Articles | Volume 33, issue 2
https://doi.org/10.5194/npg-33-233-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-33-233-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 May 2026
Research article |
| 28 May 2026
| 28 May 2026
Boosting ensembles for statistics of tails at conditionally optimal advance split times
Justin Finkel
CORRESPONDING AUTHOR
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States
Current affiliation: Department of Geophysical Sciences and the Data Science Institute, University of Chicago, 5801 S. Ellis Ave, Chicago, IL 60637, United States
Paul A. O'Gorman
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States
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Short summary
Estimating small probabilities of high-impact extreme weather events is a persistent computational challenge, motivating techniques such as
rare event samplingand
ensemble boosting: lightly perturbing simulated moderate events into more extreme ones. We formulate a new, flexible sampling strategy and characterizes a critical parameter – the
advance split time, dictating when to perturb – in a simple atmospheric turbulence model, with generalizable entropy-based criteria.
Estimating small probabilities of high-impact extreme weather events is a persistent...
