• 대기
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• 제29권4호
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• pp.451-461
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• 2019

Medium-range forecast is highly dependent on ensemble forecast data. However, operational weather forecasters have not enough time to digest all of detailed features revealed in ensemble forecast data. To utilize the ensemble data effectively in medium-range forecasting, representative weather patterns in East Asia in this study are defined. The k-means clustering analysis is applied for the objectivity of weather patterns. Input data used daily Mean Sea Level Pressure (MSLP) anomaly of the ECMWF ReAnalysis-Interim (ERA-Interim) during 1981~2010 (30 years) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Using the Explained Variance (EV), the optimal study area is defined by 20~60°N, 100~150°E. The number of clusters defined by Explained Cluster Variance (ECV) is thirty (k = 30). 30 representative weather patterns with their frequencies are summarized. Weather pattern #1 occurred all seasons, but it was about 56% in summer (June~September). The relatively rare occurrence of weather pattern (#30) occurred mainly in winter. Additionally, we investigate the relationship between weather patterns and extreme weather events such as heat wave, cold wave, and heavy rainfall as well as snowfall. The weather patterns associated with heavy rainfall exceeding 110 mm day^-1 were #1, #4, and #9 with days (%) of more than 10%. Heavy snowfall events exceeding 24 cm day^-1 mainly occurred in weather pattern #28 (4%) and #29 (6%). High and low temperature events (> 34℃ and < -14℃) were associated with weather pattern #1~4 (14~18%) and #28~29 (27~29%), respectively. These results suggest that the classification of various weather patterns will be used as a reference for grouping all ensemble forecast data, which will be useful for the scenario-based medium-range ensemble forecast in the future.

• 대한지리학회지
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• 제30권3호
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• pp.207-229
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• 1995

한반도는 중위도의 아시아대륙 동안에 위치하여 있고 지형적으로 복잡하므로 계절 을 통해 다양하게 탁월한 천후가 출현한다. 이러한 탁월천후의 특성은 일기출현율과 그 연 변화상에 잘 반영되고 있을 것으로 생각된다. 따라서 본 논문은 cybernetics의 일부로 있는 정보이론을 이용하여 남한의 탁월일기의 다소와 그 연변화형에 대해 일기의 평균정보량인 일기엔트로피를 구하고, 주성분분석기법을 응용하여 추출된 일기엔트로피의 연변화형과 그 공간 스케일에 따라 객관적인 작업에 의해 계량적으로 기후지역구분을 한 것이다. 주로 소 백, 태백산맥을 경계로 그 이서와 이동은 일기엔트로피의 출현월과 주성분분석 기법을 응용 하여 수량적으로 추출된 일기엔트로피의 연변화형이 각각 다르게 나타난다. 또한 이것은 해 안과 내륙에 위치하느냐에 따라서도 지역별로 그 정도차가 크다. 남한에서는 최소 일기엔트 로피의 출현월에 다라 8개형의 지역이 나타나며, 주성분분석에 의한 일기엔트로피의 연변화 형에 따라서는 16개형 지역으로 분류된다. 이를 종합하면 남한은 31개 지역으로 지역구분 된다.

• 한국환경과학회지
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• 제8권3호
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• pp.271-280
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• 1999

This study was carried out to investigate the characteristics of acidity in the precipitation and weather patterns that were influenced it at Kimhae area from March, 1992 to June, 1994. The range of pH value in the precipitation at Kimhae is 3.45 to 6.80 and the average is pH 4.62, and the major anion components associated with acidity in the precipitation are $Cl^-, SO_4^{-2}, NO_3^-$. These distributions are to be expected the influence of industrialization such as, urbanization and construction of industrial complex at Kimhae area and the long range trasporting of air pollutants from China. The weather patterns governing the acid rain at Kimhae were classified broadly into four types(Cyclone(type I-a, type I-b), Migratory Anticyclone(type II), Tropical Cyclone(type III), Siberia High(type IV) and weather pattern which had the most occurrence frequency of acid rain was type I-a and the average pH value of precipitation in this pattern was 4.45, and we are found that the source area of air mass which was accompanied with high acidic precipitation in Kimhae was the central China include with Peking through the analysis of surface weather maps, 850 hPa wind fields, and the streamline analyses.

