• 대기
• /
• 제34권2호
• /
• pp.217-231
• /
• 2024

The Korea Integrated Model (KIM)-based real-time volcanic ash dispersion prediction system, which employs the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, has been developed to quantitatively predict volcanic ash dispersion in East Asia and the Northwest Pacific airspace. This system, known as KIM-HYSPLIT, automatically generates forecasts for the vertical and horizontal spread of volcanic ash up to 72 hours. These forecasts are initiated upon the receipt of a Volcanic Ash Advisory (VAA) from the Tokyo Volcanic Ash Advisory Center by the server at the Korea Meteorological Administration (KMA). This system equips KMA forecasters with diverse volcanic ash prediction information, complemented by the Unified Model (UM)-based HYSPLIT (UM-HYSPLIT) system. Extensive experiments have been conducted using KIM-HYSPLIT across 128 different volcanic scenarios, along with qualitative comparisons with UM-HYSPLIT. The results indicate that the ash direction predictions from KIM-HYSPLIT are consistent with those from UM-HYSPLIT. However, there are slight differences in the horizontal extent and movement speed of the volcanic ash. Additionally, quantitative verifications of the KIM-HYSPLIT forecasts have been performed, including threat score evaluations, based on recent eruption cases. On average, the KIMHYSPLIT forecasts for 6 and 12 hours show better quantitative alignment with the VAA forecasts compared to UM-HYSPLIT. Nevertheless, both models tend to predict a broader horizontal spread of the ash cloud than indicated in the VAA forecasts, particularly noticeable in the 6-hour forecast period.

• 대한안전경영과학회지
• /
• 제2권4호
• /
• pp.19-32
• /
• 2000

Assignable causes producing temporary deviation from the underlying system can influence on process adjustment and process monitoring in dynamic feedback control system. In this paper, the impact of assignable causes on EWMA forecasts and process adjustment which is based on the EWMA forecasts are derived for optimum control methods.

• 한국해안·해양공학회논문집
• /
• 제34권6호
• /
• pp.222-232
• /
• 2022

우리나라 연안의 30개 조위관측소에서 관측된 조위자료를 조화분해하여 2021년에 대한 해역별 장주기 조석성분의 특성 및 장주기 조석성분이 총 수위 예측에 미치는 영향에 대해 알아보았다. 먼저 관측조위의 조화분해 결과, 우리나라 연안에서 장주기 조석성분은 연주조(Sa)와 반년주조(Ssa)가 우세하였으며, 해역별로는 서해안에서 약 17.8 cm의 상대적으로 큰 진폭을 보였다. 계속해서 총 수위 예측에 대한 영향을 살펴보고자, 2021년을 연구 기간으로 장주기 조석성분이 포함된 예측조위와 포함되지 않은 예측조위를 생산하였고, 각각의 예측조위를 폭풍해일 모델의 해일고 예측결과에 더하여 총 수위를 생산하였다. 장주기 조석성분을 고려하지 않은 총 수위와 고려한 총 수위의 오차를 비교한 결과, 전반적으로 뚜렷한 계절적 차이가 나타났다. 장주기 조석성분을 고려하지 않은 총 수위에 비해서, 장주기 성분이 고려된 총 수위는 여름철에는 오차가 큰 폭으로 감소하였고, 겨울철에는 강한 음의 편차를 보이며 오차가 증가하는 경향이 나타났다. 이는 우리나라 겨울철의 강한 고기압과 같은 기상현상에 의한 영향이 예측조위와 해일고 예측결과에 이중으로 반영되어 나타난 결과로 해석되었다. 해일고 예측결과를 조화분해하였을 때, 연주기 성분이 우세하였고 이를 장주기 조석성분이 고려된 총 수위에서 제거하였을 때, 특히 겨울철에 나타난 강한 음의 편차가 사라지고 연평균 RMSE도 감소하는 것으로 나타났다.

• 한국수자원학회논문집
• /
• 제56권9호
• /
• pp.529-541
• /
• 2023

기후변화에 따른 가뭄 등 극한 기상을 예측하기 위해, 최근 전 세계적으로 GCMs 모델에 기반하여 향후 7개월까지를 전망하는 계절 기상 전망(Seasonal Forecasts) 기술이 꾸준히 관심을 받고 있다. 그러나 국내에서의 연구 및 적용사례는 많지 않으며, 특히 유역단위에서 그 활용성에 대해서는 검증이 필요하다. 따라서 본 연구에서는 국내 12개 다목적댐 유역에 대해 2011년부터 2020년까지 계절 기상 전망의 정확성을 과거 45년간의 기상 자료(climatology)와 비교하여 평가하였다. 본 연구에서는 ECMWF에서 제공하는 계절 기상 전망의 인자로 향후 수문전망에 활용성이 높은 강수, 기온 그리고 증발산을 선정하였고, 앙상블 전망의 정확성 평가를 위해 Continuous Ranked Probability Skill Score (CRPSS) 기법을 적용하였다. 또한, 계절 기상 전망에 대해 선형 편의 보정기법(Linear scaling)을 적용하여 그 효과를 평가하였다. 연구결과, 계절 기상 전망이 향후 1개월 간은 climatology와 유사한 정확도를 보이나 전망 리드타임이 증가함에 따라 그 정확도가 크게 낮아지는 특성을 나타냈다. Climatology와 비교하여, 계절적으로는 Dry season보다는 Wet season이 더 나은 결과를 보였으며, 특히 건조했던 2015년과 2017년의 Wet season에서는 장기간에 걸친 전망 정확도가 모두 높게 나타났다.

