• Korean Journal of Agricultural and Forest Meteorology
• /
• v.19 no.3
• /
• pp.120-129
• /
• 2017

Agrometeorological advisories for farms and orchards are issued when daily weather exceeds a predefined range of the local reference climate, which is a long-term average of daily weather for the location. The reference climate at local scales is prepared by various simplification methods, resulting in uncertainty in the agrometeorological advisories. We restored daily weather data for the 1981-2010 period and analyzed the differences in prediction results of weather risk by comparing with the temporal and spatial simplified normal climate values. For this purpose, we selected the agricultural drought index (ADI) among various disaster related indices because ADI requires many kinds of weather data to calculate it. Ten rural counties within the Seomjin River Basin were selected for this study. The normal value of 'temporal simplification' was calculated by using the daily average value for 30 years (1981-2010). The normal value of 'spatial simplification' is the zonal average of the temporally simplified normal values falling within a standard watershed. For residual moisture index, temporal simplification normal values were overestimated, whereas spatial simplification normal values were underestimated in comparison with non-simplified normal values. The ADI's calculated from January to July 2017 showed a significant deviation in terms of the extent of drought depending on the normal values used. Through this study, we confirmed that the result of weather risk calculation using normal climatic values from 'simplified' methods can affect reliability of the agrometeorological advisories.

• Journal of the Korea Institute of Building Construction
• /
• v.17 no.1
• /
• pp.73-81
• /
• 2017

In this paper, the periods of cold weather concrete and early frost damage depending on each region in South Korea were studied using the climate data from Korea meteorological administration. The specifications of Korea Concrete Institute(KCI) and Architectural Institute of Japan(AIJ) were applied to provide the periods of cold weather concrete. The periods of early frost damage risk(EFD) were calculated by Hasegawa's suggestion depending on 91 cities in Korea. Climate data for 5 years (2008~2012) were used to obtain both of the periods. Existing data from 1971 to 2000 were also used to compare differences in the periods between past and present study. The periods of cold weather concrete by KCI were calculated about 98 days on average. As the latitude goes up and close to mountain areas, the periods tend to be increased. The periods by present study was shown to be reduced compared to that of previous study by 1~2days. The period of EFD was provided with the level of daily lowest temperature from $-5^{\circ}C$, $-2^{\circ}C$ and $0^{\circ}C$. The beginning day of the period of EFD was earlier than the period of cold weather concrete and the finishing day of the period of EFD was later than the period of cold weather concrete.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.3
• /
• pp.149-155
• /
• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• The Plant Pathology Journal
• /
• v.36 no.1
• /
• pp.54-66
• /
• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• Journal of Korea Water Resources Association
• /
• v.52 no.10
• /
• pp.753-761
• /
• 2019

Localized heavy storm is one of the major causes of flood damage in urban regions. According to the recent disaster statistics in South Korea, the frequency of urban flood is increasing more frequently, and the scale is also increasing. However, localized heavy storm is difficult to predict, making it difficult for local government officials to deal with floods. This study aims to construct a Flood risk matrix (FRM) using ensemble weather prediction data and to assess its applicability as a means of reducing damage by securing time for such urban flood response. The FRM is a two-dimensional matrix of potential impacts (X-axis) representing flood risk and likelihood (Y-axis) representing the occurrence probability of dangerous weather events. To this end, a regional FRM was constructed using historical flood damage records and probability precipitation data for basic municipality in Busan and Daegu. Applicability of the regional FRMs was assessed by applying the LENS data of the Korea Meteorological Administration on past heavy rain events. As a result, it was analyzed that the flood risk could be predicted up to 3 days ago, and it would be helpful to reduce the damage by securing the flood response time in practice.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.10
• /
• pp.733-738
• /
• 2017

