Classification of Weather Patterns in the East Asia Region using the K-means Clustering Analysis

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 similar to 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 similar to 60 degrees N, 100 similar to 150 degrees 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 similar to 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 degrees C and < -14 degrees C) were associated with weather pattern #1 similar to 4 (14 similar to 18%) and #2829 (27 similar to 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.