• 대기
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• 제28권2호
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• pp.123-139
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• 2018

The prediction skills of stratospheric sudden warming (SSW) events and its impacts on the tropospheric prediction skills in global seasonal forecasting system version 5 (GloSea5), an operating subseasonal-to-seasonal (S2S) model in Korea Meteorological Administration, are examined. The model successfully predicted SSW events with the maximum lead time of 11.8 and 13.2 days in terms of anomaly correlation coefficient (ACC) and mean squared skill score (MSSS), respectively. The prediction skills are mainly determined by phase error of zonal wave-number 1 with a minor contribution of zonal wavenumber 2 error. It is also found that an enhanced prediction of SSW events tends to increase the tropospheric prediction skills. This result suggests that well-resolved stratospheric processes in GloSea5 can improve S2S prediction in the troposphere.

• 대기
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• 제28권3호
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• pp.233-245
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• 2018

This study explores the Subseasonal-to-Seasonal (S2S) prediction skills of the Northern Hemisphere mid-latitude geopotential height in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment. The prediction skills are quantitatively verified for the period of 1991~2010 by computing the Anomaly Correlation Coefficient (ACC) and Mean Square Skill Score (MSSS). GloSea5 model shows a higher prediction skill in winter than in summer at most levels regardless of verification methods. Quantitatively, the prediction limit diagnosed with ACC skill of 500 hPa geopotential height, averaged over $30^{\circ}N{\sim}90^{\circ}N$, is 11.0 days in winter, but only 9.1 days in summer. These prediction limits are primarily set by the planetary-scale eddy phase errors. The stratospheric prediction skills are typically higher than the tropospheric skills except in the summer upper-stratosphere where prediction skills are substantially lower than upper-troposphere. The lack of the summer upper-stratospheric prediction skill is caused by zonal mean error, perhaps strongly related to model mean bias in the stratosphere.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

• 한국지구과학회지
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• 제39권3호
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• pp.211-227
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• 2018

본 연구에서는 한국기상청의 장기예측시스템 현업모형인 GloSea5의 성층권 극소용돌이 강화사례에 대한 예측성을 진단 및 검증하였다. 진단에 사용된 통계량은 이상상관계수(ACC, Anomaly Correlation Coefficient)와 평균제곱근 예측성(MSSS, Mean Squared Skill Score)으로, 1991-2010년간 발생한 14개 극소용돌이 강화사례에 대한 GloSea5의 예측성한계는 ACC를 기준으로 13.6일, MSSS를 기준으로 18.5일로 나타났다. 모형의 평균제곱오차(MSE, Mean Squared Error)의 각 성분을 정량적으로 비교분석한 결과, 예측성을 저하시키는 가장 큰 요인은 맴돌이(에디)오차로, 그 중 에디의 위상오차가 전체 예측오차의 큰 부분을 차지하는 것으로 나타났다. 또한 극소용돌이 현상이 수평적으로 큰 규모를 가지는 만큼 동서파수 1의 에디와 관련한 오차가 더 작은 규모의 에디에 비해 가장 크게 예측오차에 기여하는 것으로 나타났다. 한편, 분석한 사례들에 대하여 GloSea5의 대류권 순환에 대한 예측성은 성층권 예측성과는 큰 관련이 없는 것으로 나타났다. 이는 단순히 GloSea5 모형이 성층권-대류권 접합과정을 잘 모의하지 못하기 때문에 나타난 결과로 유추할 수 있다. 하지만, 극소용돌이 강화에 의한 영향에 비해 대류권에서 내부변동성의 절대적인 크기가 종종 크게 나타난다는 점을 감안하면, 모형에서 성층권-대류권 접합을 잘 모의하고 있더라도 극소용돌이 강화 자체만의 영향이 뚜렷하게 나타나지 않았을 가능성 또한 간과하면 안 될 것이다.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.113-124
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• 2024

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.

