Articles | Volume 7, issue 1
https://doi.org/10.5194/ascmo-7-35-2021
https://doi.org/10.5194/ascmo-7-35-2021
21 Apr 2021
 | 21 Apr 2021

A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns

Vitaly Kholodovsky and Xin-Zhong Liang

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Cited articles

AghaKouchak, A., Nasrollahi, N., Li, J., Imam, B., and Sorooshian, S.: Geometrical Characterization of Precipitation Patterns, J. Hydrometeor., 12, 274–285, https://doi.org/10.1175/2010JHM1298.1, 2010. a
Ahijevych, D., Gilleland, E., Brown, B. G., and Ebert, E. E.: Application of Spatial Verification Methods to Idealized and NWP-Gridded Precipitation Forecasts, Weather Forecast., 24, 1485–1497, https://doi.org/10.1175/2009WAF2222298.1, 2009. a
Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A. M. G., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Rupa Kumar, K., Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D. B., Burn, J., Aguilar, E., Brunet, M., Taylor, M., New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. L.: Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res., 111, D05109, https://doi.org/10.1029/2005JD006290, 2006. a
Bader, B., Yan, J., and Zhang, X.: Automated selection of r for the r largest order statistics approach with adjustment for sequential testing, Stat. Comput., 27, 1435–1451, https://doi.org/10.1007/s11222-016-9697-3, 2017. a
Balkema, A. A. and de Haan, L.: Residual Life Time at Great Age, Ann. Probab., 2, 792–804, https://doi.org/10.1214/aop/1176996548, 1974. a
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Short summary
Consistent definition and verification of extreme events are still lacking. We propose a new generalized spatio-temporal threshold clustering method to identify extreme event episodes. We observe changes in the distribution of extreme precipitation frequency from large-scale well-connected spatial patterns to smaller-scale, more isolated rainfall clusters, possibly leading to more localized droughts and heat waves.