• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.3
• /
• pp.252-261
• /
• 2018

This study was conducted to develop a method to generate daily maximum and minimum temperatures using monthly data. We analyzed 30-year daily weather data of the 23 meteorological stations in South Korea and elucidated the parameters for predicting annual trend (center value ($\hat{U}$), amplitude (C), deviation (T)) and daily fluctuation (A, B) of daily maximum and minimum temperature. We use national average values for C, T, A and B parameters, but the center value is derived from the annual average data on each stations. First, daily weather data were generated according to the occurrence of rainfall, then calibrated using monthly data, and finally, daily maximum and minimum daily temperatures were generated. With this method, we could generate daily weather data with more than 95% similar distribution to recorded data for all 23 stations. In addition, this method was able to generate Growing Degree Day(GDD) similar to the past data, and it could be applied to areas not subject to survey. This method is useful for generating daily data in case of having monthly data such as climate change scenarios.

• Journal of the Korean Geographical Society
• /
• v.39 no.1
• /
• pp.13-26
• /
• 2004

This paper examined the possibility of NET application for a relative weather stress index in Korea. The characteristic of NET distribution used temperature, relative humidity, wind speed which forecasting at Korean Meteorological Administration were analyzed. Regional critical values of daily maximum NET of stress index for summer resembled the distribution of daily maximum temperature because were not impacted wind and humidity but temperature. Regional critical values of daily minimum NET of stress index for winter distributed variously compared with summer. The highland region and the northern region of Seoul were impacted of low temperature and coastal region which strong wind. The occurrences of stressful days did not vary in summer, but obviously increased in winter after mid-1990s.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.2 no.4
• /
• pp.175-182
• /
• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• Journal of the Korean Geographical Society
• /
• v.47 no.2
• /
• pp.208-225
• /
• 2012

The spatial characteristics of changes in extreme temperature indices for 2070-2099 relative to 1971-2000 in the Republic of Korea were investigated using daily maximum (Tmax) and minimum (Tmin) temperature data from a regional climate model (HadGEM3-RA) based on the IPCC RCP4.5/8.5 at 12.5km grid spacing and observations. Six temperature-based indices were selected to consider the frequency and intensity of extreme temperature events. For validation during the reference period (1971-2000), the simulated Tmax and Tmin distributions reasonably reproduce annual and seasonal characteristics not only for the relative probability but also the variation range. In the future (2070-2099), the occurrence of summer days (SD) and tropical nights (TR) is projected to be more frequent in the entire region while the occurrence of ice days (ID) and frost days (FD) is likely to decrease. The increase of averaged Tmax above 95th percentile (TX95) and Tmin below 5th percentile (TN5) is also projected. These changes are more pronounced under RCP8.5 scenario than RCP4.5. The changes in extreme temperature indices except for FD show significant correlations with altitude, and the changes in ID, TR, and TN5 also show significant correlations with latitude. The mountainous regions are projected to be more influenced by an increase of low extreme temperature than low altitude while the southern coast is likely to be more influenced by an increase of tropical nights.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.206-206
• /
• 2023