• 대기
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• 제25권4호
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• 대기
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• 제33권4호
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

• 한국과학교육학회지
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• 제32권5호
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• pp.841-854
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• 2012

이 연구의 목적은 초등 교사들의 예상, 추리 및 가설에 대한 개념 이해와 지도방법에 대해 알아보는 것이다. 이를 위하여 초등학교 과학 지도 경험이 있는 교사 22명과 반구조화된 개별 면담이 이루어졌다. 면담은 1회, 50~80분 정도 진행되었다. 면담 내용은 모두 녹음되었고, 전사하여 면담 문항별로 주제들을 도출하고 귀납적으로 범주화하였다. 연구는 전체적으로 질적 연구의 방법을 따라 진행되었다. 연구 결과, 참여 교사들은 예상, 추리, 가설의 중요성을 인식하고 있었으나 개념을 정확하게 이해하지 못했고, 예상, 추리, 가설을 구분하여 설명하는 것에 어려움을 호소하였다. 교사들의 지도방법을 알아보기 위해 수업 중 탐구의 비중, 용어 사용 여부, 지도 시기, 발문 등으로 나누어 정리하였고 많은 교사들이 이들을 지도하는 것을 힘들어하였다. 그 이유로 교사역량부족, 학생들로 인한 어려움, 교육과정의 문제 등으로 답하였다. 아울러 교사들이 예상, 추리, 가설을 바르게 인식하지 못하는 원인에는 탐구과정요소에 대한 인식부족, 교과의 부담감 및 교재연구 부족, 평가위주의 교육체제, 교사교육의 부족 등이 있었다. 이러한 연구 결과들로부터 본 연구에서는 과학 관련 연수 등을 통해 교사들에게 탐구과정요소의 개념 및 지도방법에 대한 프로그램이 강화되어야 함을 제안한다.

• 한국과학교육학회지
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• 제16권3호
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• pp.249-259
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• 1996

The purpose of this study was to analyze relationships among science achievement, science process skills and affective perception of high school students. The affective perception was included attitude toward science and science anxiety in the study. The instruments were developed HARS and SAMS for this study. The subject was sampled 1,115 students by stratified cluster sampling method. The major findings of this study were as follows: The tendency to affective perception was investigated according to students variables. Atittude toward science was showed a negative perception on female than male, in rural area than city. Science anxiety was percepted highly on female than male, in rural area than city. Attitude toward science showed positive relations to science process skills, science achievement, but which showed negative relation to science anxiety. Science anxiety showed negative relations among science process skills, science achievement and attitude toward science. Structural relationships among affective perception, science process skills and science achievement were analyzed by effect size through the path analysis on the independent and dependent variables. By the results, it was indicated that there have significant direct effect not only affective perception influence on science achievement but also on science process skills in hypothesized model. Prediction of science achievement and science process skills were clarified to characteristics of the affective perception.Therefore, understanding about affective perception will be helpful to make the strategy of science teaching

• 한국보육지원학회지
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• 제11권5호
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• pp.91-111
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• 2015

본 연구에서는 유아가 오감을 이용한 벼 관찰활동을 통해 유아의 과학과정기술이 어떻게 변화하는지 살펴보고자 실시되었다. 연구참여자는 경기도 오산시에 소재한 Y어린이집에 재원중인 만3세 유아 10명이었다. 유아의 과학과정기술의 변화를 살펴보기 위하여 Spradley(1980)의 참여관찰 분석과정을 최정열(2000)이 수정 보완한 분석과정을 사용하였다. 분석자료에는 유아 인터뷰 자료, 유아관찰일지, 교사 관찰기록 등이 사용되었다. 연구결과 오감을 이용한 벼 관찰활동은 유아의 과학과정기술에 긍정적인 효과가 있는 것으로 나타났다. 유아는 초기에 비해 후기로 갈수록 벼에 대해 예측하기, 관찰하기, 분류하기, 비교하기, 측정하기 능력이 향상되었다. 본 연구결과는 유아의 과학적 사고 신장을 위한 교수방법의 기초자료로 활용될 수 있을 것이다.