• Atmosphere
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• v.16 no.4
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• pp.351-358
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• 2006

The structure of frontal thunderstorm in winter time is different from that of in summer time over the Korean peninsula, due to dry tongue and upward motion. The dry tongue, that is propagation of dry zone from upper level to lower level, was formed after front passage and the upward motion is intensified by the strengthened low level jet. Since this mechanism makes the structure more unstable, thunderstorm occurs at relatively low cloud top height. This study suggests a forecast guidance of winter time frontal thunderstorm that thunderstorms develop when one of the following conditions are satisfied: 1) total totals (TT) >40, 2) K index >-10, 3) mixing ratio ${\geq}$ 3.5 g/kg.

• Atmosphere
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• v.29 no.4
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• pp.463-480
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• 2019

This study classified heavy rain types from K-means clustering for the hourly relationship between rainfall intensity and cloud top height over the Korean peninsula, and then examined their statistical characteristics for the period of June~August 2013~2018. Total rainfall amount of warm-type events was 2.65 times larger than that of the cold-type, whereas the lightning frequency divided by total rainfall for the warm-type was only 46% of the cold-type. Typical cold-type cases exhibited high cloud top height around 16 km, large reflectivity in the upper layer, and frequent lightning flashes under convectively unstable condition. Phenomenally, the cold-type cases corresponded to cloud cluster or multi-cell thunderstorms. However, two warm-type cases related to Changma and typhoon were characterized by heavy rainfall due to long duration, relatively low cloud top height and upper-level reflectivity, and the absence of lightning under the convectively neutral and extremely humid conditions. This study further confirmed that the forecast skill of rainfall could be improved by applying correction factor with the overestimation for cold-type and underestimation for warm-type cases in the Local Data Assimilation and Prediction System (LDAPS) operational model (e.g., BIAS score was improved by 5%).

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.647-658
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• 2021

Analysts' forecasts are important for providing useful guidance to investors, especially individual or small investors, and therefore it becomes critical to identify the elements which can potentially increase errors in analysts' forecasts. This study investigates potential factors which can lead to errors in forecasting by analysts, specifically in terms of the level and attributes of corporate earnings. Utilizing a sample of firms listed on the Korean stock markets, this study provides evidence that firms with more volatile and unpredictable earnings feature less accurate analyst forecasts. This study fills a void in the literature by conducting empirical tests for earnings attributes in terms of volatility and unpredictability that could potentially undermine the forecast accuracy. The negative association between the quality of earnings and forecast accuracy is more pronounced for firms with negative net income values. Additional analysis demonstrates that forecast accuracy is significantly lower for the fourth quarter than for other fiscal quarters and that fourth quarter earnings tend to be more volatile and unpredictable. This study contributes to the literature by providing new empirical evidence regarding the comprehensive effects of earnings quality and level on analysts' forecasting accuracy and further suggests potential factors contributing to the fourth quarter anomaly in analyst forecasts in terms of earnings attributes.

• Atmosphere
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• v.16 no.3
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• pp.247-257
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• 2006

The Yeong-dong heavy snowfall forecast supporting system has been developed during the last several years. In order to construct the conceptual model, we have examined the characteristics of heavy snowfalls in the Yeong-dong region classified into three precipitation patterns. This system is divided into two parts: forecast and observation. The main purpose of the forecast part is to produce value-added data and to display the geography based features reprocessing the numerical model results associated with a heavy snowfall. The forecast part consists of four submenus: synoptic fields, regional fields, precipitation and snowfall, and verification. Each offers guidance tips and data related with the prediction of heavy snowfalls, which helps weather forecasters understand better their meteorological conditions. The observation portion shows data of wind profiler and snow monitoring for application to nowcasting. The heavy snowfall forecast supporting system was applied and tested to the heavy snowfall event on 28 February 2006. In the beginning stage, this event showed the characteristics of warm precipitation pattern in the wind and surface pressure fields. However, we expected later on the weak warm precipitation pattern because the center of low pressure passing through the Straits of Korea was becoming weak. It was appeared that Gangwon Short Range Prediction System simulated a small amount of precipitation in the Yeong-dong region and this result generally agrees with the observations.

• Atmosphere
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• v.18 no.4
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• pp.441-458
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• 2008

A TP/WVP-3000A, ground-based microwave radiometer, that was first introduced to South Korea has been operated since August 22, 2007 at the National Center for Intensive Observation of Severe Weathers (NCIO). Using the dual-band, the radiometer provides temperature and humidity soundings from the surface up to 10 km height with the high-temporal resolution of a few minutes. In this study, the performance of the radiometer on the predictability of the high impact weathers was evaluated and various practical applications were investigated. To verify the retrieved profile data from the radiometer, temperature and relative humidity soundings are compared with those from the rawinsonde launched at the NCIO and Gwangju station. The root mean squared errors for temperature and relative humidity soundings were smaller under rainy weather conditions. The correlation coefficient between PWVs (Precipitable Water Vapors) obtained from the radiometer and Global Positioning System satellite at Mokpo station is 0.92 on average. In order to investigate the structure and characteristics of precipitation, stability indexes related to rainfall such as the Convective Available Potential Energy (CAPE), K-index, and Storm RElative Helicity (SREH) were calculated using windprofiler at the NCIO from 14 to 16 September, 2007. CAPE and K-index tended to be large when the thermodynamic unstability was strong. On the other hand, SREH index was dominantly large when the dynamic unstability was strong due to the passage of the typhoon 'Nari'.

