• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.197-197
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• 2012

본 연구에서는 대기 순환패턴 및 수문 환경변화에 영향을 미치는 주요인자인 El Ni$\tilde{n}$o-Southern Oscillation (ENSO)의 서로 다른 형태인 Warm-pool (WP) El Ni$\tilde{n}$o, Cold-tongue (CT) El Ni$\tilde{n}$o에 따른 한강유역의 봄철 (March~May)과 여름철 (June~August) 강수 및 유출의 특성을 분석하였다. 봄철 강수량의 경우, WP El Ni$\tilde{n}$o 시기에 증가추세를 보이며, 강수의 변동특성 또한 크게 나타났다. 여름철 강수량의 경우, CT El Ni$\tilde{n}$o 시기에는 평년보다 대체로 건조한 경향을 보이나, WP El Ni$\tilde{n}$o 시기에는 유역 전체에서 습한 경향을 보였으며 강수의 변동성은 매우 작은 것으로 분석되었다. 봄철 유출량의 경우, CT El Ni$\tilde{n}$o 시기와 WP El Ni$\tilde{n}$o 시기에 모두 평년치보다 크게 나타났으며, WP El Ni$\tilde{n}$o 시기에 한강 남부 대부분 유역에서 유출량이 통계적으로 유의한 증가 경향을 보였다. 여름철 유출량의 경우, CT El Ni$\tilde{n}$o 시기에는 대부분 유역에서 평년치보다 감소하나 수문 변량의 변동성은 큰 것으로 분석되었다. WP El Ni$\tilde{n}$o 시기에는 거의 모든 유역에서 유출이 증가하는 것으로 나타났으며, 특히 13개 중권역에서는 유출의 변동성이 작고 통계적으로 유의한 증가패턴이 분석되었다. 따라서 본 연구는 서로 다른 두가지 형태의 El Ni$\tilde{n}$o패턴에 대하여 한강유역의 봄철과 여름철 수자원 변동성에 민감하게 영향을 미치고 있음을 확인하였으며 수자원의 효율적인 예측 및 관리와 안정적인 용수공급을 위한 수문기상인자와 수문자료간의 관계 규명에 유용하게 활용될 것으로 기대한다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.1
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• pp.32-39
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• 2013

Although space-borne satellites are useful in obtaining information all around the world, they cannot observe at a suitable time and place. In order to overcome these limitations, an airborne remote sensing system was developed in this study. It is composed of a SAR sensor and a thermal infrared sensor. Additionally GPS, IMU, and thermometer/hygrometer were attached to the plane for radiometric and geometric calibration. The brightness of SAR image varies depending on surface roughness, and capillary waves on the sea surface, which are easily generated by sea winds, induce the surface roughness. Thus, sea surface wind can be estimated using the relationship between quantified SAR backscattering coefficient and the sea surface wind. On the other hand, thermal infrared sensor is sensitive to measure object's temperature. Sea surface temperature is obtained from the thermal infrared sensor after correcting the atmospheric effects which are located between sea surface and the sensor. Using these two remote sensing sensors mounted on airplane, four test flights were carried out along the west coast of Korea. The obtained SAR and thermal infrared images have shown that these images were useful enough to monitor coastal environment and estimate marine meteorology data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.23-23
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• 2020

털부처꽃(Lythrum salicaria L.)은 예로부터 발효해서 술로 마시고, 잎은 채소로 식용하며, 식중독 치료 및 설사를 멈추게 하는 효과가 있는 것으로 알려져 있다. 한국, 중국, 아프리카, 유럽, 북아메리카 등의 습한 지역에 분포하며, 7~8월에 취산꽃차례로 붉은 자주색의 꽃이 핀다. 본 연구는 털부처꽃을 지피용 소재로 개발하기 위한 최적의 재배기술을 확립하기 위해 수행되었다. 재배방법의 확립을 위하여 2019년 4월 27일에 파종하여 생산된 유묘를 7월 4일에 정식하여 9월 24일까지 12주 동안 재배하였다. 공통 조건은 3치 비닐 포트에 원예상토를 충진하고, 200구 트레이에 셀당 1립씩 파종하여 생산된 1셀 묘를 정식하였으며, 추비, 차광, 적심 등은 처리하지 않았다. 추비 실험은 hyponex 하이그레이드(N-P-K, 7-10-6)를 0, 500, 1000, 2000mg·L^-1를 4주간 간격으로 총 3회 엽면시비 하였다. 차광 정도 실험은 0, 35, 55, 75% 차광막을 이용하였으며, 적심은 무적심과 4주차에 1회 적심처리 하였다. 연구의 결과, 털부처꽃은 추비 농도가 증가할수록, 생육이 감소하는 결과를 보였다. 차광처리 별로는 무차광 조건에서 생육이 가장 왕성한 결과를 보였고, 광량의 부족은 전반적으로 생육을 억제하는 경향이었다. 적심 처리는 줄기 신장을 크게 억제하였으며, 측지수를 증가시키는 결과를 보였다. 엽수는 무처리와 비교하였을 때 유의적인 차이가 없는 것으로 확인되었다. 따라서 털부처꽃은 적심을 하는 것이 효과적인 것을 알 수 있었으며, 적심 시기에 관한 추가적인 연구가 필요할 것으로 생각된다. 결론적으로 추비처리는 털부처꽃의 생육에 부정적인 영향을 주므로 처리하지 않고, 무차광 조건에서 재배하는 것이 전반적인 생육에 유리하였다. 또한 도장이 잘되는 식물임으로 적심처리하는 것이 좋을 것으로 판단된다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.47-56
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• 2018

