Hsu et al. (2026) Sub‐Seasonal Forecasting of Precipitation and Soil Moisture in CFSv2 Near Gulf Coast Tropical Cyclone Landfall Locations

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2026
• Date: 2026-10-14
• Authors: Wei‐Ching Hsu, Gabriel Joseph Kooperman, J. Marshall Shepherd, Kevin A. Reed
• DOI: 10.1029/2026jd046700

Research Groups

Not explicitly stated in the abstract.

Short Summary

This study assesses the Climate Forecast System Version 2's (CFSv2) ability to represent near-surface soil moisture and associated precipitation at various lead times over the U.S. Gulf coast, revealing decreasing predictability with lead time and a failure to capture intense precipitation events, which limits its subseasonal-to-seasonal forecast utility for tropical cyclone impacts beyond a week.

Objective

• To investigate the ability of the 9-month Climate Forecast System Version 2 (CFSv2) operational forecast to represent near-surface soil moisture at forecast lead times from hours to weeks over the Gulf coast tropical cyclone landfalling region.

Study Configuration

• Spatial Scale: U.S. Gulf coast tropical cyclone landfalling region.
• Temporal Scale: Forecast lead times from hours to weeks; evaluation of interannual variability and a 7-year mean climatology; forecast horizon of 9 months.

Methodology and Data

• Models used: Climate Forecast System Version 2 (CFSv2) operational forecast.
• Data sources: Forecasted soil moisture and precipitation from CFSv2. Observational or reanalysis data used for comparison/validation are not explicitly stated in the abstract.

Main Results

• The predictability of near-surface soil moisture and associated precipitation decreases with increasing lead times, evident across short timescales (weekly or shorter events), interannual variability, and a 7-year mean climatology.
• Spatial biases in the forecasts increase with lead time, particularly around one to two weeks, leading to a damping of spatial variability.
• Even at shorter lead times (hours to days), the forecasted precipitation fails to capture more intense events, contributing to biases in soil moisture across all lead times.
• Consequently, CFSv2 is unlikely to accurately capture the influence of antecedent soil moisture variability on subsequent events (e.g., tropical cyclone evolution after landfall) for lead times exceeding one week.

Contributions

• Provides a comprehensive assessment of the relationship between the predictability of near-surface soil moisture and related precipitation with varying lead times using the CFSv2 operational forecast.
• Highlights specific limitations of CFSv2, particularly its inability to capture intense precipitation events and the increasing spatial biases with lead time, which have significant implications for subseasonal-to-seasonal predictability of tropical cyclone impacts related to antecedent soil moisture.

Funding

Not explicitly stated in the abstract.

Citation

@article{Hsu2026SubSeasonal,
  author = {Hsu, Wei‐Ching and Kooperman, Gabriel Joseph and Shepherd, J. Marshall and Reed, Kevin A.},
  title = {Sub‐Seasonal Forecasting of Precipitation and Soil Moisture in CFSv2 Near Gulf Coast Tropical Cyclone Landfall Locations},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2026},
  doi = {10.1029/2026jd046700},
  url = {https://doi.org/10.1029/2026jd046700}
}