Hsu et al. (2025) Predictability of Compound Impacts From Hurricane Helene and Predecessor Rain Event in CFSv2 Operational Forecasts

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Identification

• Journal: Geophysical Research Letters
• Year: 2025
• Date: 2025-12-23
• Authors: Wei‐Ching Hsu, Gabriel J. Kooperman, Kevin A. Reed, J. Marshall Shepherd
• DOI: 10.1029/2025gl119139

Research Groups

NOAA (National Oceanic and Atmospheric Administration)

Short Summary

This study investigated the predictability of precipitation and soil moisture conditions associated with Hurricane Helene and a predecessor rain event (PRE) in the Southeastern United States using NOAA's Coupled Forecast System model (CFSv2). It found that predictability for these events significantly drops around 4- to 5-day lead times due to biases in the timing and location of the systems and underestimated PRE precipitation.

Objective

• To examine the predictability of precipitation and soil moisture conditions associated with Hurricane Helene and a predecessor rain event (PRE) in NOAA's operational Coupled Forecast System model (CFSv2) as a function of forecast lead time.

Study Configuration

• Spatial Scale: Southeastern United States
• Temporal Scale: Late September 2024 (event period); forecast lead times ranging from 3 to 6 days.

Methodology and Data

• Models used: NOAA's operational Coupled Forecast System model (CFSv2)
• Data sources: Model output from CFSv2

Main Results

• Predictability of precipitation and soil moisture associated with Hurricane Helene and the PRE drops significantly around 4- to 5-day forecast lead times.
• This drop in predictability is linked to biases in the timing and geographical location of both Hurricane Helene and the PRE within the model forecasts.
• The model also underestimated the precipitation associated with the predecessor rain event (PRE).

Contributions

• Provides a case study analysis of the predictability of a compound extreme weather event (hurricane and predecessor rain event) in an operational forecast model.
• Quantifies the specific lead time at which predictability for such events degrades and identifies the contributing model biases (timing, location, precipitation underestimation).

Funding

Not specified in the provided text.

Citation

@article{Hsu2025Predictability,
  author = {Hsu, Wei‐Ching and Kooperman, Gabriel J. and Reed, Kevin A. and Shepherd, J. Marshall},
  title = {Predictability of Compound Impacts From Hurricane Helene and Predecessor Rain Event in CFSv2 Operational Forecasts},
  journal = {Geophysical Research Letters},
  year = {2025},
  doi = {10.1029/2025gl119139},
  url = {https://doi.org/10.1029/2025gl119139}
}