• 대한한방내과학회지
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• 제30권1호
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• pp.200-211
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• 2009

There are many reports about correlations between meteorological elements and stroke. In Oriental medicine, it is recognized that the weather affects the human body and diseases, but there are few studies about the correlation between meteorological elements and pattern identification of stroke. 105 stroke patients classified into fire-heat pattern or dampress-phlegm pattern were registered during the study period. We took the measurement of each meteorological element (atmospheric pressure, temperature, humidity, wind speed) according to pattern identification and analyzed pattern identification into two groups according to mean of each meteorological element during the study period. Mean temperature was higher with the heat-fire pattern than with the dampness-phlegm pattern. Heat-fire pattern also had higher frequency when temperature was higher than mean temperature. There was no correlation between atmospheric pressure, relative humidity, or wind speed and pattern identification.

• The Plant Pathology Journal
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• 제21권2호
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• pp.111-118
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• 2005

Weather data for disease forecasts are usually derived from automated weather stations (AWS) that may be dispersed across a region in an irregular pattern. We have developed an alternative method to simulate local scale, high-resolution weather and plant disease in a grid pattern. The system incorporates a simplified mesoscale boundary layer model, LAWSS, for estimating local conditions such as air temperature and relative humidity. It also integrates special models for estimating of surface wetness duration and disease forecasts, such as the grapevine downy mildew forecast model, DMCast. The system can recreate weather forecasts utilizing the NCEP/NCAR reanalysis database, which contains over 57 years of archived and corrected global upper air conditions. The highest horizontal resolution of 0.150 km was achieved by running 5-step nested child grids inside coarse mother grids. Over the Finger Lakes and Chautauqua Lake regions of New York State, the system simulated three growing seasons for estimating the risk of grape downy mildew with 1 km resolution. Outputs were represented as regional maps or as site-specific graphs. The highest resolutions were achieved over North America, but the system is functional for any global location. The system is expected to be a powerful tool for site selection and reanalysis of historical plant disease epidemics.

• 한국도로학회논문집
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• 제20권2호
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집
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• pp.746-755
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• 2009

This study will analyze the data of Kyongbu & Honam Line whether how much the weather change has an effect on the change of transportation modes and check the pattern of customer's behavior and investigate the influence on the changing transportation modes. Especially, this study will confine to the rainfall and snowfall out of various weather causes, and the questionnaire investigation is used to know the sensitivity of customer's patterns selecting transportation modes in accordance with weather change, and the research result will be used to make a marketing strategy & efficient train operation.

• 전기학회논문지
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• 제65권7호
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• 한국통신학회논문지
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• 제25권6B호
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• pp.1120-1126
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• 2000

Recently, in order to analyze the time series problems that occur in the nature word, and analyzing method using a neural electric network is being studied more than a typical statistical analysis method. A neural electric network has a generalization performance that is possible to estimate and analyze about non-learning data through the learning of a population. In this paper, after collecting weather datum that was collected from 1987 to 1996 and learning a population established, it suggests the weather forecasting system for an estimation and analysis the future weather. The suggested weather forecasting system uses 28*30*1 neural network structure, raises the total learning numbers and accuracy letting the selecting concentration learning about the pattern, that is not collected, using the descending epsilon learning method. Also, the weather forecasting system, that is suggested through a comparative experiment of the typical time series analysis method shows more superior than the existing statistical analysis method in the part of future estimation capacity.

• 한국환경과학회지
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• 제18권4호
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• pp.369-374
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• 2009

To examine the trend on the flowering time in some weather flora including Prunus serrulata var. spontanea, Cosmos bipinnatus, and Robinia pseudo-acacia in Busan, the changes in time series and rate of flowering time of plants were analyzed using the method of time series analysis. According to the correlation between the flowering time and the temperature, changing pattern of flowering time was very similar to the pattern of the temperature, and change rate was gradually risen up as time goes on. Especially, the change rate of flowering time in C. bipinnatus was 0.487 day/year and showed the highest value. In flowering date in 2007, the difference was one day between measurement value and prediction value in C. bipinnatus and R. pseudo-acacia, whereas the difference was 8 days in P. mume showing great difference compared to other plants. Flowering time was highly related with temperature of February and March in the weather flora except for P. mume, R. pseudo-acacia and C. bipinnatus. In most plants, flowering time was highly related with a daily average temperature. However, the correlation between flowering time and a daily minimum temperature was the highest in Rhododendron mucronulatum and P. persica, otherwise the correlation between flowering time and a daily maximum temperature was the highest in Pyrus sp.