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
제11권 제1호 (2016년 3월) 6건
1.
2016.03
서비스 종료(열람 제한)
A statistical change-point analysis examined the existence of climate regime shift in the time series of the Asian dust frequency in Seoul during spring. As a result, the Asian dust frequency in Seoul during spring has sharply increased since 1993. To investigate the cause of the increasing Asian dust frequency in Seoul during spring, therefore the averages during the period of 1993 to 2011 and the differences in large-scale environment during the period of 1974 to 1992 were analyzed. According to the analysis results for 850 hPa, 500 hPa, and 200 hPa stream flows, northwesterly anomaly was formed from the Lake Baikal to the Korean Peninsula due to the intensification of anomalous anticyclonic circulation in Northern China. This northwesterly anomaly has become a major circulation that moves the sand particles from Northern China to Seoul.
2.
2016.03
서비스 종료(열람 제한)
The occurrence of heat waves estimated on historical runs of climate change was compared to that on reanalysis data from 1981 to 2005. Heat waves in the future then were predicted on the basis of climate change scenarios from 2006 to 2100. For the past period, the heat wave days predicted from the climate change scenarios data overestimated and than those by the reanalysis data. For the future period, the heat wave days increased until the mid-21st century and then stay stagnant by the RCP 2.6 scenario. However, the yearly heat wave days steadily increased until 2100 by the RCP 8.5 scenario. The synoptic cause of the most severe year of the heat wave days was analyzed as a strong high pressure developed around the Korean peninsula. The high pressure under the RCP 2.6 scenario was caused by the high level jet stream in the border area between China and Russia, whereas the high pressure under the RCP 8.5 scenario was caused by the strong high level jet stream and pressure ridge in the East Sea.
3.
2016.03
서비스 종료(열람 제한)
We developed the Parameter-elevation Regressions on Independent Slopes Model(PRISM)-based Dynamical downscaling Error correction(PRIDE)-Wind speed(WS) model version 3.0 to produce highresolution( 1km) grid data at a monthly time scale by using observation and Regional Climate Model(RCM) wind speed data. We consequently produced monthly wind speed grid data during the observation period(2000~2014) and the future period(2021~2100) for Representative Concentration Pathway(RCP) 2 type scenarios by using the PRIDE-WS model. The PRIDE-WS model is constructed by combining the MK(Modified Korean)-PRISM-Wind, the RCM anomaly and Cumulative Density Function(CDF) fitting, basically based on Kim et al.(2016)’s algorithm applied for daily precipitation. The upper level wind(80m altitude) was estimated by Deacon equation using surface wind speed that was produced by the PRIDE-WS model. The results show that the wind speed at the upper level generally increased during the summer season while it decreased during the spring, autumn and winter seasons.
4.
2016.03
서비스 종료(열람 제한)
In this study, we analyzed the performance of calibrated probabilistic forecasts of surface temperature over Pyeongchang area in Gangwon province by using Bayeisan Model Averaging (BMA). BMA has been proposed as a statistical post-processing method and a way of correcting bias and underdispersion in ensemble forecasts. The BMA technique provides probabilistic forecast that take the form of a weighted average of Gaussian predictive probability density function centered on the bias-corrected forecast for continuous weather variables. The results of BMA to calibrate surface temperature forecast from 24-member Ensemble Prediction System for Global (EPSG) are obtained and compared with those of multiple regression. The forecast performances such as reliability and accuracy are evaluated by Rank Histogram (RH), Residual Quantile-Quantile (R-Q-Q) plot, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and the Continuous Ranked Probability Score (CRPS). The results showed that BMA improves the calibration of the equal weighted ensemble and deterministic-style BMA forecasts performs better than that of the deterministic forecast using the single best member.
5.
2016.03
서비스 종료(열람 제한)
This study evaluates climate simulations performed over the CORDEX Phase 2 East Asia domain with a Regional Climate Model (RCM), Consortium for Small-scale Modelling (COSMO)- Climate Limited-area Modelling (CLM) (CCLM), driven by the European Centre for Medium- Range Weather Forecast (ECMWF) Reanalysis (ERA)-Interim reanalysis. We focus on examining the influence of spectral nudging on East Asian climate simulations by comparing a control simulation to a simulation including spectral nudging. Spatio-temporal climatology of temperature and precipitation is well reproduced by CCLM with distinct regional patterns of systematic biases. Improvement of CCLM performance on East Asian climate simulation is identified when applying a spectral nudging technique. The use of spectral nudging alleviates systematic biases existing on horizontal winds and geopotential heights during summer and winter. Stronger reduction in systematic biases is found at lower troposphere during summer, partly explaining improvement of summer precipitation over the northeast Asia. Bias and RMSE analysis shows considerable improvement occurring in both climatology and inter-annual variability of summer precipitation and winter temperature over South Korea. Results from a Taylor diagram analysis reveal that CCLM reproduces the observed spatial patterns reasonably well for both summer and winter, and that spectral nudging improves spatial pattern simulations of horizontal winds and geopotential height at 850hPa during summer.
6.
2016.03
서비스 종료(열람 제한)
This paper considers a homogeneous multiple regression (HMR) model and a non-homogeneous multiple regression model, that is, ensemble model output statistics (EMOS), which are easy to implement postprocessing techniques to calibrate probabilistic forecasts that take the form of Gaussian probability density functions for continuous weather variables. The HMR and EMOS predictive means are biascorrected weighted averages of the ensemble member forecasts and the EMOS predictive variance is a linear function of the ensemble variance. We also consider the alternative implementations of HMR and EMOS which constrains the coefficients to be non-negative and we call these techniques as HMR+ and EMOS+, respectively. These techniques are applied to the forecasts of surface temperature over Pyeongchang area using 24-member Ensemble Prediction System for Global (EPSG). The performances are evaluated by rank histogram, residual quantile-quantile plot, means absolute error, root mean square error and continuous ranked probability score (CRPS). The results showed that HMR+ and EMOS+ models perform better than the raw ensemble mean, HMR and EMOS models. In the comparison of HMR+ and EMOS+ models, HMR+ performs slightly better than EMOS+ model in terms of CRPS, however they had a very similar CRPS and if there exists a ensemble spread-skill relationship, it is seen that EMOS is slightly better calibrated than the homogeneous multiple regression model.
