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http://dx.doi.org/10.14191/Atmos.2019.29.4.451

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

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)
Publication Information
Atmosphere / v.29, no.4, 2019 , pp. 451-461 More about this Journal
Abstract
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.
Keywords
Weather pattern; East Asia region; k-means clustering analysis; medium-range forecast; extreme weather;
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1 Goodess, C. M., and P. D. Jones, 2002: Links between circulation and changes in the characteristics of Iberian rainfall. Int. J. Climatol., 22, 1593-1615, doi:10.1002/joc.810.   DOI
2 Hisashi, K., N. Yoshinori, and T. Seiji, 2013: Objective classification of the sea level pressure distribution pattern in East Asia: Analysis of the cold half of the year. Geogr. Rev. Japan Ser. A, 86, 95-114, doi:10.4157/grj.86.95 (in Japanese with English abstract).   DOI
3 Hoffmann, P., and K. H. Schlunzen, 2013: Weather pattern classification to represent the urban heat island in present and future climate. J. Appl. Meteor. Climatol., 52, 2699-2714, doi: 10.1175/jamc-d-12-065.1.   DOI
4 Hoinka, K. P., C. Schwierz, and O. Martius, 2006: Synoptic-scale weather patterns during Alpine heavy rain events. Q. J. R. Meteorol. Soc., 132, 2853-2860, doi:10.1256/qj.05.239.   DOI
5 Hsu, C.-H., and F.-Y. Cheng, 2016: Classification of weather patterns to study the influence of meteorological characteristics on PM2.5 concentrations in Yunlin County, Taiwan. Atmos. Environ., 144, 397-408, doi:10.1016/j.atmosenv.2016.09.001.   DOI
6 Huth, R., C. Beck, A. Philipp, M. Demuzere, Z. Ustrnul, M. Cahynová, J. Kysely, and O. E. Tveito, 2008: Classifications of atmospheric circulation patterns: recent advances and applications. Ann. N. Y. Acad. Sci., 1146, 105-152, doi:10.1196/annals.1446.019.   DOI
7 Kohonen, T., 1982: Self-organized formation of topologically correct feature maps. Biol. Cybern., 43, 59-69, doi:10.1007/bf00337288.   DOI
8 Kysely, J., 2007: Implications of enhanced persistence of atmospheric circulation for the occurrence and severity of temperature extremes. Int. J. Climatol., 27, 689-695, doi:10.1002/joc.1478.   DOI
9 Lee, D.-K., and J.-G. Park, 1999: Regionalization of summer rainfall in South Korea using cluster analysis, Asia-Pac. J. Atmos. Sci., 35, 511-518 (in Korean with English abstract).
10 Lim, W.-I., and K.-H. Seo, 2018: Investigation on characteristics of summertime extreme temperature events occurred in South Korea using self-organizing map. Atmosphere, 28, 305-315, doi:10.14191/Atmos.2018.28.3.305 (in Korean with English abstract).   DOI
11 MacQueen, J., 1967: Some methods for classification and analysis of multivariate observations. Proc. fifth Berkeley Symp. on Math. Statist. and Prob., 1, 281-297.
12 Mukougawa, H., and M. Mabuchi, 2012: Characteristics of atmospheric circulation related to wintertime temperature variation over the Far-East. Annuals of Disas. Prev. Res. Inst., 55, 247-253 (in Japanese with English abstract).
13 Neal, R., D. Fereday, R. Crocker, and R. E. Comer, 2016: A flexible approach to defining weather patterns and their application in weather forecasting over Europe. Meteor. Appl., 23, 389-400, doi:10.1002/met.1563.   DOI
14 Neal, R., R. Dankers, A. Saulter, A. Lane, J. Millard, G. Robbins, and D. Price, 2018: Use of probabilistic medium- to long-range weather-pattern forecasts for identifying periods with an increased likelihood of coastal flooding around the UK. Meteorol. Appl., 25, 534-547, doi:10.1002/met.1719.   DOI
15 Nguyen-Le, D., T. J. Yamada, and D. Tran-Anh, 2017: Classification and forecast of heavy rainfall in northern Kyushu during Baiu season using weather pattern recognition. Atmos. Sci. Lett., 18, 324-329, doi:10.1002/asl.759.   DOI
16 Ohba, M., S. Kadokura, Y. Yoshida, D. Nohara, and Y. Toyoda, 2015: Anomalous weather patterns in relation to heavy precipitation events in Japan during the Baiu season. J. Hydrometeor., 16, 688-701, doi:10.1175/jhm-d-14-0124.1.   DOI
17 Robbins, J., R. Dankers, C. Dashwood, K. Lee, R. Neal, and H. Reeves, 2018: Early warning of landslides in Scotland using probabilistic weather pattern forecasts. Proc. 20th EGU General Assembly 2018, Vien, Austria, EGU2018, 7496.
