Mure-Ravaud et al. (2026) Physically-based transposition of a mesoscale convective system for estimating probable maximum precipitation

Identification

• Journal: Climate Dynamics
• Year: 2026
• Date: 2026-02-16
• Authors: Mathieu Mure-Ravaud, M. Levent Kavvas
• DOI: 10.1007/s00382-025-07912-4

Research Groups

• Hydrologic Research Laboratory, Department of Civil and Environmental Engineering, University of California, Davis, USA

Short Summary

This paper introduces a novel physically-based storm transposition approach, leveraging numerical weather prediction model internal variability, to objectively define transposition regions and reduce boundary condition shifts for probable maximum precipitation estimation, applied to a mesoscale convective system over the Raccoon River Watershed in Iowa.

Objective

• To introduce and apply a new physically-based storm transposition (ST) approach, called "internal variability leveraging" (IVL), for probable maximum precipitation (PMP) estimation, aiming to provide a physically consistent basis for defining the ST region and reduce the magnitude of initial and boundary condition (IBC) shifts required to steer a storm over a target area.

Study Configuration

• Spatial Scale:
  • Target basin: Raccoon River Watershed in Iowa, approximately 9,388 square kilometers.
  • Model domains: Outer domain with 15-kilometer resolution, inner domain with 3-kilometer resolution.
  • Reanalysis data: 0.25° x 0.25° (approximately 31 kilometers) horizontal resolution.
  • Observational data: 4-kilometer horizontal resolution.
• Temporal Scale:
  • Case study event: Mesoscale convective system (MCS) on July 27–28, 2011.
  • Simulation period: Hourly outputs from 27 July 2011 21:00 UTC to 28 July 2011 12:00 UTC.
  • Simulation start times: 27 different start times, initially spaced by 6-hour increments, with subsequent refinements of ±1, ±2, and ±3 hours.
  • Accumulation windows: 1 hour, 3 hours, 6 hours, and 12 hours.
  • Reanalysis data: Hourly temporal resolution.

Methodology and Data

• Models used: Weather Research and Forecasting (WRF) model, version 4.6.1, with one-way nesting.
• Data sources:
  • Initial and Boundary Conditions (IBCs): Fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5).
  • Observational data for validation: Stage IV multi-sensor precipitation analyses.

Main Results

• The proposed "internal variability leveraging" (IVL) method successfully generated an ensemble of 135 MCS realizations, providing a physically consistent storm transposition region and identifying realizations that impacted the target Raccoon River Watershed, which was originally located approximately 4° west of the historical storm.
• IVL significantly reduced the magnitude of initial and boundary condition (IBC) shifts needed to steer the storm, addressing a limitation of previous IBC shifting (IBCS) methods.
• Through temporal refinement and subsequent IBCS, the 6-hour basin-average precipitation depth (BA PD) over the Raccoon River Watershed was maximized from an initial 48.6 millimeters (from an IVL realization) to 74.4 millimeters.
• The mechanism for the maximized rainfall accumulation over the target basin was the MCS tracking southeastward and sweeping rainfall along the watershed’s main axis, rather than the quasi-stationary behavior observed in the original MCS.
• The study demonstrates that the WRF model, using the same atmospheric "ingredients," can create a new storm whose characteristics and dynamical behavior are adapted to the target location.

Contributions

• Introduces a novel physically-based storm transposition (ST) approach, "internal variability leveraging" (IVL), which objectively defines the ST region by exploiting the intrinsic spread in numerical weather prediction model outcomes.
• Provides a physically consistent and objective alternative to traditional, subjective meteorological judgment for defining transposition regions in probable maximum precipitation (PMP) estimation.
• Significantly reduces the magnitude of initial and boundary condition (IBC) shifts required for storm steering, thereby limiting the impact of such shifts on the physical consistency of simulated fields.
• Proposes a three-stage methodology (IVL, followed by simulation start date/outer domain refinement, then IBCS) for PMP estimation, offering a structured approach to enhance basin-average precipitation depth.
• Highlights that the maximized storm's behavior can differ from the original storm, adapting its trajectory and rainfall distribution to the target basin.

Funding

Not explicitly stated in the provided text.

Citation

@article{MureRavaud2026Physicallybased,
  author = {Mure-Ravaud, Mathieu and Kavvas, M. Levent},
  title = {Physically-based transposition of a mesoscale convective system for estimating probable maximum precipitation},
  journal = {Climate Dynamics},
  year = {2026},
  doi = {10.1007/s00382-025-07912-4},
  url = {https://doi.org/10.1007/s00382-025-07912-4}
}