• Korean Journal of Remote Sensing
• /
• v.34 no.6_2
• /
• pp.1261-1272
• /
• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Journal of the Korean earth science society
• /
• v.44 no.1
• /
• pp.1-12
• /
• 2023

Oceanic wind waves have been recognized as one of the important indicators of global warming and climate change. It is necessary to study the spatial and temporal variability of significant wave height (SWH) and wave direction in the Yellow Sea and a part of the East China Sea, which is directly affected by the East Asian monsoon and climate change. In this study, the spatial and temporal variability including seasonal and interannual variability of SWH and wave direction in the Yellow Sea and East China Sea were analyzed using European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data. Prior to analyzing the variability of SWH and wave direction using the model reanalysis, the accuracy was verified through comparison with SWH and wave direction measurements from Ieodo Ocean Science Station (I-ORS). The mean SWH ranged from 0.3 to 1.6 m, and was higher in the south than in the north and higher in the center of the Yellow Sea than in the coast. The standard deviation of the SWH also showed a pattern similar to the mean. In the Yellow Sea, SWH and wave direction showed clear seasonal variability. SWH was generally highest in winter and lowest in late spring or early summer. Due to the influence of the monsoon, the wave direction propagated mainly to the south in winter and to the north in summer. The seasonal variability of SWH showed predominant interannual variability with strong variability of annual amplitudes due to the influence of typhoons in summer.

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

• 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.

• Ocean Science Journal
• /
• v.42 no.4
• /
• pp.211-222
• /
• 2007

We compare insolation results calculated from two well-known empirical formulas (Socket and Beaudry's SB73 formula and the original Smithsonian (SMS) formula) and a radiative transfer model using input data predicted from meteorological weather-forecast models, and review the accuracy of each method. Comparison of annual mean daily irradiance values for clear-sky conditions between the two formulas shows that, relative to the SMS, the SB73 underestimates spring values by 9 W $m^{-2}$ in the northern Adriatic Sea, although overall there is a good agreement between the annual results calculated with the two formulas. We also elucidate the effect on SMS of changing the 'Sun-Earth distance factor (f)', a parameter which is commonly assumed to be constant in the oceanographic context. Results show that the mean daily solar radiation for clear-sky conditions in the northern Adriatic Sea can be reduced as much as 12 W $m^{-2}$ during summer due to a decrease in the f value. Lastly, surface irradiance values calculated from a simple radiative transfer model (GM02) for clear-sky conditions are compared to those from SB73 and SMS. Comparison with iu situ data in the northern Adriatic Sea shows that the GM02 estimate gives more realistic surface irradiance values than SMS, particularly during summer. Additionally, irradiance values calculated by GM02 using the buoy meteorological fields and ECMWF (The European Centre for Medium Range Weather Forecasts) meteorological data show the suitability of the ECMWF data usage. Through tests of GM02 sensitivity to key regional meteorological factors, we explore the main factors contributing significantly to a reduction in summertime solar irradiance in the Adriatic Sea.

• Journal of Advanced Navigation Technology
• /
• v.22 no.3
• /
• pp.233-239
• /
• 2018

Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.

• Atmosphere
• /
• v.26 no.2
• /
• pp.301-319
• /
• 2016

The evolutionary process of the polar low, which caused the heavy snowfall in the East Coast area on 11~12 February 2011, was investigated to describe in detail using synoptic weather charts, satellite imageries, and ERA (European Centre for Medium-Range Weather Forecasts Re-Analysis) -Interim reanalysis data. It was revealed that 1) the polar low was generated over the sea near Busan where a large cyclonic shear in the inverted trough branched from the parent low existed, 2) during the developing and mature stages, there was a convectively unstable region in the lower layer around the polar low and its south side, 3) the polar low was developed in the region where the static stability in the 500~850 hPa layer was the lowest, 4) the result from the budget analysis of the vorticity equation indicated that the increase in the vorticity at the lower atmosphere, where the polar low was located, was dominated mainly by the stretching term, 5) the warm core structure of the polar low was identified in the surface-700 hPa layer during the mature stage, 6) there was a close inverse relationship between a development of the polar low and the height of the dynamic tropopause over the polar low, and 7) for generation and development of the polar low, large-scale circulation systems, such as upper cold low and its combined short wave trough, major low (parent low), and polar air outbreak, should be presented, indicating that the polar low has the nature of the baroclinic disturbance.

• Journal of Digital Convergence
• /
• v.12 no.3
• /
• pp.237-242
• /
• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of Digital Convergence
• /
• v.12 no.3
• /
• pp.211-218
• /
• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.305-305
• /
• 2019

태풍에 의한 재해는 우리나라에서 발생하는 자연재해 중 발생빈도가 가장 높은 것으로 나타나며, 최근 들어 태풍 및 집중호우로 인한 홍수가 급증하고 있는 실정이다. 최근에는 치수증대사업으로 하천 범람의 재해가 감소하는 추세이지만, 도시지역의 경우 도시개발에 따른 내수 범람 피해가 증가하고 있고, 산지에서는 토석류 등의 토사 재해가 증가하고 있다. 이러한 홍수피해를 경감하기 위해서는 치수사업 등과 같은 구조적인 대책도 필요하지만, 정확한 홍수 예 경보를 통한 대비시간의 확보 등과 같은 비구조적인 대책도 중요하며, 홍수 예 경보를 통한 선행시간(Lead time)확보를 위해 강우 및 홍수예측 시스템 구축이 하나의 대안으로 대두되고 있다. 강우예측 기법으로는 레이더(Radar)를 통해 관측된 자료를 외삽하는 초단기 강우예측기법이 최근까지 많이 수행되어 왔다. 하지만 컴퓨터 계산 능력이 향상되면서 수치예보(Numerical Weather Prediction; NWP) 모델을 이용한 강우예측 및 수문학적 적용에 관한 연구들이 대두되고 있다. 본 연구에서는 수치예보모델을 이용하여 기상 및 수자원 간의 연계를 통한 강우 및 홍수 예측에 활용방안을 검토하기 위해 한국 기상청에서 제공하는 국지예보모델(LDAPS)과 예측 도메인에 한국을 포함하는 일본 기상청의 중규모 모델(MSM)을 이용하여 남강댐 유역 내 산청 유역에 대해 강우 및 홍수 예측 정확도를 평가하고 비교 검토하였다. 본 연구에서 적용한 LDAPS와 MSM은 사용하는 수치모델, 물리과정 매개변수, 자료동화 기법 및 지배 방정식 등이 다르기 때문에 직접적인 비교를 하는데 무리가 있지만 국내의 강우 및 홍수 예측 분야에서의 각 수치예보모델의 활용성을 검토하고자 한다.