On Using Medium-Range Ensemble Forecasts for Storm Transposition of Synoptic-Scale Systems in Probable Maximum Precipitation Estimation
Mathieu Mure-Ravaud

TL;DR
This paper introduces an innovative storm transposition method using ensemble forecasts to improve probable maximum precipitation estimates, addressing limitations of traditional approaches.
Contribution
The study applies the internal variability leveraging (IVL) approach with medium-range ensemble forecasts to enhance storm transposition accuracy for PMP estimation.
Findings
Transposed storm produced 24-h precipitation of 119 mm for Willamette River basin.
Transposed storm produced 24-h precipitation of 98 mm for Nass River basin.
Ensemble-based approach captures storm variability and improves PMP estimation.
Abstract
Most methods for estimating probable maximum precipitation (PMP) -- the greatest depth of precipitation that is physically possible over a given area and duration -- rely on storm transposition (ST), the process of transporting a storm, either historically observed or simulated, from its original location to a target basin. Existing ST approaches, whether classical or physically based, involve assumptions and manipulations that can introduce inconsistencies, leaving the physical validity of the transposed storm uncertain. In this study, the internal variability leveraging (IVL) approach is used to transpose an atmospheric river cluster that affected the U.S. West Coast during 20-29 October 2021. Steering the storm toward the target basin and determining its transposition region are achieved by considering an ensemble of plausible storm evolutions and trajectories obtained from archived…
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