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Biases in estimating long-term recurrence intervals of extreme events due to regionalised sampling
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  • Moutassem El Rafei,
  • Steven Sherwood,
  • Jason Peter Evans,
  • Andrew J. Dowdy,
  • Fei Ji
Moutassem El Rafei
Climate Change Research Centre

Corresponding Author:m.el_rafei@unsw.edu.au

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Steven Sherwood
University of New South Wales
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Jason Peter Evans
University of New South Wales
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Andrew J. Dowdy
Bureau of Meteorology (Australia)
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Fei Ji
NSW Department of Planning and Environment
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Preparing for environmental risks requires estimating the frequencies of extreme events, often from data records that are too short to confirm them directly. This requires fitting a statistical distribution to the data. To improve precision, investigators often pool data from neighboring sites into single samples, referred to as “superstations,” before fitting. We demonstrate that this technique can introduce unexpected biases in typical situations, using wind and rainfall extremes as case studies. When the combined locations have even small differences in the underlying statistics, the regionalization approach gives a fit that may tend toward the highest levels suggested by any of the individual sites. This bias may be large or small compared to the sampling error, for realistic record lengths, depending on the distribution of the quantity analysed. The results of this analysis indicate that previous analyses could potentially have overestimated the likelihood of extreme events arising from natural weather variability.
09 Mar 2023Submitted to ESS Open Archive
09 Mar 2023Published in ESS Open Archive