• Korean Journal of Environmental Agriculture
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• v.41 no.1
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• pp.32-40
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• 2022

BACKGROUND: Climate-smart agriculture (CSA) has been proposed for sustainable agriculture and food security in an agricultural ecosystem disturbed by climate change. However, scientific approaches to local agricultural ecosystems to realize CSA are rare. This study attempted to evaluate the weather condition, rice production, and greenhouse gas emissions from the rice cultivation in Hwaseong-si, Gyeonggi-do to fulfill CSA of the rice cultivation. METHODS AND RESULTS: Over the past 3 years (2017~2019), Chucheong rice cultivar yield and methane emissions were analyzed from the rice field plot (37°13'15"N, 127° 02'22"E) in the Gyeonggi-do Agricultural Research and Extension Services located in Gisan-dong, Hwaseong-si, Gyeonggi-do. Methane samples were collected from three automated closed chambers installed in the plot. The weather data measured through automatic weather station located in near the plot were analyzed. CONCLUSION(S): The rice productivity was found to vary with weather environment in the agricultural ecosystem. And methane emissions are high in a favorable weather condition for rice growth. Therefore, it is necessary to minimize the trade-off between the greenhouse gas emission target for climate change mitigation and productivity improvement for CSA in a local rice cultivation.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.305-311
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• 2019

Recently, global climate change causes abnormal weather and disaster countermeasures do not provide sufficient defense and mitigation because they were established according to the historical climate condition. Repeated torrential rains, in particular, are causing damage even in the robust urban flood defense system. Therefore, in this study, the change of runoff considering the spatial distribution of rainfall and urban characteristics was analyzed. For rainfall concentrated in small catchment, rainfall in the watershed must be accurately measured. This study is based on the rainfall data observed with Automated Surface Observing System (ASOS) and Automatic Weather Stations (AWS) provided by the Seoul Meteorological Administration. Effluent from the pumping station was estimated using the EPA-SWMM model and compared and analyzed. Catchments with rainwater pumping station are small with large portion of impermeable areas. Thus, when the ASOS data where is located from from the chatchment, runoff is often calculated using rainfall data that is different from rainfall in the catchment. In this study, the difference between rainfall data observed in the AWS near the catchment and ASOS away from the catchment was calculated. It was found that accurate rainfall should be used to operate rainwater pumping stations or forecast urban flooding floods. In addition, the results of this study may be helpful for estimating design rainfall and runoff calculation.

• Journal of the Korean Solar Energy Society
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• v.32 no.1
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• pp.1-7
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• 2012

Predictions of wind speed for six different near-shore sites were made using the NCAR (National Center for Atmospheric Research) wind data. The distances between the NCAR sites and prediction sites were varied between 40km and 150km. A well-known wind energy prediction program, WindPRO, was used. The prediction results were compared with the measured data from the AWS(Automated Weather Stations). Although the NCAR wind data were located far away from the AWS sites, the prediction errors were within 9% for all the cases. In terms of sector-wise wind energy distributions, the predictions were fairly close to the measurements, and the error in predicting main wind direction was less than $30^{\circ}$. This proves that the NCAR wind data are very useful in roughly estimating wind energy in offshore or near-shore sites where offshore wind farm might be constructed in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.366-372
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• 2019

The PRISM model has been used to estimate precipitation in South Korea where observation data are readily available at a large number of weather station. However, it is likely that the PRISM model would result in relatively low reliability of precipitation estimates in North Korea where weather data are available at a relatively small number of weather stations. Alternatively, a hybrid method has been developed to estimate the precipitation distribution in area where availability of climate data is relatively low. In the hybrid method, Regression coefficients between the precipitation-terrain relationships are applied to a low-resolution precipitation map produced using the PRISM. In the present study, a hybrid approach was applied to North Korea for estimation of precipitation distribution at a high spatial resolution. At first, the precipitation distribution map was produced at a low-resolution (2,430m) using the PRISM model. Secondly, a deviation map was prepared calculating difference between altitudes of synoptic stations and virtual terrains produced using 270m-resolution digital elevation map (DEM). Lastly, another deviation map of precipitation was obtained from the maps of virtual precipitation produced using observation data from the synoptic weather stations and both synoptic and automated weather station (AWS), respectively. The regression equation between precipitation and terrain was determined using these deviation maps. The high resolution map of precipitation distribution was obtained applying the regression equation to the low-resolution map. It was found that the hybrid approach resulted in better representation of the effects of the terrain. The precipitation distribution map for the hybrid approach had similar spatial pattern to that for the existing method. It was estimated that the mean annual cumulative precipitation of entire territory of North Korea was 1,195mm with a standard deviation of 253mm.

• Journal of Biosystems Engineering
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• v.25 no.2
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• pp.123-130
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• 2000

A forcasting scheme for daily solar irradiance on agricultural field sis proposed by application of chaos theory to a long term observation data. It was conducted by reconstruction of phase space, attractor analysis, and Lyapunov analysis. Using the methodology , it was determined whether evolution of the five climatic data such as daily air temperature , water temperature , relative humidity, solar radiation, and wind speed are chaotic or not. The climatic data were collected for three years by an automated weather station at Hwasung-gun, Kyonggi-province. The results showed that the evolution of solar radiation was chaotic , and could be predicted. The prediction of the evolution of the solar radiation data was executed by using ' local optimal linear reconstruction ' algorithm . The RMS value of the predicting for the solar radiation evolution was 4.32 MJ/$m^2$ day. Therefore, it was feasible to predict the daily solar radiation based on the chaos theory.