Providing high-quality meteorological observation data at sites that represent actual farming environments is essential for useful agrometeorological services. The Automated Agricultural Observing System (AAOS) of the Korean Meteorological Administration, however, has been deployed on lawns rather than actual farm land. In this study, we show the inaccuracies that arise in AAOS data by analyzing temporal and vertical variation and by comparing them with data recorded by the National Center for AgroMeteorology (NCAM) tower that is located at an actual farming site near the AAOS tower. The analyzed data were gathered in August and October (before and after harvest time, respectively). Observed air temperature and water vapor pressure were lower at AAOS than at NCAM tower before and after harvest time. Observed reflected shortwave radiation tended to be higher at AAOS than at NCAM tower. Soil variables showed bigger differences than meteorological observation variables. In August, observed soil temperature was lower at NCAM tower than at AAOS with smaller diurnal changes due to irrigation. The soil moisture observed at NCAM tower continuously maintained its saturation state, while the one at AAOS showed a decreasing trend, following an increase after rainfall. The trend changed in October. Observed soil temperature at NCAM showed similar daily means with higher diurnal changes than at AAOS. The soil moisture observed at NCAM was continuously higher, but both AAOS and NCAM showed similar trends. The above results indicate that the data gathered at the AAOS are inaccurate, and that ground surface cover and farming activities evoke considerable differences within the respective meteorological and soil environments. We propose to shift the equipment from lawn areas to actual farming sites such as rice paddies, farms and orchards, so that the gathered data are representative of the actual agrometeorological observations.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.53-68
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• 1998

Observation of hydrometeors' behavior in the atmosphere is important to understand weather and climate. By conventional observations, we can get the distribution of water vapor at limited number of points on the earth. In this study, the precipitable water has been estimated from the split window channel data on GMS-5 based upon the technique developed by Chesters et al.(1983). To retrieve the precipitable water, water vapor absorption parameter depending on filter function of sensor has been derived using the regression analysis between the split window channel data and the radiosonde data observed at Osan, Pohang, Kwangiu and Cheju staions for 4 months. The air temperature of 700 hPa from the Global Spectral Model of Korea Meteorological Administration (GSM/KMA) has been used as mean air temperature for single layer radiation model. The retrieved precipitable water for the period from August 1996 through December 1996 are compared to radiosonde data. It is shown that the root mean square differences between radiosonde observations and the GMS-5 retrievals range from 0.65 g/$cm^2$ to 1.09 g/$cm^2$ with correlation coefficient of 0.46 on hourly basis. The monthly distribution of precipitable water from GMS-5 shows almost good representation in large scale. Precipitable water is produced 4 times a day at Korea Meteorological Administration in the form of grid point data with 0.5 degree lat./lon. resolution. The data can be used in the objective analysis for numerical weather prediction and to increase the accuracy of humidity analysis especially under clear sky condition. And also, the data is a useful complement to existing data set for climatological research. But it is necessary to get higher correlation between radiosonde observations and the GMS-5 retrievals for operational applications.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.797-807
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• 2014

The Sea Surface Temperature (SST) is one of the most important oceanic environmental factors in determining the change of marine environments and ecological activities. Satellite thermal infrared images can be effective for understanding the global trend of sea surface temperature due to large scale. However, their low spatial resolution caused some limitations in some areas where complicated and refined coastal shapes due to many islands are present as in the Korean Peninsula. The coastal ocean is also very important because human activities interact with the environmental change of coastal area and most aqua farming is distributed in the coastal ocean. Thus, low-cost airborne thermal infrared remote sensing with high resolution capability is considered for verifying its possibility to extract SST and to monitor the changes of coastal environment. In this study, an airborne thermal infrared system was implemented using a low-cost and ground-based thermal infrared camera (FLIR), and more than 8 airborne acquisitions were carried out in the western coast of the Korean Peninsula during the periods between May 23, 2012 and December 7, 2013. The acquired thermal infrared images were radiometrically calibrated using an atmospheric radiative transfer model with a support from a temperature-humidity sensor, and geometrically calibrated using GPS and IMU sensors. In particular, the airborne sea surface temperature acquired in June 25, 2013 was compared and verified with satellite SST as well as ship-borne thermal infrared and in-situ SST data. As a result, the airborne thermal infrared sensor extracted SST with an accuracy of $1^{\circ}C$.

• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.97-107
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• 2011

For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.