In this study, we selected chlorine, a typical toxic material used in many workplaces, as the leakage material, and through the analysis of alternative scenarios based on the meteorological conditions in the summer frequently encountered in accidents, we suggest ways to improve the (method of analysis/accident scenario modeling). The analysis of 296 chemical accidents from January 2014 to December 2016 found that the highest rate of occurrence was in summer, accounting for 35.81% of the total. According to the risk assessment, the influence range and number of inhabitants in the influence area were 712.4 m and 20,090 under the annual mean weather conditions and 796.2 m and 27,143 people under the summer mean weather conditions, respectively. This result implies that, under certain conditions, the range of impacts in the current alternative scenario is incomplete. Therefore, risk assessment systems need to be improved in order to take into consideration the characteristics of each chemical substance.

• Journal of Korea Multimedia Society
• /
• v.16 no.5
• /
• pp.610-618
• /
• 2013

The damaged district by flooding has been changed from mainly farmland to cities due to the weather phenomena which is different from the past. This has caused not only irreparable damage of people's lives and property but social infrastructures. There also exist serious damages such as isolation of drivers and traffic jam as the roads were flooded. In this study, we suggested a method to develop a flood risk index focused on not Si, Gun or Gu but roads. In addition, flood risk index in the roads just at the moment, when it rains quantitatively, will be provided by using real-time rainfall information provided by the Weather Center. Then it should be helpful to prevent people from being isolated by flooded roads in advance.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.15 no.4
• /
• pp.320-331
• /
• 2013

The National Center for AgroMeteorology (NCAM) has designed a risk management solution for individual farms threatened by the climate change and variability. The new service produces weather risk indices tailored to the crop species and phenology by using site-specific weather forecasts and analysis derived from digital products of the Korea Meteorological Administration (KMA). If the risk is high enough to cause any damage to the crops, agrometeorological warnings or watches are delivered to the growers' cellular phones with relevant countermeasures to help protect their crops against the potential damage. Core techniques such as scaling down of weather data to individual farm level and the crop specific risk assessment for operational service were developed and integrated into a cloud based service system. The system was employed and implemented in a rural catchment of 50 $km^2$ with diverse agricultural activities and 230 volunteer farmers are participating in this project to get the user-specific weather information from and to feed their evaluations back to NCAM. The experience obtained through this project will be useful in planning and developing the nation-wide early warning service in agricultural sector exposed to the climate and weather extremes under climate change and climate variability.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.5B
• /
• pp.267-272
• /
• 2012

To reduce the flood risk caused by unexpected heavy rainfall, many prediction methods for flood have been developed. A major constituent of flood prediction is an accurate rainfall estimation which is an input of hydrologic models. In this study, a regional-scale weather model which can provide relatively longer lead time for flood mitigation compared to the Nowcasting based on radar system will be introduced and applied to the Chongmi river basin located in central part of South Korea. The duration of application of a regional weather model is from July 11 to July 23 in 2006. The estimated rainfall amounts were compared with observations from rain gauges (Sangkeuk, Samjook, and Sulsung). For this rainfall event at Chongmi river basin, Thomson and Kain-Frisch Schemes for microphysics and cumulus parameterization, respectively, were selected as optimal physical conditions to present rainfall fall amount in terms of Mean Absolute Relative Errors (MARE>0.45).

• International Journal of Highway Engineering
• /
• v.16 no.3
• /
• pp.59-66
• /
• 2014

PURPOSES : This study is to suggest heavy snow risk analysis in Seoul. METHODS : Recently, the increase of extreme weather caused by global warming raises the occurrences of unpredictable natural disasters and the loss potential of human disasters by land use facilities accumulation. It is necessary to develop the risk analysis for the natural and human disasters. RESULTS : In this study, heavy snow risk analysis among natural disasters in Seoul was suggested. The spatial unit of risk analysis level was established for the lines and administrative districts. CONCLUSIONS : The risk analysis was performed using risk matrix of disaster occurrence score and disaster damage score. The components affecting the risk disaster analysis by types were analyzed and the application of heavy snow risk analysis was suggested.