• Korean Journal Plant Pathology
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• v.12 no.1
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• pp.121-128
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• 1996

농업 기상 자료의 수집 및 식물병 예찰 절차를 통합한 시스템을 -32비트 개인용 컴퓨터 운영 체계인 OS/2에서 개발하였다. 통합 시스템은 무인기상관측기(AWS)로부터 자료 수집을 하는 절차, 준실시간 기상자료로부터 병예찰을 하는 절차, 기상 정보와 병예찰 정보를 글과 그림으로 출력하는 절차의 세 부분으로 나뉘어 있다. 통합 시스템은 여러 지역의 실시간 기상 자료를 수집하며 기상 자료를 이용하여 각 지역의 병예찰 정보를 즉시 생성한다. 본 연구에서는 기상 자료를 이용한 병예찰 모형의 예로서 도열병 예찰 시뮬레이션 모형을 사용하였다. 또한 식물병 예찰을 위하여 무인기상관측기가 갖추어져야 하는 최소한의 요구 사항을 검토하였다. 본 시스템은 각종 식물병 예찰 모형의 개발과 관련하여 각 모형의 구동을 위하여 쓰여질 수 있을 것이다. 현재 각 농촌진흥원과 지도소에는 많은 수의 무인기상관측기가 설치되어 있으므로 이를 이용하여 본 시스템을 실용화 할 수 있을 것이다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.2
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• pp.122-133
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• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.297-309
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• 2007

The first Korean geostationary weather satellite, Communications, Oceanography and Meteorology Satellite (COMS) will be launched in 2008. The ground station for COMS needs to perform geometric correction to improve accuracy of satellite image data and to broadcast geometrically corrected images to users within 30 minutes after image acquisition. For such a requirement, we developed automated and fast geometric correction techniques. For this, we generated control points automatically by matching images against coastline data and by applying a robust estimation called RANSAC. We used GSHHS (Global Self-consistent Hierarchical High-resolution Shoreline) shoreline database to construct 211 landmark chips. We detected clouds within the images and applied matching to cloud-free sub images. When matching visible channels, we selected sub images located in day-time. We tested the algorithm with GOES-9 images. Control points were generated by matching channel 1 and channel 2 images of GOES against the 211 landmark chips. The RANSAC correctly removed outliers from being selected as control points. The accuracy of sensor models established using the automated control points were in the range of $1{\sim}2$ pixels. Geometric correction was performed and the performance was visually inspected by projecting coastline onto the geometrically corrected images. The total processing time for matching, RANSAC and geometric correction was around 4 minutes.

• Atmosphere
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• v.29 no.5
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• pp.567-583
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• 2019

In this study, the high-resolution numerical simulations considering detailed anthropogenic heat, albedo, emission and roughness length are analyzed by using single layer Urban Canopy Model (UCM) in Weather Research Forecast (WRF). For this, improved urban parameter data for Seoul Metropolitan Area (SMA) was collected from global data. And then the parameters were applied to WRF-UCM model after it was processed into 2-dimensional topographical data. The 6 experiments were simulated by using the model with each parameter and verified against observation from Automated Weather Station (AWS) and flux tower for the temperature and sensible heat flux. The data for sensible heat flux of flux towers on Jungnang and Bucheon, the temperature of AWS on Jungnang, Gangnam, Bucheon and Neonggok were used as verification data. In the case of summer, the improvement of simulation by using detailed anthropogenic heat was higher than the other experiments in sensible flux simulation. The results of winter case show improved in all simulations using each advanced parameters in temperature and sensible heat flux simulation. Improvement of urban parameters in this study are possible to reflect the heat characteristics of urban area. Especially, detailed application of anthropogenic heat contributed to the enhancement of predicted value for sensible heat flux and temperature.

• Research in Plant Disease
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• v.8 no.4
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• pp.199-206
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• 2002

It was investigated on the relationship of the rice blast epidemics and the real-time meteorological factors, at the experimental paddy field in 1997. Weather factors(temperature, relative humidity, irradiation, precipitation, the direction of wind, wind speed, soil temperature and leaf-wetness, etc) were measured by using the automated weather station. The most influenced weather factor to blast epidemics, was the average max-temp($R^2$= 0.95) during 10 days before leaf blast epidemics, while the least thing was wind speed($R^2$= 0.24). The most potential weather factors correlated with the blast epidemics were T-ave(average temperature), T-max(maximum temperature), RH(Relative Humidity) and RD(Relative Humidity > 90% hrs). A statistics model(the regression equation) of the blast epidemics with the potential weather factors, was established as tallows ; Y = -3410.91 - 23.91 $\times$ T-ave ＋ 28.56 $\times$ T-max ＋ 41.0 $\times$ RH - 3.75 $\times$ RD, ($R^2$= 0.99). (T-ave >= 19$^{\circ}C$, T-max - T-ave >= 5.2$^{\circ}C$ and RH% >= 90.4%). According to the fitness test($\chi$$^2$) of the model, the observed blast disease severity was quite close to those expected.