지구온난화로 인한 기후변화에 따라 평균강수량과 증발량이 증가하며 강우지역 집중화와 강우강도가 높아질 가능성이 크다. 우리나라의 경우 협소한 국토면적과 높은 인구밀도로 기후변동의 영향이 크기 때문에 한반도에 적합한 유역규모의 수자원 예측과 대응방안을 마련해야 한다. 이를 위한 수자원 관리를 위해서는 유역에서 강수량, 유출량, 증발량 등의 장기적인 자료가 필요하며 경험식, 물리적 강우-유출 모형 등이 사용되었고, 최근들어 연구의 확장성과 비 선형성 등을 고려하기 위해 딥러닝등 인공지능 기술들이 접목되고 있다. 본 연구에서는 ASOS(동해, 태백)와 AWS(삼척, 신기, 도계) 5곳의 관측소에서 2011년~2020년까지의 일 단위 기상관측자료를 수집하고 WAMIS에서 같은 기간의 오십천 하구 일 유출량 자료를 수집 후 5개 관측소를 기준으로Thiessen 면적비를 적용해 기상자료를 구축했으며 Angstrom & Hargreaves 공식으로 잠재증발산량 산정해 3개의 모델에 각각 기상자료(일 강수량, 최고기온, 최대 순간 풍속, 최저기온, 평균풍속, 평균기온), 일 강수량과 잠재증발산량, 일 강수량 - 잠재증발산량을 학습 후 관측 유출량과 비교결과 기상자료(일 강수량, 최고기온, 최대 순간 풍속, 최저기온, 평균풍속, 평균기온)로 학습한 모델성능이 가장 높아 최적 모델로 선정했으며 일, 월, 연 관측유출량 시계열과 비교했다. 또한 같은 학습자료를 사용해 다층 퍼셉트론(Multi Layer Perceptron, MLP) 앙상블 모델을 구축하여 수자원 분야에서의 인공지능 활용성을 평가했다.

• Journal of the Korean Geographical Society
• /
• v.46 no.3
• /
• pp.257-276
• /
• 2011

This study aimed to analyze the changes of extreme temperature indices in order to investigate impacts of urbanization on changes of extreme temperature. It was analyzed 16 indices related to extreme temperature indices to 60 weather stations in South Korea. Extreme temperature indices-related summer mostly increased, and its related to winter decreased. Percentile-based indices were clearly increased more than fixed-based indices as a tropical night. Decreasing trend of extreme temperature indices related to winter had more clear than increasing trend of extreme temperature indices related to summer. It was similar to trend that urban temperature was more clearly increased in winter than summer. Decreasing trend of indices-related daily minimum temperature had more clear than increasing trend of indices-related daily maximum temperature. Also, it was similar to increasing trend of minimum temperature had more clear than maximum temperature.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.11 no.4
• /
• pp.185-191
• /
• 2009

Few studies have attempted to analyze variations of daily maximum temperature in the summer whereas many studies have analyzed warming trends in other seasons with respect to greenhouse gases or urban heat islands. We analyzed daily maximum temperature data for the summer season (June to August) at 18 locations in South Korea from 1983 to 2007. Compared to the climatic normal (from 1971 to 2000), an average increase of $0.1^{\circ}C$ was found for the summer daily maximum temperature along with an increase of $0.61MJ\;m^{-2}$ in daily solar radiation. Approximately 65% of the annual variations of the summer daily maximum temperature could be explained by the solar radiance alone. Higher atmospheric transmittance due to lower aerosol concentration (especially of sulfur dioxide) is believed to have caused the recent increase in solar irradiance. Daily maximum temperature of the summer is expected to keep rising if the clean air activities are maintained in the future.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.2
• /
• pp.80-98
• /
• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.201-217
• /
• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.53 no.1
• /
• pp.42-49
• /
• 2008

GDD models of corn were developed in bare soil, while sweet and super sweet corns are grown under black polyethylene (P. E.) film mulch in Korea. To develop a suitable GDD model under black P. E. film mulch, a super sweet com hybrid "Cambella-90" was planted from 1 April to 30 June in 2003 at the 10-day intervals under black P. E. film mulch and in bare soil. In bare soil the best GDD model was $GDD\;=\;{\sum}[H"+L')/2\;-\;10^{\circ}C]$, where H" was daily maximum temperature but is was substituted for $30^{\circ}C$ - (daily maximum temperature - $30^{\circ}C$) when higher than $30^{\circ}C$ and L' was daily minimum temperature, but it was substituted for $10^{\circ}C$ when lower than $10^{\circ}C$. The same GDD model could be adapted for com grown under black P. E. film mulch, but base temperature was substituted for $9^{\circ}C$. To determine planting date for the scheduled harvests, accumulated GDDs were calculated using 30-year average temperature data during the growing season. Under black P. E. film mulch planting dates were determined by subtracting GDD of the hybrid, $970^{\circ}C$, from accumulated GDD of scheduled harvest dates.