Battula et al. (2025) Characteristics and Predictability of Extreme Precipitation Related to Atmospheric Rivers, Mesoscale Convective Systems, and Tropical Cyclones in the U.S. Southeast

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Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2025
• Date: 2025-07-30
• Authors: Suma Bhanu Battula, Jason M. Cordeira, F. Martin Ralph
• DOI: 10.1029/2024jd042471

Research Groups

Not specified in the provided text.

Short Summary

This study evaluates how different synoptic patterns and storm types (atmospheric rivers, mesoscale convective systems, and tropical cyclones) influence the predictability of extreme quantitative precipitation forecasts (QPF) in the Southeastern United States. The findings indicate that events associated with atmospheric rivers (ARs) exhibit higher QPF skill than those driven by isolated mesoscale convective systems (MCSs).

Objective

• To investigate the relationship between synoptic patterns, storm types, and the predictability (QPF skill) of extreme precipitation events (EPEs) in the Southeastern United States.

Study Configuration

• Spatial Scale: Southeastern United States (SEUS).
• Temporal Scale: 2001 to 2019.

Methodology and Data

• Models used: GEFS (Global Ensemble Forecast System) reforecast dataset.
• Data sources: GEFS reforecasts; metrics including Integrated Vapor Transport (IVT), Convective Available Potential Energy (CAPE), and integrated water vapor.

Main Results

• Identified six distinct synoptic patterns associated with EPEs, distributed across cool (3 patterns), warm (2 patterns), and transition (1 pattern) seasons.
• Coincident ARs and MCSs were found in approximately 35% of cool season, 24% of transition season, and 29% of warm season EPEs.
• Cool season patterns, characterized by high IVT and high frequency of ARs, demonstrate higher QPF skill.
• Warm season patterns, characterized by high CAPE and integrated water vapor, exhibit lower QPF skill across multiple lead times.
• Predictability is significantly higher for patterns involving ARs or coincident ARs and MCSs than for those involving isolated MCSs.

Contributions

• Clarifies the drivers of forecast skill variability in the SEUS by linking predictability to specific storm types and synoptic configurations.
• Quantifies the occurrence of coincident ARs and MCSs across different seasons, providing a basis for improving extreme precipitation forecasting in the region.

Funding

Not specified in the provided text.

Citation

@article{Battula2025Characteristics,
  author = {Battula, Suma Bhanu and Cordeira, Jason M. and Ralph, F. Martin},
  title = {Characteristics and Predictability of Extreme Precipitation Related to Atmospheric Rivers, Mesoscale Convective Systems, and Tropical Cyclones in the U.S. Southeast},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2025},
  doi = {10.1029/2024jd042471},
  url = {https://doi.org/10.1029/2024jd042471}
}