간행물
기후연구 KCI 등재 Journal of Climate Research
- 발행기관 건국대학교 기후연구소
- 자료유형 학술지
- 간기 계간
- ISSN 1975-6151 (Print)2288-8772 (Online)
- 수록기간 2006 ~ 2021
- 주제분류 사회과학 > 지리학 사회과학 분류의 다른 간행물
- 십진분류KDC 453DDC 551
권호리스트/논문검색
제15권 제4호 (2020년 12월) 7건
1.
2020.12
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Extreme low temperature has various effects on society. It has negative impacts on health, industry, agriculture, and transportation during winter season. Therefore, extreme low temperature is recognized as main factor that causing traffic accidents. This study analyzed the relationship between low temperature and traffic accidents from 2012 to 2018. This study also compared the differences in vulnerability to low temperature between urban and rural areas. Generalized Additive Model (GAM) and Poisson regression model are used to estimate the thresholds and the level of traffic accidents. The results show that the thresholds tend to be decreased gradually, but the level of traffic accidents are more likely to increase in both urban and rural areas Comparatively, rural areas are more vulnerable to traffic accident caused by drop in temperature than urban areas.
2.
2020.12
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This study analyzed future projections on daily mean values and extremes for temperature and daily precipitation over Seoul metropolitan city using bias-corrected high-resolution multi-regional climate models. The factors of uncertainty for the future projection of climate variables were defined. In the time series analysis of future projections for regional climate models, the average daily temperature and the number of days of the hot day-hot night were predicted to have a stable trend in the RCP2.6 scenario, and showed a tendency to increase continuously in the RCP8.5 scenario. The daily mean precipitation and RX1day (annual daily maximum precipitation) had large annual variabilities in the models. In the estimation of the fraction of total variance, the daily mean temperature was dominated by the internal variability in the early 21st century and the most contributing to the scenario uncertainty in the late 21st century. The daily mean precipitation showed a remarkable contribution from the internal variability over the entire period. The number of days of the hot day-hot night showed a similar contribution pattern to that of the daily mean temperature. For the RX1day, the internal variability dominated over the entire period, and the scenario uncertainty had little contribution. This study will help establish more scientific climate change adaptation policies by providing the uncertainty information for future climate change projection.
3.
2020.12
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In this study, the impact of cumulus parameterization usage in Weather Research and Forecasting (WRF) model on reproducing summer precipitation in South Korea is evaluated. Two sensitivity experiments are set up with using cumulus parameterization (ON experiment) and without using cumulus parameterization, which is called Convection Permitting Model (OFF experiment). For the both ON and OFF experiments, the horizontal grid resolution is 2.5km, and initial and lateral boundary conditions are derived from ERA5 reanalysis data. Overall, both of the two experiments can capture the spatial distribution of 2014 summer mean and extreme precipitation but show dry biases in the southern region of Korean Peninsula. Occurrence percentage analyses for different precipitation intensity reveal that OFF experiments show better performance than ON experiment for extreme precipitation. In the case of heavy rainfall over Gyeongnam region for 25 August 2014, OFF experiment shows similar characteristic of rainfall to the observations, although it simulates earlier precipitation peak. On the other hand, ON experiment underestimates the amount of precipitation. Also, vertical distribution of equivalent potential temperature and strong southerly wind which play an important role in developing heavy rainfall on 25 August 2014 are better simulated in OFF experiment.
4.
2020.12
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In meteorological data, various studies are being conducted to improve the prediction performance of rainfall with irregular patterns, unlike temperature and solar radiation with certain patterns. Especially in the case of the short-term forecast model for Dong-Nae Forecasts provided by the Korea Meteorological Administration (KMA), forecast data are provided at 6-hour intervals, and there is a limit to analyzing the impact of disasters. In this study, Hydrological Quantitative Precipitation Forecast (HQPF) information was generated by applying the machine learning method to Local ENsemble prediction system (LENS), Radar-AWS Rainrates (RAR), AWS and ASOS observation data and Dong-Nae Forecast provided by the KMA. Through the preprocessing process, the temporal and spatial resolutions of all the data were converted to the same resolution, and the predictor of machine learning was derived through the factor analysis of the predictor. Considering the processing speed and expandability, the XGBoost method of machine learning was applied, and the Probability Matching (PM) method was applied to improve the prediction accuracy of heavy rainfall. As a result of evaluating the HQPF performance produced for 14 heavy rainfall events that occurred in 2020, it was found that the predicted performance of HQPF was improved quantitatively and qualitatively.
5.
2020.12
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Because of the population growth and industrialization in recent decades, the air quality over the world has been worsened with the increase of PM10 concentration. Korea is located near the eastern part of China which has many industrial complexes, so the consideration of China’s air quality is necessary for the PM10 prediction in Korea. This paper examines a machine learning-based modeling of the prediction of tomorrow’s PM10 concentration in the form of a gridded map using the AirKorea observations, Chinese cities’ air quality index, and NWP (numerical weather prediction) model data. A blind test using 23,048 cases in 2019 produced a correlation coefficient of 0.973 and an MAE (mean absolute error) of 4.097㎍/㎥, which is high accuracy due to the appropriate selection of input variables and the optimization of the machine learning model. Also, the prediction model showed stable predictability irrespective of the season and the level of PM10. It is expected that the proposed model can be applied to an operative system if a fine-tuning process using a larger database is accomplished.
6.
2020.12
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As global warming continues, we expect extreme climate events to occur more frequently and intensively and the extremes to become normal eventually. Such timing of when the climate extremes in the present climate become normal in a future climate has been estimated in previous studies, but these studies used different methods and definitions, making an interpretation and application of the results difficult. Defining the year of beginning of a new normal climate as the timing of unprecedented climate (TUC), this study suggests a new estimation method using a return level of extreme temperatures. The TUC was estimated using CMIP5 climate model simulation data with an application to the wintertime daily maximum and minimum temperatures in Korea. With a 50-year return level obtained from Generalized Extreme Value distribution of the CMIP5 historical experiment data, overall in the models, TUC was estimated to come in the 2070s under RCP 8.5 (a business- as-usual) scenario and in the 2090s under RCP 4.5 (an intermediate level of emissions) scenario. Using this new method, TUC can be estimated for various fields globally or regionally in different seasons with different variables, providing a useful guideline for climate change mitigation and adaption policy and also a timetable for the actions.
7.
2020.12
서비스 종료(열람 제한)
In this study, a high-resolution daily data set of surface weather were obtained from PRIDE(PRISMbased Dynamic downscaling Error correction) model for the period of 2000 to 2017 over South Korea. The simulation data of five RCM(Regional Climate Model) were also used which are forced by the CMIP6 participating model UK-ESM as the boundary condition under historical period (2000-2014) and SSP 5-8.5 period (2015- 2017). Here we compared the RCM data and the PRIDE data with MK-PRISM data in terms of ensemble mean and ensemble spread. Results show that the PRIDE model effectively eliminates systematic error in the RCM up to 63.0% for daily average temperature, 72.2% for daily maximum temperature, 68.2% for daily minimum temperature, and 28.7% for daily precipitation when evaluated from the RMSE perspective. Overall, the ensemble spread of the PRIDE model is significantly decreased from 1.46°C to 0.36°C for daily temperature and from 2.0 mm/day to 0.72 mm/day for daily precipitation compared to the RCM ensemble spread, indicating that the largest systematic error of the RCMs is effectively removed in the PRIDE model.