• Journal of Environmental Health Sciences
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• v.42 no.2
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• pp.92-101
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• 2016

Objectives: Outdoor workers are exposed to thermally stressful work environments. In this study, heat stress indices for harbor workers in summer were calculated to evaluate thermal comfort based on a human heat balance model. These indices are Physiological Subjective Temperature (PST), Dehydration Risk (DhR), and Overheating Risk (OhR) according to respective stage of cargo work in a harbor. In addition, we constructed a forecast system to provide heat stress information. Methods: Thermophysiological indices in this study were calculated using the MENEX model (i.e. the human heat balance model), which used as inputs the meteorological parameters, clothing insulation, and metabolic rate for each stage of cargo work in the harbor of Masan over the course of seven days, including a four-day heat wave. The forecast heat stress information constructed for Masan harbor was based on meteorological data supported by the Dong-Nae Forecast from the KMA (Korea Metrological Administration) and other input parameters. Results: According to higher metabolic rate, thermophysiological indices showed a critical level. In particular, PST was evaluated as reaching the 'Very hot' or 'Hot' level during all seven days, despite the heat occurring over only four. It is important in a regard to consider the work environment conditions (i.e. labor intensity and clothing in harbor). On a webpage, the forecast thermophysiological indices show as infographics to be easily understand. This webpage is comprised of indices for both current conditions and the forecast, with brief guidance. Conclusion: Thermophysiological indices show the risk level to health during a heat wave period. Heat stress information could help to protect the health of harbor workers. Further, this study could extend the applicability of these indices to a variety of outdoor workers in consideration of work environments.

• Atmosphere
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• v.16 no.4
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• pp.359-370
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• 2006

Terrain height variance spectra for the Korean mountain region are calculated in order to determine an adequate grid size required to resolve terrain forcing on mesoscale model simulation. One-dimensional spectral analysis is applied to specifically the central-eastern part of the Korean mountain region, where topographical-scale forcing has an important effect on mesoscale atmospheric flow. It is found that the terrain height variance spectra in this mountain region has a wavelength dependence with the power law exponents of 1.5 at the wavelength near 30 km, but this dependence is steeply changed to 2.5 at the wavelength less than 30 km. For the adequate horizontal grid size selection on mesoscale simulation two-dimensional terrain height spectral analysis is also performed. There is no directionality within 50% of spectral energy region, so one-dimensional spectral analysis can be reasonably applied to the Korea Peninsula. According to the spectral analysis of terrain height variance, the finer grid size which is higher than 6 km is required to resolve a 90% of terrain variance in this region. Numerical simulation using WRF (Weather Research and Forecasting Model) was performed to evaluate the effect of different terrain resolution in accordance with the result of spectral analysis. The simulated results were quantitatively compared to observations and there was a significant improvement in the wind prediction across the mountain region as the grid space decreased from 18 km to 2 km. The results will provide useful guidance of grid size selection on mesoscale topographical simulation over the Korean mountain region.

• Atmosphere
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• v.20 no.3
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• pp.287-306
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• 2010

An ensemble Kalman filter (EnKF) with Weather Research and Forecasting (WRF) Model is applied for Typhoon Wukong (200610) to investigate the performance of ensemble forecasts depending on experimental configurations of the EnKF. In addition, the ensemble sensitivity analysis is applied to the forecast and analysis ensembles generated in EnKF, to investigate the possibility of using the ensemble sensitivity analysis as the adaptive observation guidance. Various experimental configurations are tested by changing model error, ensemble size, assimilation time window, covariance relaxation, and covariance localization in EnKF. First of all, experiments using different physical parameterization scheme for each ensemble member show less root mean square error compared to those using single physics for all the forecast ensemble members, which implies that considering the model error is beneficial to get better forecasts. A larger number of ensembles are also beneficial than a smaller number of ensembles. For the assimilation time window, the experiment using less frequent window shows better results than that using more frequent window, which is associated with the availability of observational data in this study. Therefore, incorporating model error, larger ensemble size, and less frequent assimilation window into the EnKF is beneficial to get better prediction of Typhoon Wukong (200610). The covariance relaxation and localization are relatively less beneficial to the forecasts compared to those factors mentioned above. The ensemble sensitivity analysis shows that the sensitive regions for adaptive observations can be determined by the sensitivity of the forecast measure of interest to the initial ensembles. In addition, the sensitivities calculated by the ensemble sensitivity analysis can be explained by dynamical relationships established among wind, temperature, and pressure.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.94-102
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• 2005

This paper performs analysis the load patterns for the all the special days and studies the change of the load patterns for the last 15 years using Expert system based on the load record and the weather condition record. The Expert system is one of the four major load forecasting methods of the power system And it is used for forecasting. loads of the special days based on the Know-how of the load forecasting Experts. After the author simulates the load forecasting using hourly relative factors of the load patterns based on the past load records, there is considerable improved effect. The average errors of past 5 days load forecasting of lunar New Year's Day (year 2002 and 2003) is $3.23{[\%]}$. Using the new method the author forecast loads of the lunar new year's days (the year 2005) and it shows only $1.78{[\%]}$ error. A field manual for the load forecast can be made using proposed method. The authors expect this article could give a guidance to those who wish to be load forecast expert.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.