• Atmosphere
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• v.34 no.2
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• pp.83-95
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• 2024

Atmospheric rivers (ARs) are closely related to local precipitation which can be both beneficial and destructive. Although several studies have evaluated their predictability, there is a lack of studies on East Asian ARs. This study evaluates the prediction skill of East Asian ARs in the Korean Integrated Model (KIM) for 2020~2022 summer. The spatial distribution of AR frequency in KIM is qualitatively similar to the observation but overestimated. In particular, the model errors greatly increase along the boundary of the western North Pacific subtropical high as the forecast lead time increases. When the prediction skills are quantitatively verified by computing the Anomaly Correlation Coefficient and Mean Square Skill Score, the useful prediction skill of daily AR around the Korean Peninsula is found up to 5 days. Such prediction limit is primarily set by the wind field errors with a minor contribution of moisture distribution errors. This result suggests that the improved prediction of atmospheric circulation field can improve the prediction of East Asian summer ARs and the associated precipitation.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Atmosphere
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• v.25 no.2
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• pp.323-337
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• 2015

This study explores the 6-month lead prediction skill of several climate indices that influence on East Asian climate in the GloSea5 hindcast experiment. Such indices include Nino3.4, Indian Ocean Diploe (IOD), Arctic Oscillation (AO), various summer and winter Asian monsoon indices. The model's prediction skill of these indices is evaluated by computing the anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) for ensemble mean values over the period of 1996~2009. In general, climate indices that have low seasonal variability are predicted well. For example, in terms of ACC, Nino3.4 index is predicted well at least 6 months in advance. The IOD index is also well predicted in late summer and autumn. This contrasts with the prediction skill of AO index which shows essentially no skill beyond a few months except in February and August. Both summer and winter Asian monsoon indices are also poorly predicted. An exception is the Western North Pacific Monsoon (WNPM) index that exhibits a prediction skill up to 4- to 6-month lead time. However, when MSSS is considered, most climate indices, except Nino3.4 index, show a negligible prediction skill, indicating that conditional bias is significant in the model. These results are only weakly sensitive to the number of ensemble members.

• Atmosphere
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• v.31 no.5
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• pp.511-523
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• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• Journal of the Korean earth science society
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• v.43 no.4
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• pp.511-519
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• 2022

Given the significant social and economic impact caused by heat waves, there is a pressing need to predict them with high accuracy and reliability. In this study, we analyzed the real-time forecast data from six models constituting the Subseasonal-to-Seasonal (S2S) prediction project, to elucidate the key mechanisms contributing to the prediction of the recent record-breaking Korean heat wave event in 2018. Weekly anomalies were first obtained by subtracting the 2017-2020 mean values for both S2S model simulations and observations. By comparing four Korean heat-wave-related indices from S2S models to the observed data, we aimed to identify key climate processes affecting prediction accuracy. The results showed that superior performance at predicting the 2018 Korean heat wave was achieved when the model showed better prediction performance for the anomalous anticyclonic activity in the upper troposphere of Eastern Europe and the cyclonic circulation over the Western North Pacific (WNP) region compared to the observed data. Furthermore, the development of upper-tropospheric anticyclones in Eastern Europe was closely related to global warming and the occurrence of La Niña events. The anomalous cyclonic flow in the WNP region coincided with enhancements in Madden-Julian oscillation phases 4-6. Our results indicate that, for the accurate prediction of heat waves, such as the 2018 Korean heat wave, it is imperative for the S2S models to realistically reproduce the variabilities over the Eastern Europe and WNP regions.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.82-84
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• 2021

Through the smart factory construction project, sensor data such as power, vibration, pressure, and temperature are collected from production facilities, and services such as predictive maintenance, defect prediction, and abnormality detection are developed through data analysis. In general, in the case of manufacturing data, because the imbalance between normal and abnormal data is extreme, an anomaly detection service is preferred. In this paper, FFT method is used to extract feature data of manufacturing data as a pre-stage of the anomaly detection service development. Using this method, we classified the produced products and confirmed results. In other words, after FFT of the representative pattern for each product, we verified whether product classification was possible or not, by calculating correlation coefficient.

• Geomechanics and Engineering
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• v.20 no.6
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• pp.475-484
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• 2020

The prediction of the ground conditions ahead of a tunnel face is very important, especially for tunnel boring machine (TBM) tunneling, because encountering unexpected anomalies during tunnel excavation can cause a considerable loss of time and money. Several prediction techniques, such as BEAM, TSP, and GPR, have been suggested. However, these methods have various shortcomings, such as low accuracy and low resolution. Most studies on electrical resistivity tomography surveys have been conducted using numerical simulation programs, but laboratory experiments were just a few. Furthermore, most studies of scaled model tests on electrical resistivity tomography were conducted only on the ground surface, which is a different environment as compared to that of mechanized tunneling. This study performed a laboratory experimental test to extend and verify a prediction method proposed by Lee et al., which used electrical resistivity tomography to predict the ground conditions ahead of a tunnel face in TBM tunneling environments. The results showed that the modified dipole-dipole array is better than the other arrays in terms of predicting the location and shape of the anomalies ahead of the tunnel face. Having longer upper and lower borehole lengths led to better accuracy of the survey. However, the number and length of boreholes should be properly controlled according to the field environments in practice. Finally, a modified and verified technique to predict the ground conditions ahead of a tunnel face during TBM tunneling is proposed.

• Atmosphere
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• v.32 no.2
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• pp.149-162
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• 2022

This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation for the period of 1991~2016. Additionally, we evaluated the prediction skill of those variables depending on the combination of the initial dates in the sub-seasonal scale for the dust source region affecting South Korea. The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) from GloSea5-ADAM were compared to that from Synoptic observation and European Centre for medium range weather forecasts reanalysis v5, respectively, based on Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC) as evaluation criteria. In general, the Asian dust and meteorological variables in the source region showed high ACC in the prediction scale within one month. For all variables, the use of the initial dates closest to the prediction month led to the best performances based on MBE, RMSE, and ACC, and the performances could be improved by adjusting the number of ensembles considering the combination of the initial date. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions.

• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.