transformation, mesoscale cyclones and/or mesoscale convergence over the East Sea, etc. Each type shows different characteristics in location of snowfall and composition of precipitation (i.e., dry snow, rain, and mixed precipitation). The AT-type snowfall occurs mostly in the west coast, Jeju and Ullung Islands whereas the TE-type snowfall occurs in the East coast especially over the Young Dong area. The ECS-type snowfall occurs mostly over the southern part of the peninsula and some east cost area (sometimes, whole south Korea depending on the location of cyclones). The EC- and COM-type snowfalls occur in wider area, often whole south Korea. Precipitation composition also varies with the type. The AT-type has a snow ratio (SR) higher than the mean value. The TE- and EC-type have SR similar to the mean. The ECS- and COM-type have SR values smaller than the mean. Generally the SR values at high latitude and mountainous areas are higher than those at the other areas. The SR value informs the characteristics of the precipitation composition. An SR value larger than 10 means that all precipitation is composed of snow whereas a zero SR value means that all precipitation is composed of rain.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.252-261
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• 2018

This study was conducted to develop a method to generate daily maximum and minimum temperatures using monthly data. We analyzed 30-year daily weather data of the 23 meteorological stations in South Korea and elucidated the parameters for predicting annual trend (center value ($\hat{U}$), amplitude (C), deviation (T)) and daily fluctuation (A, B) of daily maximum and minimum temperature. We use national average values for C, T, A and B parameters, but the center value is derived from the annual average data on each stations. First, daily weather data were generated according to the occurrence of rainfall, then calibrated using monthly data, and finally, daily maximum and minimum daily temperatures were generated. With this method, we could generate daily weather data with more than 95% similar distribution to recorded data for all 23 stations. In addition, this method was able to generate Growing Degree Day(GDD) similar to the past data, and it could be applied to areas not subject to survey. This method is useful for generating daily data in case of having monthly data such as climate change scenarios.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.513-521
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• 2008

The objective of this study is the evaluation of the impact on the construction condition due to historical observation data and IPCC SRES A2 climate change scenario. For this purpose, daily precipitation and daily mean temperature data which have been observed over the past 30 years by Korea Meteorological Administration are collected and applied. Also, A2 scenarios during 2011~2040 and 2051~2080 are used for this analysis. According to the results of trend analyses on annual precipitation and annual mean temperature, they are on the increase mostly. The available working day and the day occurred an extreme event are used as correlation indices between climate factor and construction condition. For the past observation data, linear regression and Mann-Kendall test are used to analyze the trend on the correlation index. As a result, both working day and extreme event occurrence day are increased. Likewise, for the future, variation analysis showed the similar result to that of the past and the occurrence frequency of extreme events is increased obviously. Therefore, we can project to increase flood damage potential on the construction site by climate change.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.4
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• pp.108-114
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• 1980

