• Title, Summary, Keyword: Weather pattern

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Classification of Weather Patterns in the East Asia Region using the K-means Clustering Analysis (K-평균 군집분석을 이용한 동아시아 지역 날씨유형 분류)

  • Cho, Young-Jun;Lee, Hyeon-Cheol;Lim, Byunghwan;Kim, Seung-Bum
    • Atmosphere
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    • v.29 no.4
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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.

The annual variation pattern and regional division of weather eatropy in South Korea (남한의 일기엔트로피의 연변화유형과 지역구분)

  • ;Park, Hyun-Wook
    • Journal of the Korean Geographical Society
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    • v.30 no.3
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    • pp.207-229
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    • 1995
  • The characteristics of weather and climate in South Korea has great influences on the annual variation pattern and the appearance of the prevailing weather. The purpose of this paper is to induce the quantity of the weather entropy and annual variation pattern using the information theory and the principal component analysis. And author tried to classify the region according to the variation of its space scale, The raw materials used for this study are the daily cloudiness and precipitation during the years 1990-1994 at 69 stations in South Korea. It is divided into four classes of fine, clear, cloudy and rainy. The rcsults of this study can be summarized as follows: 1. Thc characteristics of annual variation pattern of weather entropy can be chiefly divided into five categories and the accumulated contributory rate of these is 73.1%. 2. Annual variation pattern of the first principal component reaches smaller in May, April and September than national average, and becomes greater when the winter comes. This weather entropy's quantity(Rs1) is positive in most area to the western sife of Soback Mountains and negative in most seaside area to the eastern side of Soback Mountains. 3. The characteristics of annual variation pattern of the second principal component shows that the entropy is more smaller in summer than national average and the rest of seasons shows larger, especially in January, May and September. This weather entropy's quantity(Rs2) is positive in most Honam Inland area to the western side of Soback Mountains and negative in most Youngnam Inland area to the eastern side of Soback Mountains. 4. Eight type regions (S1-S11) are classified based on the occurrences of minimum weather entropy in South Korea, and annual variation pattern of weather entropy by principal component analysis may be classified into sixteen type regions (Rs1-Rs9). Putting these things together, South Korea can be classifieed into thirty one type regions (Rs1S7-Rs9S10).

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Weather Patterns and Acid Rain at Kimhae Area (김해지역의 산성강우와 기압유형)

  • 박종길;황용식
    • Journal of Environmental Science International
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    • v.8 no.3
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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.

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The Study of Correlation between Pattern Identification of Stroke Patients and Meteorological Elements (중풍 환자 변증과 기후 요소와의 상관성에 관한 연구)

  • Ma, Mi-Jin;Han, Chang-Ho
    • The Journal of Internal Korean Medicine
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    • v.30 no.1
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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.

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Simulation of Grape Downy Mildew Development Across Geographic Areas Based on Mesoscale Weather Data Using Supercomputer

  • Kim, Kyu-Rang;Seem, Robert C.;Park, Eun-Woo;Zack, John W.;Magarey, Roger D.
    • The Plant Pathology Journal
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    • v.21 no.2
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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.

24-Hour Load Forecasting For Anomalous Weather Days Using Hourly Temperature (시간별 기온을 이용한 예외 기상일의 24시간 평일 전력수요패턴 예측)

  • Kang, Dong-Ho;Park, Jeong-Do;Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.7
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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.

The analysis of customers patterns selecting transportation modes in accordance with weather change (기상요인 변화에 따른 고객의 교통수단 이용행태 분석)

  • Park, Eun-Kyung
    • Proceedings of the KSR Conference
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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.

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Implementation of Efficient Weather Forecasting Model Using the Selecting Concentration Learning of Neural Network (신경망의 선별학습 집중화를 이용한 효율적 온도변화예측모델 구현)

  • 이기준;강경아;정채영
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.25 no.6B
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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.

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Changes of Flowering Time in the Weather Flora in Susan Using the Time Series Analysis (시계열 분석을 이용한 부산지역 계절식물의 개화시기 변화)

  • Choi, Chul-Mann;Moon, Sung-Gi
    • Journal of Environmental Science International
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    • v.18 no.4
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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.

A Study on the Impact of Weather on Sales and Optimal Budget Allocation of Weather Marketing (날씨가 기업 매출에 미치는 영향과 날씨 마케팅 예산의 최적 할당에 관한 연구)

  • Chu, Kyounghee;Kim, Soyeon;Choi, Changhui
    • Journal of the Korean Operations Research and Management Science Society
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    • v.38 no.1
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    • pp.153-181
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    • 2013
  • Weather is an influential factor to sales of companies. There have been growing attempts with which companies apply weather to developing their strategic marketing plans. By executing weather marketing activities, companies minimize risks (or negative impacts) of weather to their business and increase sales revenues. In spite of managerial importance of weather management, there are scarce empirical studies that comprehensively investigate its impact and present an efficient method that optimally allocates marketing budget. Our research was conducted in two parts. In the first part, we investigated influences of weather on sales based on real-world daily sales data. We specifically focused on the contextual factors that were less focused in the weather related research. In the second part, we propose an optimization model that can be utilized to efficiently allocate weather marketing budget across various regions (or branches) and show how it can be applied to real industry cases. The results of our study are as follow. Study 1 investigated the impact of weather on sales using store sales data of a family restaurant company and an outdoor fashion company. Results represented that the impacts of weather are context-dependent. The impact of weather on store sales varies across their regional and location characteristics when it rains. Based on the results derived from Study 1, Study 2 proposes a method on how optimally companies allocate their weather marketing budgets across each region.