• 유통과학연구
• /
• 제17권10호
• /
• pp.41-48
• /
• 2019

Purpose - This study investigates whether financial analysts consider the intangible investment implicit in selling, general, and administrative (SG&A) expenditures to forecast firms' future earnings. Research design, data, and methodology - Using 52,609 U.S. firm-year observations spanning 1984-2016, this study examines the association between the Intangible investment implicit in SG&A expenditures and properties of analysts' earnings forecasts. To estimate the Intangible investment of SG&A, I decompose SG&A excluding R&D and advertising expenditures into maintenance and investment components following Enache and Srivastava (2017). Results - The main results show that analysts' earnings forecast errors and dispersion in analysts' forecasts increase with the intangible investment derived from SG&A because the investment component of SG&A affects future earnings and the uncertainty of those earnings. However, these results are weakened in the wholesale and retail industries where firms have a higher level of investment component of SG&A. I attribute the weaker results to low R&D expenditures in those industries. Conclusion - This study indicates that financial analysts incorporate the intangible investment of SG&A into their earnings forecasts differently across firms and industries. Furthermore, this study supports the argument for the separate reporting of the investment nature of SG&A from other operating expenses such as maintenance nature of SG&A.

• 대기
• /
• 제26권4호
• /
• pp.711-716
• /
• 2016

The dynamical model forecasts using state-of-art general circulation models (GCMs) have some limitations to simulate the real climate system since they do not depend on the past history. One of the alternative methods to correct model errors is to use the canonical correlation analysis (CCA) correction method. CCA forecasts at the present time show better skill than dynamical model forecasts especially over the midlatitudes. Model outputs are adjusted based on the CCA modes between the model forecasts and the observations. This study builds a canonical correlation prediction model for subseasonal (June) precipitation. The predictors are circulation fields over western North Pacific from the Global Seasonal Forecasting System version 5 (GloSea5) and observed snow cover extent over Eurasia continent from Climate Data Record (CDR). The former is based on simultaneous teleconnection between the western North Pacific and the East Asia, and the latter on lagged teleconnection between the Eurasia continent and the East Asia. In addition, we suggest a technique for improving forecast skill by applying the ensemble canonical correlation (ECC) to individual canonical correlation predictions.

• 응용통계연구
• /
• 제28권6호
• /
• pp.1235-1243
• /
• 2015

최근 들어 ICT 분야의 발달에 따라 데이터 사용량의 급격한 증가로 인터넷 트래픽 사용량 예측은 중요성은 강조되고 있다. 이러한 예측치를 적절한 트래픽 관리와 제어를 위한 계획 수립에 도움을 준다. 본 논문은, 5분 단위의 인터넷 트래픽 자료를 이용하여 결합 예측 모형을 제안하고자 한다. 이에 대하여 시계열의 대표적인 3개 모형인 Seasonal ARIMA, Fractional ARIMA(FARIMA), Taylor의 수정된 Holt-Winters 모형을 적용하였다. 모형 간 결합 예측 방법으로 예측치 간의 SA(Simple Average) 결합 예측 방법과 OLS(Ordinary Least Square)를 이용한 결합방법, ERLS(Equality Restricted Least Squares)를 이용한 결합 예측 방법, Armstrong(2001)이 제안한 MSE 기반 결합 예측 방법을 사용한다. 이에 따른 결과로서 3시간에서의 예측은 Seasonal ARIMA가 선택된 반면, 6시간 이후 예측에서는 결합 예측 방법이 좋은 예측 성능을 보여준다.

• 경영과학
• /
• 제26권1호
• /
• pp.77-91
• /
• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• The Journal of Asian Finance, Economics and Business
• /
• 제6권2호
• /
• pp.45-54
• /
• 2019

This study examines whether auditors restrain the analysts' opportunistic behavior as reviewing the companies' interim reports. Analysts' forecasts show a walkdown pattern in which their optimism has decreased as the earnings announcement date has approached. At the beginning of the year, there is a lack of high-quality benchmark information that enables information users to judge the accuracy of analyst's earnings forecasts. Thus, early in the year, analysts are highly inspired to disseminate optimistic forecasts in order to gain manager's favor. In this study, we examine adequate benchmarks prevent analysts from disclosing optimistically biased forecasts. We conjecture that auditors' efforts might mitigate analysts' walkdown pattern. To test this hypothesis, we use data from Korea, where it is mandatory to disclose auditor's review hours. We find that the analyst forecast's walkdown decreases with the ratio as well as the number of audit hours. It implies that an auditor's effort in reviewing interim financial information has a monitoring function that reduces analysts' opportunistic optimism at the beginning of the year. We conjecture that the tendency will be more pronounced when BIG4 auditors review the interim reports. Consistent with the prediction, BIG4 auditors' interim review effort is more effective in suppressing the analysts' walkdown.

• 융합경영연구
• /
• 제9권2호
• /
• pp.1-10
• /
• 2021

Purpose: In this work, we examine several time series models to predict internet and mobile transaction amount in South Korea, whereas Jeong (2020) has obtained the optimal forecasts for online shopping transaction amount by using time series models. Additionally, optimal forecasts based on the model considered can be calculated and applied to the Corona 19 situation. Research design, data, and methodology: The data are extracted from the online shopping trend survey of the National Statistical Office, and homogeneous and comparable in size based on 46 realizations sampled from January 2007 to October 2020. To achieve the goal of this work, both multiplicative ARIMA model and Holt-Winters Multiplicative seasonality method are taken into account. In addition, goodness-of-fit measures are used as crucial tools of the appropriate construction of forecasting model. Results: All of the optimal forecasts for the next 12 months for two online shopping transactions maintain a pattern in which the slope increases linearly and steadily with a fixed seasonal change that has been subjected to seasonal fluctuations. Conclusions: It can be confirmed that the mobile shopping transactions is much larger than the internet shopping transactions for the increase in trend and seasonality in the future.