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 (Observation and Forecast Research Division, National Institute of Meteorological Sciences) ;
  • Lee, Hyeon-Cheol (Observation and Forecast Research Division, National Institute of Meteorological Sciences) ;
  • Lim, Byunghwan (Observation and Forecast Research Division, National Institute of Meteorological Sciences) ;
  • Kim, Seung-Bum (Observation and Forecast Research Division, National Institute of Meteorological Sciences)
  • 조영준 (기상청 국립기상과학원 관측예보연구과) ;
  • 이현철 (기상청 국립기상과학원 관측예보연구과) ;
  • 임병환 (기상청 국립기상과학원 관측예보연구과) ;
  • 김승범 (기상청 국립기상과학원 관측예보연구과)
  • Received : 2019.07.02
  • Accepted : 2019.09.26
  • Published : 2019.11.30


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.


Grant : 위험기상에 대한 분석.예보의 융합기술 고도화

Supported by : 국립기상과학원


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