18 Park, J.-G., and D.-K. Lee, 1998: Cluster analysis and development mechanism of explosive cyclones in East Asia. Asia-Pac. J. Atmos. Sci., 34, 523-547 (in Korean with English abstract).
19 Philipp, A., P. M. Della-Marta, J. Jacobeit, D. R. Fereday, P. D. Jones, A. Moberg, and H. Wanner, 2007: Longterm variability of daily north Atlantic-European pressure patterns since 1850 classified by simulated annealing clustering. J. Climate, 20, 4065-4095, doi: 10.1175/jcli4175.1.   DOI
20 Richardson, D., H. J. Fowler, C. G. Kilsby, and R. Neal, 2018: A new precipitation and drought climatology based on weather patterns. Int. J. Climatol., 38, 630-648, doi:10.1002/joc.5199.   DOI
21 Steele, E., R. Neal, R. Dankers, N. Fournier, K. Mylne, P. Newell, A. Saulter, A. Skea, and J. Upton, 2018: Using weather pattern typology to identify calm weather windows for local marine operations. Conf. Offshore Tech., OTC-28784-MS, Houston, Texas, USA, doi:10.4043/28784-MS
22 Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553-597, doi:10.1002/qj.828.   DOI
23 Yoon, D., D.-H. Cha, G. Lee, C. Park, M.-I. Lee, and K.-H. Min, 2018: Impacts of synoptic and local factors on heat wave events over southeastern region of Korea in 2015. J. Geophys. Res.: Atmos., 123, 12081-12096, doi:10.1029/2018JD029247.   DOI
24 Vicente-Serrano, S. M., and J. I. Lopez-Moreno, 2006: The influence of atmospheric circulation at different spatial scales on winter drought variability through a semi-arid climatic gradient in Northeast Spain. Int. J. Climatol., 26, 1427-1453, doi:10.1002/joc.1387.   DOI
25 Zhang, Y., A. Ding, H. Mao, W. Nie, D. Zhou, L. Liu, X. Huang, and C. Fu, 2016: Impact of synoptic weather patterns and inter-decadal climate variability on air quality in the North China Plain during 1980-2013. Atmos. Environ., 124, 119-128, doi:10.1016/j.atmosenv.2015.05.063.   DOI
26 Beck, C., and A. Philipp, 2010: Evaluation and comparison of circulation type classifications for the European domain. Phys. Chem. Earth, 35, 374-387, doi:10.1016/j.pce.2010.01.001.   DOI
27 Beck, C., A. Philipp, and F. Streicher, 2016: The effect of domain size on the relationship between circulation type classifications and surface climate. Int. J. Climatol., 36, 2692-2709, doi:10.1002/joc.3688.   DOI
28 Casola, J. H., and J. M. Wallace, 2007: Identifying weather regimes in the wintertime 500-hPa geopotential height field for the Pacific-North American sector using a limited-contour clustering technique. J. Appl. Meteor. Climatol., 46, 1619-1630, doi:10.1175/jam2564.1.   DOI
29 Choi, Y.-J., Y.-R. Jung, H.-J. Kim, K.-R, Kim, S.-Y. Lee, and H.-R. Lee, 2009: A study on regionalization by K-means method and time series pattern of precipitation areas in South Korea. J. Korean Data Anal. Soc., 11, 2761-2772 (in Korean with English abstract).
30 Esteban, P., P. D. Jones, J. Martín-Vide, and M. Mases, 2005: Atmospheric circulation patterns related to heavy snowfall days in Andorra, Pyrenees. Int. J. Climatol., 25, 319-329, doi:10.1002/joc.1103.   DOI
31 Everitt, B., 1993: Cluster analysis. 3rd edit., Halsted Press, 170 pp.
32 Fereday, D. R., J. R. Knight, A. A. Scaife, C. K. Folland, and A. Philipp, 2008: Cluster analysis of north Atlantic-European circulation types and links with tropical Pacific sea surface temperatures. J. Climate, 21, 3687-3703, doi: 10.1175/2007jcli1875.1.   DOI
33 Fleig, A. K., L. M. Tallaksen, H. Hisdal, K. Stahl, and D. M. Hannah, 2010: Inter-comparison of weather and circulation type classifications for hydrological drought development. Phys. Chem. Earth, 35, 507-515, doi:10.1016/j.pce.2009.11.005.   DOI