A deterministic conceptual erosion model which simulates detachment, entrainment, transport and deposition of eroded soil particles by rainfall impact and flowing water is presented. Both upland and channel phases of sediment yield are incorporated into the erosion model. The algorithms for the soil erosion and sedimentation processes including land and crop management effects are taken from the literature and then solved using a digital computer. The erosion model is used in conjunction with the modified Kentucky Watershed Model which simulates the hydrologic characteristics from watershed data. The two models are linked together by using the appropriate computer code. Calibrations for both the watershed and erosion model parameters are made by comparing the simulated results with actual field measurements in the Four Mile Creek watershed near Traer, Iowa using 1976 and 1977 water year data. Two water years, 1970 and 1978 are used as test years for model verification. There is good agreement between the mean daily simulated and recorded streamflow and between the simulated and recorded suspended sediment load except few partial differences. The following conclusions were drawn from the results after testing the watershed and erosion model. 1. The watershed and erosion model is a deterministic lumped parameter model, and is capable of simulating the daily mean streamflow and suspended sediment load within a 20 percent error, when the correct watershed and erosion parameters are supplied. 2. It is found that soil erosion is sensitive to errors in simulation of occurrence and intensity of precipitation and of overland flow. Therefore, representative precipitation data and a watershed model which provides an accurate simulation of soil moisture and resulting overland flow are essential for the accurate simulation of soil erosion and subsequent sediment transport prediction. 3. Erroneous prediction of snowmelt in terms of time and magnitute in conjunction with The frozen ground could be the reason for the poor simulation of streamflow as well as sediment yield in the snowmelt period. More elaborate and accurate snowmelt submodels will greatly improve accuracy. 4. Poor simulation results can be attributed to deficiencies in erosion model and to errors in the observed data such as the recorded daily streamflow and the sediment concentration. 5. Crop management and tillage operations are two major factors that have a great effect on soil erosion simulation. The erosion model attempts to evaluate the impact of crop management and tillage effects on sediment production. These effects on sediment yield appear to be somewhat equivalent to the effect of overland flow. 6. Application and testing of the watershed and erosion model on watersheds in a variety of regions with different soils and meteorological characteristics may be recommended to verify its general applicability and to detact the deficiencies of the model. Futhermore, by further modification and expansion with additional data, the watershed and erosion model developed through this study can be used as a planning tool for watershed management and for solving agricultural non-point pollution problems.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.121-121
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• 2020

Varying dominant processes, including Tropical Cyclone (TC) and non-TC rainfall events, have been known to drive the occurrence of precipitation in South Korea. With the changes in the pattern of the Earth's climate due to anthropogenic activities, nonstationarity or changes in the magnitude and frequency of these dominant processes have been separately observed for the past decades and are expected to continue in the coming years. These changes often cause unprecedented hydrologic events such as extreme flooding which pose a greater risk to the society. This study aims to take into account a more reliable future climate condition with two dominant processes. Diverse statistical models including the hidden markov chain, K-nearest neighbor algorithm, and quantile mappings are utilized to mimic future rainfall events based on the recorded historical data with the consideration of the varying effects of TC and non-TC events. The data generated is then utilized to the hydrologic model to conduct a flood frequency analysis. Results in this study emphasize the need to consider the nonstationarity of design rainfalls to fully grasp the degree of future flooding events when designing urban water infrastructures.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.933-951
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• 2009

Climate change of global warming may affect the water circulation in Korea. Rainfall is occurred with complex of multiple climatic indices. Therefore, the rainfall is one of the most significant index due to climate change in the process of water circulation. In this research, multiple time series data of rainfall events were extracted to represent the rainfall characteristics. In addition, the occurrence of rainfall time series analyzed by annual, seasonal and monthly data. Analysis method used change analysis of mean and standard deviation and trend analysis. Also, changes in rainfall characteristics and the relative error was calculated during the last 10 years for comparison with past data. At the results, significant statistical results weren't showed by randomness of rainfall data. However, amount of rainfall generally increased last 10 years, and number of raining days had trend of decrease. In addition, seasonal and monthly changes in the rainfall characteristics can be found to appear differently.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.709-720
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• 2017

In this study, we tried to analyze the correlation between corn yield and, satellite-based vegetation index, NDVI (Normalized Difference Vegetation Index) and various climatic factors in the three provinces of Northeast China during the past 20 years (1996-2015). The corn yields in the corn cultivation area of all three provinces showed a statistically significant positive correlation with the NDVI of the harvest period. Also, these have significant negative correlation with the daily maximum temperature in August and September and the occurrence frequency of above $30^{\circ}C$ for the summer season. The correlation between the corn yields and the precipitation showed a significant positive coefficient in only Liaoning Province in July, but the correlation was not found in Jilin and Heilongjiang Provinces. In this study, the NDVI and the daily maximum temperature data are suitable to be used as predictors of corn yield in the three provinces of Northeast China provinces.