• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.153-163
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• 2017

The objective of this study is to enhance the model's speed of estimating weather variables (e.g., minimum/maximum temperature, sunshine hour, PRISM (Parameter-elevation Regression on Independent Slopes Model) based precipitation), which are applied to the Agrometeorological Early Warning System (http://www.agmet.kr). The current process of weather estimation is operated on high-performance multi-core CPUs that have 8 physical cores and 16 logical threads. Nonetheless, the server is not even dedicated to the handling of a single county, indicating that very high overhead is involved in calculating the 10 counties of the Seomjin River Basin. In order to reduce such overhead, several cache and parallelization techniques were used to measure the performance and to check the applicability. Results are as follows: (1) for simple calculations such as Growing Degree Days accumulation, the time required for Input and Output (I/O) is significantly greater than that for calculation, suggesting the need of a technique which reduces disk I/O bottlenecks; (2) when there are many I/O, it is advantageous to distribute them on several servers. However, each server must have a cache for input data so that it does not compete for the same resource; and (3) GPU-based parallel processing method is most suitable for models such as PRISM with large computation loads.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.146-157
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• 2019

One of the most distinctive features of the South Korean rural environment is that the variation of weather or climate is large even within a small area due to complex terrains. The Geospatial Schemes based on Topo-Climatology (GSTP) was developed to simulate such variations effectively. In the present study, we reviewed the progress of the geospatial schemes for production of farm-scale agricultural weather data. Efforts have been made to improve the GSTP since 2000s. The schemes were used to provide climate information based on the current normal year and future climate scenarios at a landscape scale. The digital climate maps for the normal year include the maps of the monthly minimum temperature, maximum temperature, precipitation, and solar radiation in the past 30 years at 30 m or 270 m spatial resolution. Based on these digital climate maps, future climate change scenario maps were also produced at the high spatial resolution. These maps have been used for climate change impact assessment at the field scale by reprocessing them and transforming them into various forms. In the 2010s, the GSTP model was used to produce information for farm-specific weather conditions and weather forecast data on a landscape scale. The microclimate models of which the GSTP model consists have been improved to provide detailed weather condition data based on daily weather observation data in recent development. Using such daily data, the Early warning service for agrometeorological hazard has been developed to provide weather forecasts in real-time by processing a digital forecast and mid-term weather forecast data (KMA) at 30 m spatial resolution. Currently, daily minimum temperature, maximum temperature, precipitation, solar radiation quantity, and the duration of sunshine are forecasted as detailed weather conditions and forecast information. Moreover, based on farm-specific past-current-future weather information, growth information for various crops and agrometeorological disaster forecasts have been produced.

• The Journal of Society for e-Business Studies
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• v.20 no.1
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• pp.183-199
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• 2015

Due to the Four Major Rivers Restoration Project, Nakdong River Estuary Barrage's designed flood quantity has been largely increased, and this has caused to construct several drainage gates at the right side of Eulsukdo island to secure the safety of downstream river area. For successful functioning of Nakdong River Estuary Barrage, such as flood control, disaster prevention, and the securing of sufficient water capacity, drainage gates at the both sides of island have to operate systematically and reliably. To manage this under restricted personnel and resources, we have implemented the IOS (Integrated Operation System) by integrating previous facilities and resources via information and communication technologies. The IOS has been designed to have higher availability and fault tolerance to function continuously even with the partial system's failure under the emergency situation like flood. Operators can use the system easily and acknowledge alarms of facilities through its IWS (Integrated Warning System) earlier. Preparing for Integrated Water Resources Management and Smart Water Grid, the architecture of IOS conformed to open system standards which will be helpful to link with the other systems easily.

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

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